Mobility_B11_Report (2) by Team Mobility

Comment [CF1]: I pasted in my version of an Intro secrion, my edits to my part of the background and methodology, and the abstract

Traversing the Labyrinth: A Comprehensive Analysis of Pedestrian Traffic in Venice An Interactive Qualifying Project report submitted to the faculty of WORCESTER POLYTECHNIC INSTITUTE in partial fulfillment of the requirements for the Degree of Bachelor of Science.

Submitted on December 15, 2011 by: Chelsea Fogarty Geordie Folinas Steven Greco Cassandra Stacy

Project Advisors: Professor Fabio Carrera, Ph.D. Professor Frederick Bianchi, D.A.

Project Information: ve11-mobi@wpi.edu https://sites.google.com/site/ve11mobi

Sponsors: Venice Project Center Venice Department of Mobility

In Collaboration With: Santa Fe Complex Redfish Group

Abstract The 2011 Mobility Interactive Qualifying Project focused on pedestrian movement in the San Marco district of Venice, Italy. Congestion often occurs in the cityâ&#x20AC;&#x2122;s streets, and has a negative effect on quality of life. To better understand this problem, the team quantified, analyzed, and publicized pedestrian traffic at 7 bridges, 4 traghetto stops, and 3 Actv stops within the study area. Data was also compiled from census tracts, the Actv, and other sources. Utilizing the collected data, a computer model of the dynamic movement of pedestrians was created by the teamâ&#x20AC;&#x2122;s collaborators. This computer model aptly modeled traffic within the San Marco district and can be used as a proof of concept for a city-wide model that would be able to forecast instances of high pedestrian volume. Forecasting pedestrian congestion would allow preventative measures to be employed and improve traffic flow throughout the city.

Acknowledgements

Authorship This Interactive Qualifying Project was completed with contributions from each team member.

Table of Contents Abstract ............................................................................................................................................................... 1 Acknowledgements............................................................................................................................................ 2 Authorship .......................................................................................................................................................... 3 Table of Contents .............................................................................................................................................. 4 List of Figures..................................................................................................................................................... 7 List of Tables ...................................................................................................................................................... 8 Executive Summary ........................................................................................................................................... 9 Pedestrian Traffic Studies ...........................................................................................................................10 Autonomous Agent Computer Model .....................................................................................................11 Conclusions ..................................................................................................................................................11 Introduction ......................................................................................................................................................12 Background .................................................................................................. Error! Bookmark not defined. 2.1 The Architectural Framework of Venice ...................................... Error! Bookmark not defined. 2.1.1 Origins of the City .................................................................... Error! Bookmark not defined. 2.1.2 Design of the City ..................................................................... Error! Bookmark not defined. 2.1.3 The Canals.................................................................................. Error! Bookmark not defined. 2.1.4 The Streets ................................................................................. Error! Bookmark not defined. 2.2 Mobility in Venice .................................................................................................................................27 2.2.1 Watercraft in Venice ......................................................................................................................28 2.2.2 Water-Based Public Transportation ............................................................................................28 2.2.3 Pedestrian Mobility ........................................................................................................................29 2.2.4 Venetian Bridges ............................................................................................................................29 2.3 Tourism in Venice .................................................................................................................................30 2.3.1 Popular Tourist Sites and Events ................................................................................................30

2.3.2 Magnitude of Tourists ...................................................................................................................31 2.4 Environmental Impacts on Mobility ..................................................................................................32 2.4.1 Acqua Alta .......................................................................................................................................32 2.4.2 Canal Wall Damage........................................................................................................................33 2.5 Venetian Traffic Models .......................................................................................................................33 2.5.1 Past Models .....................................................................................................................................33 2.5.2 Modeling Tools ..............................................................................................................................34 2.5.3 How Models Read Data ................................................................................................................35 Methodology ................................................................................................ Error! Bookmark not defined. 3.1 Quantifying Pedestrian Agents ............................................................................................................38 3.1.1 Focus Area and Key Counting Locations ..................................................................................38 3.1.2 Distinguishing Between Agent Types .........................................................................................40 3.1.3 Counting Method ...........................................................................................................................41 3.1.4 Field Forms .....................................................................................................................................43 3.1.5 Schedule for Performing Field Counts .......................................................................................44 3.2 Determining Video Surveillance Feasibility.......................................................................................37 3.2.1 Collecting Proof of Concept Sample Video Footage ...............................................................45 3.2.2 Statistical Comparison of Manual Counting Methods..............................................................45 3.2.3 Camera Set Up ................................................................................................................................46 3.2.4 Filming Scenarios ...........................................................................................................................46 3.2.5 Collecting Control Data Set for Future Software Verification................................................47 3.2.6 Verifying Software with Control Data: Experimental Design.................................................47 3.2.7 Qualitative Video Data Collection...............................................................................................48 3.3 Analyzing and Visualizing Collected Data .........................................................................................49 3.3.1 Nodular Formatting .......................................................................................................................50 3.3.2 Rules of Attraction .........................................................................................................................50 5

3.3.3 Census Tracts and Statistical Data ...............................................................................................52 3.4 Publicizing Data.....................................................................................................................................53 3.4.1 Deliverables.....................................................................................................................................53 3.4.2 Furthering a Pedestrian Model .....................................................................................................54 Results................................................................................................................................................................55 Analysis ..............................................................................................................................................................55 Recommendations ...........................................................................................................................................58 Bibliography ......................................................................................................................................................61 Appendices........................................................................................................................................................64 Appendix 1: Pedestrian Agent Types Flow Chart ..................................................................................64 Appendix 2: Census Data Graphic ...........................................................................................................65 Appendix 3: Map Layers .............................................................................................................................65 3.1 Hotels Layer .......................................................................................................................................65 3.2 Schools Layer .....................................................................................................................................66 3.3 Museums Layer ..................................................................................................................................66 3.4 Churches Layer ..................................................................................................................................67 3.5 Tourist Sites Layer.............................................................................................................................68 Appendix 4: Database Form ......................................................................................................................69 Appendix 5: Field Forms ............................................................................................................................70 5.1 Venetian Field Form .........................................................................................................................70 5.2 Tourist Field Form ............................................................................................................................70 Appendix 6: Establishment Data Form ...................................................................................................72 Appendix 7: B Term Schedule ...................................................................................................................73 Appendix 8: Budget .....................................................................................................................................75 Appendix 9: Census Data ...........................................................................................................................76

List of Figures Figure 1: A Map of the Framework of Venice ............................................................................................19 Figure 2: A Map of the ACTV Routes for Public Transportation ...........................................................21 Figure 3: Weather conditions greatly impair traffic flow in Venice..........................................................22 Figure 5: St. Mark’s Basilica ....................................................................... Error! Bookmark not defined. Figure 6: A Canal Near the Arsenale ......................................................... Error! Bookmark not defined. Figure 7: A Standard Street in Venice ...................................................... Error! Bookmark not defined. Figure 8: Area of Study Map ..........................................................................................................................38 Figure 9: Map of the Ten Counting Locations Used by the B’10 Team .................................................39 Figure 10: Google Map of Traghetto Locations and Bridges Locations. Blue Anchors Symbolize Traghetti Stops and Red and Yellow Marker Pairs Symbolize Bridge Locations ..................................40 Figure 11: Example of Counting Based on Direction on Bridge 6 ..........................................................42 Figure 12: Frame from time lapse camera taken at Bridge 6 on November 15th ..................................49 Figure 13: Current ARGOS camera placements courtesy of the Commune di Venezia ......................49 Figure 1 - Coulomb's Law ..............................................................................................................................51 Figure 2 ..............................................................................................................................................................51 Figure 3 ..............................................................................................................................................................51 Figure 4 - Force of Attraction ........................................................................................................................52 Figure 16: Sources and Sinks ..........................................................................................................................54 Figure 17: Flow Cart of Pedestrian Agent Types ........................................................................................64 Figure 18: Hotel Locations in San Marco ....................................................................................................65 Figure 19: School Locations in Venice .........................................................................................................66 Figure 20: Museum Locations in Venice ......................................................................................................66 Figure 21: Church Locations in Venice ........................................................................................................67 Figure 22: Church Locations in San Marco .................................................................................................67 Figure 23: Major Tourist Sites in Venice ......................................................................................................68 Figure 24: Mobility October Schedule ..........................................................................................................73 Figure 25: Mobility November Schedule......................................................................................................73 Figure 26: Mobility December Schedule ......................................................................................................74

List of Tables Table 2: Bridges and Traghetto Stops in the Study Area ...........................................................................39 Table 3: On Site Manual Pedestrian Counting Template ..........................................................................43 Table 4: Video Surveillance Data Collection Template......................... Error! Bookmark not defined. Table 5: Schedule for Bridge Counts ............................................................................................................44 Table 6: Schedule for Traghetto Stops .........................................................................................................45 Table 7: Venetian Resident Density by Age and District (From 2001 Census Data) ............................65 Table 8: Venetian Field Form for Manual Counts ......................................................................................70 Table 9: Tourist Field Form for Manual Counts.........................................................................................70 Table 10: Form for Institution Information ................................................................................................72

Executive Summary Venice, Italy is a city composed of islands that are separated by canals and connected by bridges. It can only be traversed by means of boat travel or on foot. These two modes of transportation are greatly independent, but coincide at public transportation boat stops. The main focus of this project was pedestrian traffic, on which data was quantified using an efficient field counting methodology, analyzed, and distributed to a third party collaborator to include in an agent-based model. Pedestrian traffic is often impeded by narrow walkways, bridges, street vendors, and public tourist attractions. These factors can impede movement and contribute to congestion. The maximum carrying capacity reported for Venice has been determined to be approximately 25,000 tourists per day, which is frequently surpassed and is the main contributor to the issue of pedestrian traffic congestion in the city1. The native population is approximately 61 thousand people, and the amount of tourists on many occasions outnumbers the local population by more than double. Consequently, many residents relocate to the mainland to escape the high volumes of tourists. The overwhelming amount of pedestrians that flow in and out of the city creates a need for a better understanding of pedestrian traffic flow and efforts towards improving mobility efficiency within the cityâ&#x20AC;&#x2122;s infrastructure. This will reduce the pressure on the city itself and on the occupants, and ensure easier transportation in and out and within the city. The mission of this project was to collect pedestrian traffic data for the end goal of developing a modeling system that collects and archives data to effectively predict the behavior of pedestrian mobility in the San Marco sector of Venice. This model will improve mobility efficiency throughout the sector. We worked to accomplish this goal by quantifying pedestrian traffic at critical flow points and integrating this data into an autonomous agent computer model to visualize mobility. Additionally, we developed a comprehensive methodology and procedure for employment by future IQP groups and other groups who wish to improve pedestrian mobility efficiency.

Â Swarbrooke, John. Sustainable Tourism Management. N.p.: CABI, 1999.Â 9

PEDESTRIAN TRAFFIC STUDIES To analyze pedestrian traffic, this team focused on collecting data on different aspects of mobility within the district of San Marco. Focusing on this area allowed for a thorough study of pedestrian mobility and the development of a resourceful visualization of the traffic in the computer model. Population was categorized into two agent typesâ&#x20AC;&#x201D;Venetians and tourist. Each type was quantified at seven major bridges and matched to their likely destinations and origins throughout the day using census data and data on known attractor locations. Several days of preliminary field counts determined high volume time brackets as well as assumptions that can be implemented while collecting data. Counts collected using video camera feeds recorded during several different pedestrian traffic scenarios and at various angles we used to determine the feasibility of collecting data from video surveillance cameras. Ridership data available from the public transit system (ACTV) as well as pedestrian counts performed at four traghetti stops were analyzed to determine boat usage. At the seven bridges at which data was collected, the following traffic levels by proportions of Venetians and tourists were determined: [Map] [Blurb analysis] The following map displays the traghetti usage throughout the day by arrival and departure into and out of the San Marco district: [Map] [Blurb analysis] At each of the data collection locations, the overall pedestrian flow into and out of the district is shown in the following map. Future studies can supplement the data collected with additional data from the remaining bridges and traghetti in the San Marco district, as well as with data from other districts to visualize mobility throughout the entire city of Venice. Traffic levels throughout the year, especially during the tourist season, as well as the effects of weather conditions should also be studied. Video surveillance installed at birdâ&#x20AC;&#x2122;s eye angles at each major bridge location and traghetti stop, and the video 10

surveillance already installed at ACTV stops is recommended to collect counts year-round for daylight hours.

AUTONOMOUS AGENT COMPUTER MODEL

CONCLUSIONS

Introduction (Chelsea Version) Cities worldwide adopt a negative reputation for their mobility issues. Many travelers avoid city traffic to save time on their trip, and those who cannot avoid traveling through cities must plan ahead accordingly. Mobility is the freedom to move about, and when mobility is impeded, people are forced to interrupt their routes and pace and accommodate for their lost time. Mobility issues are even more perceptible in Venice because the only modes of transportation are by foot through complicated walkways and over narrow bridges, and by boat.

Figure 1 A map of the framework of Venice's islets, canals and walkways.

The city is made up of 121 islets connected by 435 bridges2, with no room to expand. The branch canals range from 10 to 30 feet in width, and the intricate network of walkways are made up of streets of no more than seven feet wide; the widest don’t exceed twenty feet3. In 2008, the City Council Tourist Department released its annual report, claiming that, in 2007, 5,875,370 people visited historic Venice4. 16% were Italians, and the rest were foreigners. This figure has doubled since the 1980s. Case studies run in 1991 determined that the carrying capacity of Venice was 25,000

http://www.comune.venezia.it/flex/cm/pages/ServeBLOB.php/L/EN/IDPagina/117

3(Morgan

1782)

http://www.aguideinvenice.com/en/venice‐case‐8‐Report‐on‐tourism‐in‐Venice‐December‐ 2008.html 12

visitors per day5. As illustrated in Figure ##, tourism is consistently increasing in Venice, but the infrastructure is limited to the amount of pedestrians it can contain.

Tourist Forecast within Historic Districts 40,000,000 35,000,000 30,000,000 25,000,000 20,000,000 Overnight Tourists Day Trips

15,000,000 10,000,000

Total Tourists

5,000,000 0 1940

1960

1980

2000

Swarbrooke, John. Sustainable Tourism Management. N.p.: CABI, 1999.

2020

2040

2060

Locations that cause holdups in traffic are called bottlenecks. Bridges are evident locations where traffic jams frequently occur. Alongside bridges, pedestrians in limited amounts can get from island to island using the gondola di traghetti or the Azienda del Consorzio Trasporti Veneziano (ACTV), the public boat transportation system. These forms of boat transport have helped alleviate a portion of the overcrowding at bridges as well as facilitate the flow of water traffic by centralizing water travel through 20 routes on the canals, as seen in Figure ##. However, at certain times of the day, waiting for space on these two types of boat transportation slows pedestrian movement down.

Figure 2 A map of the ACTV public transportation system.

On various occasions, such as the Carnival and other festivals, traffic can become so severe that pedestrians come to a standstill. During these instances, the city must take reactionary measures to alleviate congested areas. These reactionary measures include calling upon the police last-minute to go on site and direct traffic flow, or temporarily making walkways unidirectional. By the time officials can get to their stations, the traffic is already at a severe state; and when pedestrians are not informed of changes in accessibility, their routes must be amended unexpectedly. If there were a preventative measure installed so that city officials could predict traffic behavior, then traffic can be dealt with before it reaches an extreme state and events and transport can operate more smoothly.

Figure 3 At some instances, streets can become so crowded that traffic is at a standstill

The Venice Mobility Teams have for the past several years been working with the Department of Transportation and Mobility, collecting qualitative pedestrian data with the intention of creating a computer model to be used as the method for preventing traffic issues. There have been some holes in the data and execution, however. Individuals are responsible for collecting data on-site, risking the chance for human observational error. Data is collected in intervals of time and only in the tourist off-season when Worcester Polytechnic Institute Interdisciplinary Qualifying Project groups are on location. Also, the data that has been collected in the past disappears with time because there is no central database for archiving data. In order to create a comprehensive pedestrian computer model, there should be an automated data collection method so that data is continuously collected and archived in a public online resource. The city has several observational systems installed that would be advantageous for the purpose of preventing traffic issues. These surveillance systems are the Automatic and Remote Grand Canal Observation System (ARGOS), Hydra, and Security and Facility Expertise (SaFE) and they are placed in strategic locations throughout Venice that give them the ability to allow data to be collected not necessarily in real-time, but off of video clips that can be played back. Currently, these observational systems are used to implement speed limit laws, and monitor pedestrians and boats for crime. ARGOS gives the vigili urbani (the Venetian police) the opportunity to routinely dispatch officers to control traffic and make arrests on the Grand Canal, and Hydra and SaFE allow authorities to monitor the Venetian ports for potential crime6. If these cameras, as well as other cameras that could be installed in the future at other tactical locations, we used to collect traffic data, 6(Bloisi,

et al. 2009)

the data could be collected at all times of day and all year round. Clips could also be rewound and slowed down, to make sure that observational counts were collected as accurately as possible.

Figure 4 With the ARGOS system, live images are stitched together to generate a view of the Grand Canal. Observations are used from a multi-step Kalman filter to track targets over time7

Another key advantage that video surveillance has is that it can be paired with computer software that distinguishes between different types of pedestrians, which are referred to as agents for the purpose of the computer model. The benefits of this identification feature in data collection is that each agent type will have its own behavior and walking speed and will go to different points of attraction. In Venice, pedestrians can be broken down into two simple agent categoriesâ&#x20AC;&#x201D;Venetians and tourists. For example, tourists are more likely to have a random walking pattern, being attracted to museums and hotels and shopping centers, while Venetians are more likely to have a structured pattern to and from home or work. In order for the model to accurately predict the flow of traffic, it must be able to illustrate the differences in walking patterns between locals and tourists.

Â http://www.dis.uniroma1.it/~bloisi/segmentation/segmentation.html#ARGOS_ProjectÂ 16

Establishing a framework for the collection of data and developing the database for the computer model is where the Venice 2011 Mobility team comes into play. A structured methodology for collecting and archiving data has been instituted that can be executed by future traffic improvement teams. Additionally, this methodology has been executed at key bottleneck locations throughout the San Marco district for the continued development of this system. This data was integrated into the beginnings of an agent-based computer model designed by the Mobility teamâ&#x20AC;&#x2122;s collaborators, along with data compiled from various sources provided by the municipality of Venice, that is to be continuously added upon. The end goal of this project is to have a pedestrian agent-based automated model that will predict the flow of traffic efficiently for the benefit of alleviating traffic throughout the streets of Venice.

Introduction Cities worldwide adopt a negative reputation for their mobility issues. Mobility is the freedom to move about, and when mobility is impeded, people are forced to interrupt their routes and pace and accommodate for their lost time. Many travelers avoid city traffic to save time on their trip, and those who cannot avoid traveling through cities must plan ahead accordingly. Battling traffic wastes pedestrian time, and municipal authorities spend millions of dollars on regulating traffic with approaches such as police control, road construction, and regulation laws. In the 90 largest urban cities in America, 41 hours were spent per traveler in traffic in the year 20078.This could be the result of overwhelming traffic density, traffic accidents, and other various obstacles. In an attempt to better increase mobility, urban districts adopted public transit systems in the form of buses, underground subways, trams, trains, and even boats. These systems can transport large amounts of travelers and ease the congestion that results from high usage of private transportation. Other key traffic management tools include stoplights at busy intersections, speed limits to prevent hindrances from accidents, and separate lanes for directional management. For example, in Vienna, Austria, designated lanes are utilized to safely integrate bike and pedestrian traffic on sidewalks9. By creating structuralized means for transportation, cities are able to increase mobility and moderate congestion. The framework of canals and narrow streets that makes up the city of Venice, as seen in Figure 1, has prevented the invasion of automobile traffic automobiles from being a means of travel, but has consequently made water transport and travel on foot the two main modes of transportation. This creates a need for traffic congestion solutions that specifically apply to Veniceâ&#x20AC;&#x2122;s unique situation.

8(Traffic 9(Lopez

Congestion Factoids 2009) 2006)

Figure 5: A Map of the Framework of Venice

Built on a lagoon and properly titled Laguna Veneta, Venice is made up of 117 small islands with 150 canals and 409 bridges10. The branch canals range from 10 to 30 feet in width, and the intricate network of streets is mainly made up of mere lanes no more than seven feet wide; the widest do not exceed twenty feet11. It is with such a limited infrastructure, unique from any other city in the world, which makes congestion in Venice even more problematic. The city has a dire need for regulation applications that will alleviate the strain that traffic brings to the city. Some regulations that are already in effect when traffic becomes overly congested are one-way bridges and police officers that are dispatched to control flow. One of the greatest reasonsWhile the limited infrastructure largely affects mobility, another major impact on pedestrian traffic that traffic is such an issue in Venice is tourism. However, sinceSince the end of the 18th century, the Venetian economy has heavily relied on tourism, and it is a necessary burden on the city. With a native population of approximately 61 thousand people12, the amount of tourists flowing through the city on any given day outnumbers the locals in up to a 5:2 ratio13. While the cityâ&#x20AC;&#x2122;s economy is very firmly bound to tourism and its related industries, these visitors have 10(Centre

2010) 1782) 12(Italy n.d.) 13(Amilcar, et al. 2009) 11(Morgan

contributed to many problems for Venice and its inhabitants. The infrastructure is believed to be in danger of giving way to the mass amounts of traffic. Tourists congregate at specific sites throughout the city, and at places with nice views. This causes difficulty for the locals to travel around the groups of tourists, and creates congestion over bridges.Some streets and canals are more readily accessible than others at different times of the day, and mobility becomes increasingly hindered by people with baby carriages and handicapped people in wheel chairs. The issue of mobility in Venice is one that has been addressed by the Venetian government in a few different ways. The Azienda del Consorzio Trasporti Veneziano, or Actv, is a public waterâ&#x20AC;&#x201C;bus transit system that facilitates the flow of water traffic by centralizing water travel through 20 routes on the canals, as seen in Figure 2. There are also multiple surveillance systems in place, including the Automatic and Remote Grand Canal Observation System (ARGOS), Hydra, and Security and Facility Expertise (SaFE). These observational systems are used to implement speed limit laws, and monitor pedestrians and boats for crime. The 2011 Venice Mobility sponsors have developed these systems and implement them daily in Venice. ARGOS gives the vigili urbani (Venetian police) the opportunity to routinely dispatch officers to control traffic and make arrests on the Grand Canal, and Hydra and SaFE allow authorities to monitor the Venetian ports for potential crime14. In addition to the observational systems, tThe sponsors of the 2010 Worcester Polytechnic Institute Mobility Interactive Qualifying Project developed a model for displaying boat traffic in the canals using data gathered from ARGOS and Hydra. Called the Venice Table, the model is an interactive program that displays the movements of boats through certain checkpoints on the canal.

14(Bloisi,

et al. 2009)

Figure 6: A Map of the ACTV Routes for Public Transportation

In an attempt to combat the pedestrian traffic congestion that plagues Venice, there must be an efficient method to understand it. There is an abundance of information available from the ARGOS, Hydra, and SaFE systems, as well as research from WPI IQPs, but there is no tool that combines all of the data and presents it in a way that can be easily used in traffic prevention. This year’s Mobility team and collaborators created a model that will eventually accomplish this throughout the entirety of Venice. The team is contributing to the model by collecting pedestrian data and creating a methodology that can be used by future groups investigating mobility.The sponsors of this project have completed a sufficient amount of research, and the systems are being run in an effective manner. There are some holes in the data and execution, however, and limitations to the observational systems. Individuals in Venice run all of the systems, so there is no automated system to collect and archive data, costing many man-hours. This also has great potential to lead to human observational error. The data collection methods should also be fully automated to ensure that data is continuously being collected. The limited time the WPI project teams spend in Venice means that manually collected data is currently being collected manually, in intervals of minutes at a time, and only in the tourist off-season. The scattered datasets create difficulty in presenting the data in the model throughout the year. Having counts taken only once a year by the WPI Venice Interactive Qualifying ProjectIQP groups does not take into account how peak tourist times, weather, seasons, events, times of the day, and other aspects affect pedestrian counts. An efficient, comprehensive model would be one that contains sufficient amount of data from year-round. 21

Comment [CS2]: I’m going to be deleting a bunch of this. I personally think it focuses too much on the model. Obviously that should be included in the introduction, but because it’s just an eventual goal and not something we’re directly working on, I think we should focus more on the process of getting to the model. Comment [CS3]: Also, we’re not doing automated data collection?

Figure 7: Weather conditions greatly impair traffic flow in Venice

Other significant improvements that need to be made areAnother aspect of the model created is the agent identification feature. Agent identification consists of recognizing the difference between a Venetian and a tourist. It is important to study the difference in agents because each different type has its own behavior and will go to different points of attraction, and each one will have its own mobility stream. While a tourist may drift to a museum or a shopping center, a Venetian will want to go to straight to and from work or home.

Figure 8: Bridges are key bottleneck locations for collecting traffic data

This gap in data collection is where the 2011 Mobility team comes into play. A specific methodology has been established that can be executed by future traffic improvement teams. The pedestrian traffic data that has been collected in Venice included a distinction between agent types, namely Venetians and tourists, at key bottleneck locations around the San Marco district. This data was 22

integrated into the pedestrian model designed by Team Mobilityâ&#x20AC;&#x2122;s collaborators as well as archived for use by future project teams. Through analytical processes, suggestions were made for furthering the efficiency of the pedestrian agent computer model. Another issue is that there is so much data on traffic in Venice, but there is no centralized location where all of this data is archived. To solve this concern, Team Mobility also collected all imperative census data, counting data, and past IQP data and placed it in easily accessed sources to facilitate the continuation of the creation of the pedestrian model.

Background Venice is composed of canals and narrow streets, which makes it a one-of-a-kind city to travel through. Though the historic city occupies merely three square miles of land, traveling quickly and efficiently can be a challenge due to a complex web-work of walkways, overcrowding in areas and events that attract tourists, a disconcerting water bus schedule with many different routes and times, and severe weather conditions where flooding can occur, forcing pedestrians to have to walk on platforms, further narrowing the plane of mobility. For the uninformed, moving through Venice can be an unnecessary crusade.

2.1 THE ARCHITECTURAL FRAMEWORK OF VENICE Venice is a very small, yet multifaceted city that has changed its role many times over the years. Now known as a “museum city”, it was not originally meant to be an attraction for people all over the world. The city was not meant supposed to hold as many people as it fequently does. Because of Venice’s physical limitations, it has a difficult time accommodating for the congestion issues that result from the mass influx of tourist. 2.1.1 Origins of the City

Venice is a city frozen in time. Its peculiar situation and magnificent architecture render it unique and peerless even in its decadence. How a city can be afloat in the sea and still be habitable and beautiful is marvelous. Interestingly enough, Venice originated in an “expedient of desperation” and became great by “accident of position15.” The city began as a collection of inhospitable islands in the Venetian lagoon, along the western shore of the Adriatic Sea. The invasions of the Lombards into northern Italy in AD 568 drove many mainland Italians onto a group of islands of the lagoon, which were originally the homes of traveling fisherman and salt workers16. Because the canals and rivers were not easy to navigate and the lands were unwelcoming, the islands provided excellent protection against possible naval attack. The population of the new Venice revolutionized the balance of forces throughout Italy. All facets of society from the mainland were preserved along with their various rights and social roles. Among them were the leading members of their ecclesiastical hierarchy.

15(Morgan 16(Cessi,

1782) Cosgrove and Foot, Italy 2011)

Comment [FB4]: There is a problem with the organization of the paper…you start this section with a discussion of Venice and it’s geography etc…. then you lead into a statement about the problems with congestion….then…you go back to describing Venice and its lagoons, etc….I can’t follow the development of ideas in the paper….think about the organization…..

Waves of refugees continued to flow onto the islands as the Lombards gradually took more territory from the Byzantines until AD 639 when the fall of Oderzo solidified the collapse of the Byzantine defense system17. This was a key moment in the emergence of maritime Venice. Venice was still loyal to the Byzantine government, and therefore all public administration was still carried out in its name, yet the continuing war against the Lombards eventually brought strain to the government’s control of the city. The pressure of wartime life increased the Venetian’s inclination towards independence. The outbreak of religious conflict between Rome and Byzantium around 726 created serious clashes in Italy. Venetian troops joined forces with the Pope and took a stand against the authority of the exarch, electing the first doge, Duke Orso, while still remaining under the Byzantine Empire. It was not until the collapse of the empire in 751 that independence was accelerated. While Venice was dealing with political strife and continuous turnovers of power, it was also taking advantage of the opportunities offered by the sea and commerce. Trades passing through the city included dyes, leathers, spices, and many other goods. The lagoon province was the bridge between the European west, and the Islamic and Byzantine territories in the east. By the first half of the sixteenth century, Venice was the “great metropolis” that it is well-known for today. It hosted a variety of activities, trade continued on a large scale, and people came from all over the world. 2.1.2 Design of the City

What once was a group of islands with wooden houses resting on poles staked into unstable clay soil gradually morphed into an elegant and romantic city of stone. The buildings had to be strategically placed, taking into account the special environmental conditions of Venice. Weight had to be properly distributed so that there were never too many areas of stress18. Population and manufactures grew exponentially, and because the city could not expand outward, it expanded up. It was also less expensive to build another floor than to buy more land. Buildings were built close together, and very tall. The ground floor usually housed businesses, while the upper floors provided homes for families. As the city grew and its economy became prosperous, the structures reflected the transformation. The principal buildings in Venice were constructed of marble or light stone, and the remaining were 17

(Ortalli 1999)

18(How

Were Houses in Ancient Venice Designed and Why? n.d.)

of brick covered with mastic for adhesion19. Palaces and houses were built and rebuilt overtime, along with churches, monasteries, and bell towers. The shape and direction of the canals were changed and bridges, road systems and boat transportation were integrated. Various architectural styles such as the famous Gothic, Roman, Byzantine and Renaissance techniques were blended together. The architecture and design possesses characteristics of permanence and timelessness that is unsurpassable. 2.1.3 The Canals

The employment of a network of canals in place of streets was more a matter of necessity than of choice. The current canals circumscribe the original islands, while the rest of the water area has been recovered by erecting walls composed of granite along the line of these canals, which lay the foundation for the adjacent buildings20.

Figure 9: A Canal Near the Arsenale

The branch canals off of the Grand Canal are some fifteen feet wide, and are often crooked and short in length. The Grand Canal is one of the major water transportation corridors in the city; it stretches down the center of the city in a backwards S-shaped course and is approximately 2 miles in length, 30 to 70 meters wide21. The sides are lined with palaces and buildings reflecting the Gothic, Romanesque, and Renaissance grandeur from its early development.

19(How

Were Houses in Ancient Venice Designed and Why? n.d.) 1782) 21(Cessi, Cosgrove and Foot, Italy 2011) 20(Morgan

2.1.4 The Streets

There are 2,194 streets, each one as unique as its canals, which make up the labyrinth that is the city of Venice22. They too are narrow, short, and crooked, and they penetrate every part of the city. Most of them are passages about seven feet wide, with the widest of streets not more than twenty-five feet23. Some terminate abruptly and turn at sharp angles. Every street is covered with pavement, and on each side are gutter stones to pass surface water or rain into conduits underneath24. While the picture of these streets sounds uninviting, the close proximity is relieved by the numerous squares that intersect them. There are 294 squares scattered throughout the city25. The streets cross the canals by means of 409 bridges, consisting of a single arch, with a roadway graded into low steps, connecting every bit of land in Venice26.

Figure 10: A Standard Street in Venice

2.2 MOBILITY IN VENICE Due to its unique location, the city required extensive draining and dredging to provide more land to further the development of Venetian infrastructure. These operations led to the development of the first canals, and a rather unique system for the cityâ&#x20AC;&#x2122;s mobility27. Transportation in the city exists in three main entities: the canals, bridges across them, and an arrangement of walkways. This network of more than 200 canals became a staple for the transport of goods throughout the city as well an excellent form of transportation. 22(Morgan

1782) 1782) 24(Morgan 1782) 25(Morgan 1782) 26(Morgan 1782) 27 (Howard and Quill 2002) 23(Morgan

2.2.1 Watercraft in Venice

Transportation and distribution of goods via the canal network would be impossible without the use of watercraft. Throughout history, all major cargo shipments and heavy transport is done by boat. For example, gondolas are iconic boats of Venice which were once used by the wealthy for transportation28. These boats are keel-less and used almost exclusively for tourism in this day and age29. Gondolas became far less popular with the development of steam powered vessels, called vaporetti, in 1881. These vessels are still the dominant form of nautical transportation in the city. Venetian ferries, called traghetti, are unglorified gondolas which are another popular form of transportation in Venice, and there are now seven of these ferry crossings across the Grand Canal30. These ferries operate at certain points between bridges on the Grand Canal and shuttle pedestrians across for just 50 cents31. Larger boats are used in Venice for cargo shipments, as well as for sea trade throughout the Mediterranean. Due to this demand for large ships, and a lacking of local resources, many Venetians became expert shipbuilders32. During the Medieval Era, Venice became one of the mightiest cities because of this drive for mercantilism. Venice was a major port along many trade routes which connected Europe to other continents such as Asia through the use of the Mediterranean Sea33. Venice also had a very well equipped navy, which had the ability to build one war galley per day34. These galleys were handcrafted in shipyards called squeri where all types of traditional boats were crafted. 2.2.2 Water-Based Public Transportation

Private boats are less common in Venice than watercraft used for shipping cargo and public transportation. This is largely due to the existence of taxi boats and a lack of space for extended docking. Taxis in Venice are multipurpose boats which not only transport clients to their desired destination but will also serve as a means of transportation for goods when not serving pedestrians. There are also other vessels which have scheduled routes throughout the city which can be used to move people between specified stops. (Cessi and Foot, Venice 2011) (Cessi and Foot, Venice 2011) 30 (Drake 2008) 31 (Drake 2008) 32 (Davis and Marvin 2004) 33 (Davis and Marvin 2004) 34 (Davis and Marvin 2004) 28 29

These forms of public transportation are one of the leading causes of boat traffic in Venice. Both taxis and gondolas have random travel routes, depending on their clientsâ&#x20AC;&#x2122; demands, and therefore become difficult to obtain data on. For example, gondolas typically serve as sightseeing vessels for tourists and will typically slow down and make stops near points of interests35. These stops can cause a large amount of traffic and affect mobility. The traffic patterns of taxis and gondolas are difficult to predict and their destinations are random, therefore their traffic patterns do not significantly influence overall mobility in Venice. 2.2.3 Pedestrian Mobility

The other prominent form of transportation in the City of Venice utilizes an array of walkways and bridges. The problems associated with these walkways are derived from how the city was constructed, which led to limited space, and an increasing number of tourists which visit the city. As the city was being constructed, walkways were built to facilitate trade and commerce in the city. Due to the significant space constrictions associated with construction on an archipelago, many buildings were constructed to the edge of the property, leaving little space for these additional walkways. This fact has left many of the walkways narrow, some spanning only about a meter across36. The stark narrowness of the walkways contributes to much of the pedestrian related traffic which occurs in the city, but it is not the only factor involved. The layout of the walkways has been compared to that of a labyrinth as a result of many canals being paved over to broaden the network of walkways and alleviate traffic demands37. Pedestrian traffic demands have been growing perpetually since the1950â&#x20AC;&#x2122;s due to the overwhelming influx of tourists38. The combination of a large population of tourists new to the area and a confusing layout intensifies the effects of pedestrian congestion. 2.2.4 Venetian Bridges

The different islands of the archipelago are interconnected by an array of over four hundred bridges39. These bridges are crucial to the infrastructure of Venice, and have become recognizable as indispensable monuments of the city which are utilized on a daily basis40. Four of the most well-

(Chiu, Jagannath and Nodine 2002) (Davis and Marvin 2004) 37 (Davis and Marvin 2004) 38 (Van der Borg and Russo, Towards Sustainable Tourism in Venice 2001) 39 (Davis and Marvin 2004) 40 (Contesso 2011) 35 36

known bridges in Venice traverse the Grand Canal, including the Ponte di Rialto, Ponte dellâ&#x20AC;&#x2122;Accademia, Ponte degli Scalzi, and the most recent addition, the Ponte della Costituzione. The Ponte di Rialto was constructed in 1588, but initially had two predecessors. In 1175 a bridge was constructed using boats for floatation to span the canal, called a pontoon bridge, in the same location as the Ponte di Rialto41. This bridge was ultimately replaced in 1265 by a fixed bridge which later collapsed42. The Ponte di Rialto remained the only location to cross the Grand Canal until 185443. Today, pedestrians can cross the Grand Canal by using one of the four bridges which now exist, in addition to the seven different traghetti locations.

2.3 TOURISM IN VENICE The Queen of the Adriatic has been attracting foreigners for centuries, and according to Riganti and Nijkamp, the city can be considered a mature tourist destination, for it is one that witnesses negative environmental impacts caused by tourist congestion more frequently than other destinations44. The magnitude of tourists that visit Venice has a huge negative impact on the city. The resulting congestion causes mobility impairments throughout the city, and especially at top tourist locations and during peak tourist times. 2.3.1 Popular Tourist Sites and Events

The concentration of tourists is a problem that Venetians have been attempting to control for a very long time. There are a number of specific locations throughout the city that are typically visited by tourists, which creates congestion both en route to the destination and at the attraction itself. The Piazza San Marco, or St. Markâ&#x20AC;&#x2122;s square, is a popular tourist stop, where one can visit St. Markâ&#x20AC;&#x2122;s Basilica and bell tower. Another is the Ponte di Rialto (Rialto Bridge), a large bridge connecting one side of the Grand Canal to the other with shops along it. These destinations, as well as many other spots in Venice, are the cause of the large amount of pedestrian traffic that regularly occurs. Beyond the draw of the city itself, there many events held in Venice that attract a high number of tourists annually. The Carnevale di Venezia, or Carnival of Venice, takes place in February every year, and marks the beginning of Lent. A huge amount of tourists travels to Venice to witness the Venetian beauty and culture displayed throughout the Carnevale and to attend the various events held (Contesso 2011) (Contesso 2011) 43 (Contesso 2011) 44 (Riganti and Nijkamp 2008) 41 42

during it, such as La Biennale (a contemporary art festival highlighting architecture, independent films, and paintings, among other things) and the Vogalonga (a boat race through the Venetian lagoon)45. Events such as the Carnevale lead to an extremely high tourist volume, which in turn causes mobility impediments for pedestrians attempting to travel from one place to another in an efficient manner. 2.3.2 Magnitude of Tourists

The sheer magnitude of visitors to the city creates issues within the infrastructure and community. Traveling around world was once reserved for only the rich or influential, but it is now a viable experience for a majority of people. This evolution towards “mass tourism” is one that is clearly seen in Venice, where there has been a significant influx of tourists over the years46. The carrying capacity of Venice, or “the maximum number of visitors the attraction can handle at a given time without either damaging its physical structure or reducing the quality of the visitors’ experience” has been determined to be approximately 30,000 tourists per day47. This capacity is regularly surpassed, and that leads to the ultimate issue of Venetian traffic congestion. This congestion can be seen at tourist sites and on bridges, where the limited space often creates crowds of people trying to push through to their destination. Venice is becoming a European Disneyworld, or a museum city, where the tourists outnumber the natives: “[w]ith its thirteen million or more annual visitors and a local population of only around sixty-five thousand, historic Venice has the highest ratio of tourists to locals of any city in the world.”48 This overcrowding effect impairs and changes many aspects of life in Venice, not the least of which is commuting to and from work or attempting to traverse the city for another purpose. All of the factors described above: popular tourist spots, large events, and the city itself, cause an increase in tourists visiting Venice every year. The mobility impairment created by this group of people is severe, and must be addressed. The inability to traverse across the city lengthens work commutes for the employed and school commutes for students.

45(Carnevale

di Venezia 2012 2009) Lando and Bellio 2008) 47(Van der Borg, Tourism and Urban Development: The Case of Venice, Italy 1992) 48(Davis and Marvin 2004) 46(Zanini,

2.4 ENVIRONMENTAL IMPACTS ON MOBILITY Venice’s unique infrastructure is slowly degrading from the severity of the environmental impacts it sustains. The city’s environment is “… suffering from a general hydrogeological imbalance which is dramatically evident in the erosion of the lagoon morphology and in the number of exceptional high water events” in Venice49. This has been a problem for centuries, and the occurrence of tides high enough to flood, called acqua alta, has been increasing at an alarming rate: from four to five times per ten years at the turn of the 20th century to at least thirty times per ten years today50. 2.4.1 Acqua Alta

The phenomenon of acqua alta occurs when there are southeast winds and a high tide at the same time, which causes the waves to spill over the canal walls into the city streets51. When water overtakes the walkways, pedestrian traffic flow is slowed and the area in which pedestrians can travel is limited, creating severe congestion. Sidewalks become flooded when there is a tide 100 or more centimeters above the average sea level. Platforms raised 120 centimeters off of the ground, called passerelle, are placed strategically along flooded pathways to enable pedestrians to walk above the water. While this is a helpful and necessary strategy for staying dry, it has a severe impact on the walkers’ mobility. The passerelle are narrow and create a difficult passing situation. The cramped space makes the walking rate slow and creates pedestrian congestion. St. Mark’s Square, a popular tourist destination, is one of the lowest sections of the city, and as a result is flooded with every acqua alta. The passerelle are placed throughout the square and leading to other tourist destinations, and many tourists travel upon them. Since the platforms are “just barely wide enough for two-way traffic,” a tourist taking pictures or an older person walking slowly can cause a large section of the walkway to become congested52. If the tides rise higher than 120 centimeters, the passerelle are at risk of floating off of their supports. When this happens, walkways are completely hindered and only those with rainboots can walk through the city without wetting their feet.

49(Rameiri,

et al. 1998) et al. 1998) 51 (Davis and Marvin 2004) 52 (Davis and Marvin 2004) 50(Rameiri,

2.4.2 Canal Wall Damage

Acqua alta is also a contributor to the erosion that is impacting the city so severely. The other large cause of erosion is the wakes caused by motor boats. As water collides with canal walls, it erodes the mortar that acts as an adhesive between the bricks and stone, and the wall becomes “more susceptible to the destructive stresses and forces” of the tides and wakes53. When the erosion becomes dangerous for pedestrians or the infrastructure, the walls must be repaired. Construction is necessary, but impairs mobility because the materials and space required for restoration overtake parts of the walkways. This causes backups down the walkways and has an overall negative effect on congestion.

2.5 VENETIAN TRAFFIC MODELS Looking into future applications of data collection, the creation of an integrated pedestrian traffic model is necessary to provide an easy means of extracting useful information. Though the development of such a comprehensive model is out of reach for this year’s Mobility team given the

Comment [C5]: Make sure to tie everything back to our project specifically. The past models don’t matter if they don’t apply to our project Comment [C6]: I don’t know. Weird sentence structure and is it necessary?

time and fund limitations, it is important to understand pedestrian models so that data collection can be tailored to provide the models with information that is useful to its creation. The modeling approach that fits the needs of the Venice traffic model is referred to as agent-based modeling, and more specifically, autonomous agent-based modeling. This type of modeling allows for individual governing of agents, which lets each agent uniquely interact with the environment based on programmed predispositions and reactions. In modeling of traffic, each agent will be assigned a specific start and end location. Though the beginning and end are predefined, the method of transportation and the path taken vary based on the interactions between the agent and its surroundings, including other agents. In terms of Venice, agent-based modeling allows for the important distinction between tourists and locals in pedestrian mobility stream models. The accuracy of such a model is proportional to the agents’ ability to mimic the real life counterpart. Hence it is important to collect data that can speak to the various biases of agents. 2.5.1 Past Models

Since the beginning of the Venice Project Site, there have been several Interactive Qualifying Project teams that have done work that helped further traffic models. In 2008 a team created a pedestrian

(Black, et al. 2008)

Comment [C7]: Define agents

model using NetLogo, an agent based modeling environment54. The model focused on Campo San Filippo e Giacomo due to project time and resource constraints. This spot was chosen because it was identified as a hotspot, or high traffic area. The model accounted for Venetian and tourist agents and dictated their speed based upon data collected during the IQP. The model only portrayed traffic during Wednesday at 1300 hours due to data limitations. The data collected by the team during the IQP was inputted to the program. This data was collected and recorded visually using three cameras set up strategically around the hotspot55. Though the model created was limited and didnâ&#x20AC;&#x2122;t accurately portray congestion, it still demonstrates the necessity of an experienced programmer in creating a model, and demonstrates one accurate data collection technique. The importance of recording visual data should not be underestimated. It is crucial to confirming and checking past data collection.

Comment [C8]: Potentially opinion?

There was also a traffic model created in 2010 that detailed boat traffic in the city. This project was called Venice Table. The programming aspect was spearheaded by RedFish group and the Santa Fe Complex, with the Venice Mobility team providing the data for the model along with several government agencies. To allow for a comprehensive model of canal traffic, 23 observation points were used for data collection. In order to determine when each boat turns in the model, the data that was utilized included which canals boats entered from and returned to, the time of day, and each boatâ&#x20AC;&#x2122;s license plate number56. Control of the model was designed to be interactive and intuitive. To allow for the intuitive nature of the Venice Table, the model was built on an interactive gaming software program. 2.5.2 Modeling Tools

Traffic models are very useful tool for understanding and improving mobility streams. Unfortunately, the creation of good models takes time, expertise, and data. The implementation of an autonomous data collection system will allow the collection of data with minimal human interaction. There are several tools present that can make this type of continuous autonomous data collection a possibility. One of those tools is Open CV, which is a software approach that uses video to autonomously recognize, track, and record traffic and distinguish physical differences, as well as velocity.

Comment [C9]: Connect this to our project

(C. Catanese, et al. 2008) (C. Catanese, et al. 2008) 56 (VeniceTable: Interactive Traffic Simulation Table 2010) 54 55

2.5.3 How Models Read Data

Over the years, Venice has had countless groups, individuals, and governments study it and collect a wide array of data relevant to traffic. The question therefore becomes “How is this data formatted so that it can be inputted into a model?” The agent based models have proved useful in the past and will continue to be a method of data presentation. Agents, in our case pedestrians, will interact with

Comment [C10]:

the environment, Venice, developed in the model. The environment itself is made up of two main

Comment [C11]: Word choice?

components; edges and nodes. Edges are the borders and boundaries that define the fields in which the pedestrian agent types move. Nodes, on the other hand, are not physical or visible entities in the final 2D model. They help to define how the pedestrians will move. For instance a specific pedestrian, depending on the constraints that are programmed into a model, will move from a node ‘A’ to another node ‘B’. For the Venice models, these nodes are typically placed at traffic ‘choke points’ like bridges. For instance, a bridge spanning a canal in an east to west direction might have a node ‘A’ on its east side and another node ‘B’ on its west side. Movement defined as ‘AB’ would indicate a pedestrian moving from ‘A’ to ‘B,’ or one traveling west across the bridge. Movement defined as ‘BA’ would indicate the opposite: a pedestrian traveling east across the same bridge. Therefore data is organized by the number and type of pedestrian, as well as their node movement at choke points. Nodes can also help define sources (points where pedestrians originate) and sinks (points where pedestrians are attracted). How agent types are programmed will determine their ‘source-sink interaction’. In Venice, sources and sinks can be split up into two categories based on the types of pedestrians. Locals tend to originate from residential areas and will generally flow to places of employment or learning. In this case, this would mean that their homes are the sources and their places of work and schools are the sinks. At the end of the day, this would be reversed and the sources and sinks would switch. Tourists tend to originate from hotels, bus terminals, and the train station, and are attracted to places like museums, shops, and the “tourist triangle”. In the case of a museum, two nodes would still have to be used to define movement ‘in’ and ‘out’ of the museum. The museum would then be defined visually on the model so the movement in and out of the building doesn’t look like pedestrians disappearing and reappearing at a point inside the model. Data on sources and sinks can either be collected by hand, as it has been done previously at bridges, or extracted from readily available information related to attendance at museums. Another method is counting pedestrians from a security camera video feed of the front door. 35

Comment [C12]: Is this defined already?

The concept of ‘disappearing’ and ‘reappearing’ occurs when modeling pedestrian traffic in Venice. Walking is not the sole form of transportation in the city, and many people use multiple forms of transportation throughout a day. If there is no integration between pedestrian traffic and boat traffic in the model, then when a pedestrian ‘gets on’ a traghetto or a water taxi in the model it will look as if someone disappeared from their original position and reappeared somewhere else. To combat this, data can be collected that reflects the number of pedestrians that are getting on and off at each boat stop. Nodes can then be used at each stop in the model to define movement on or off boats. A truly comprehensive Venice traffic model would completely integrate the boat and pedestrian traffic models into one because the various forms of transportation are not independent of one another.

Methodology The mission of this project was to collect pedestrian traffic data for the end goal of developing an agent-based modeling system that collects and archives data to effectively predict the behavior of pedestrian mobility streams in Venice. Project Objectives: 1. To quantify pedestrian traffic at key locations 2. To analyze the feasibility of using video based pedestrian traffic counting techniques 3. To organize the pedestrian traffic data into a format capable of helping develop a pedestrian agent-based model 4. To publicize pedestrian traffic data in a visually intuitive format on an online source This project focused on pedestrian movement throughout the district of San Marco in Venice, Italy. A methodology was developed for accurately counting pedestrians, and the feasibility of using video feeds to count pedestrians was investigated. The data that was collected was integrated into an agentbased model developed by the teamâ&#x20AC;&#x2122;s collaborators. Using real time pedestrian counts ensures that the walkers in the model have appropriate timing and destinations. Employing the methodologies that have been developed during this project in future years for the other five districts of Venice will ensure a greater understanding of pedestrian movement in the city. The project occurred from August to December of 2011, with preparatory work during the first 8week term and on site work throughout the latter 8 weeks. The project was limited to gathering data concerning pedestrian congestion, taking into account only the predetermined agent typology. To accomplish this, pedestrians were quantified based on direction of movement and whether the pedestrian was a local or tourist.

Figure 11: Area of Study Map

3.1 QUANTIFYING PEDESTRIAN AGENTS To accomplish the project objectives, Team Mobility counted pedestrians at key locations in the area of study. This data was then collected and integrated into a computer model for traffic analysis. To do this, a specific counting method was developed to conduct manual counts based on direction of flow and pedestrian type at key connection points around San Marco. This counting method is to be used by future teams in order to ensure consistent data sets. 3.1.1 Focus Area and Key Counting Locations

The 2010 Venice Mobility team previously analyzed congestion in the San Marco district at ten bridge locations, as seen in Figure ##57. However, the 2011 Mobility team focused on different counting locations, also known as nodes, for the purpose of creating a distinct location for the starting point of the computer model within the San Marco district. The Accademia bridge was the only bridge in common between the two collection years.

Amilicar, Marcus, Amy Bourgeois, Savonne Setalsingh, and Matthew Tassinari. Mobility in the Floating City: A Study of Pedestrian Transportation. Worcester: Worcester Polytechnic Institute, 2010.

Figure 12: Map of the Ten Counting Locations Used by the B’10 Team

After evaluating a map of the area, the counting locations were determined to take place at the six bridges that connect the two sections of land divided by the Rio San Luca, Rio del Barcaroli, and Rio San Moisè. It was concluded that, because Ponte dell’Accademia is the only bridge on the Grand Canal that leads into the western part of the San Marco district, it should also be analyzed by the team. Counts were also performed at the four traghetto stops in the district along the Grand Canal. These eleven counting locations covered all locations for pedestrians on foot into and out of the western half of the San Marco district. The complete list of bridges and traghetto stops are referenced in Table ##, and the map of each of these is seen in Figure ##. Table 1: Bridges and Traghetto Stops in the Study Area

Study Area Bridges

Study Area Traghetto Stops

Ponte del Teatro

Riva del Carbòn – Fondamente del Vin

Ponte de San Paternian

Sant’ Angelo – San Tomà

Ponte de la Cortesia

San Samuele – Ca’Rezzónico

Ponte dei Barcaroli o del Cuoridoro

Campo del Traghetto – Calle Lanza

Ponte de Piscina

Ponte San Moisè Ponte dell’Accademia

Figure 13: Google Map of Traghetto Locations and Bridges Locations. Blue Anchors Symbolize Traghetti Stops and Red and Yellow Marker Pairs Symbolize Bridge Locations 3.1.2 Distinguishing Between Agent Types

A useful feature of the pedestrian model is the distinction between pedestrian agent types, such as Venetians and tourists, because each type of pedestrian behaves differently. Venetians have a structured schedule that occurs daily. During the workweek, Venetian pedestrians leave their residence to go to the market, work, or school. The route traveled by locals is usually predetermined to account for the shortest path and time. Tourists are often random in their routes, and travel in a “wandering” pattern. Major tourist sites are often destinations, but they may stop at a shop or restaurant on the way. As a result, tourist movement is less structured. In order to reflect this different behavior in the agent-based computer model, it was important to collect data based on the type of pedestrian. The individuals that were on-site conducting the counts distinguished pedestrians mainly based on visual cues. As previously mentioned, Venetians had more of a direct route, so their pace was 40

steadier, while tourists had more of a random behavior. They often walked with pets or pulled dollies; and businessmen and women or employees were dressed in business attire. Tourists were singled out by whether or not they were holding cameras, or if they were in tourist groups led by a guide. They were more likely to wear leisurely clothing. A complete list of the classifications used is in Table ##. Tourists

Venetians

“Wandering” walking pattern

More direct walking pattern

Carries a camera or takes pictures

Business or uniform attire

Led by a tour guide

Briefcase or cart

Speaks in another language

Walking a pet

Window shops Looking at a map

3.1.3 Counting Method

In order to accurately quantify the flux of pedestrians at bottleneck locations the team utilized a specific counting method, which allowed a quick and efficient method of counting a large number of pedestrians. Once the peak times were discovered (when pedestrian mobility is at its heaviest), manual counts were conducted in the field based on direction of flow. Individuals were stationed at each bridge in clear view of pedestrian flow, with mechanical counters in each hand. Each clicker represented a direction of flow. For example, the clicker in the individual’s left hand represented pedestrians moving away from the counter, and the clicker in the individual’s right hand represented pedestrians moving towards the counter. For fifteen-minute intervals, the individual would click for each pedestrian that crossed the bridge and in which direction he or she moved. Each individual determined a node on the bridge, and clicked for each person to cross that node. For consistency, children being carried by their parent or in carriages, and dogs and other pets were not counted.

Figure 14: Example of Counting Based on Direction on Bridge 6

At the end of each fifteen minute interval, the number read on the clicker was recorded into a field form (see Appendix ##) which was later placed into spreadsheets to be submitted for integration into the agent-based computer model. If flow at the peak time was determined to be too heavy for one individual to count, then two individuals were stationed at that location and each individual counted only one direction of flow. This ensured the accuracy of the data collected. To determine the volume of tourists utilizing a specific bridge on any given day, three project members counted tourists while one project member counted total flow for 15-minute intervals for two-hour blocks during the peak volume time. The tourist counts were averaged to account for outliers (if one team member identified a significantly larger or smaller number of tourists) and recorded in database forms. A percentage of tourist attendance at each bridge was calculated by dividing the average by the total number of pedestrians. These percentages were applied to the rest of the bridge data collected by the 2011 Mobility Team and can be seen in Table/Figure ##. The 2010 team performed preliminary field counting to determine the limit of one counter, and found that one counter was capable of recording one direction of flow while distinguishing between Venetian and tourist without being overwhelmed. Their team decided that two counters per 42

location, one per direction, were necessary to reduce the risk of data loss. If a certain time or location was anticipated to have unusually high traffic volumes, the decision was made as to whether or not more than two counters would be stationed at that location. Additionally, to verify the efficiency of the 2011 model and the accuracy of the on location counts, this yearâ&#x20AC;&#x2122;s Mobility team employed same form for our video recording counts which are discussed further in Section 3.2. The counts made by each individual were then collaborated at the end of the time bracket and collected in Excel spreadsheets that were submitted to the collaborators at Santa Fe Complex and integrated into the pedestrian computer model. This data was also converted into a format visible on GIS Cloud for still-time visualizations. Refer to the following section ### for the details on the data collection forms. 3.1.4 Quantifying Traghetti Passengers

The same method for direction-based counting was used for counting at traghetti stops. A clicker in each hand represented the direction of traffic traveling into or out of the study area. The time of when the boat arrived and departed each stop was recorded along with the number of passengers that got on or off the boat. The field form for traghetti counts can be viewed in Section 3.1.5 in Table ##. 3.1.5 Field Forms

To collect all of the data in an organized manner for the utilization of the collaborators, a field spreadsheet template was created. This was used to collect the number of persons that cross through a specific node by type of agent, and in which direction of travel. Refer to Appendix ## for an example of a field form. The same spreadsheet template was used to collect counts through video clips that are discussed in Section 3.2. Table ## shows the columns that were filled out for collection of all on-field data. Table 2: On Site Manual Pedestrian Count Template

Date: Time

Location: Traveling To

Recorder: Count

Traveling From

Number of Tourists

Number of Venetians

The field form for traghetto counts were used to collect the number of passengers that entered or exited the San Marco district using a traghetto. Because traghetti usage is not continuous, arrival and departure times must be recorded. Also, because every passenger on a boat must travel at the speed of the boat, agent mobility patterns are negligible and agent types were not recorded. Table ## shows the template used to record on-field traghetti passenger data. Table 3 On Site Manual Traghetti Passenger Count Template

Traghetto:

Recorder:

Count Entering San

Departure Time:

Date: Arrival Time:

Count Leaving San

Marco

3.1.6 Schedule for Performing Field Counts

For the purpose of having consistent data for a comprehensive computer model of pedestrian flow, the team counted at specific times of day. After determining the peak volume times of and which bridges contained the majority of traffic (as seen in section ###), it was decided that these times would be the best to conduct counts for the model. While data from all times of day would be most ideal, due to the time limitation of seven weeks, the team sought the most crucial data for the framework of the model. Refer to section ## for recommendations on other schedule choices. The team decided that the best time to conduct counts was late afternoon into the early evening, when most people were retiring home from work or most tourists were ending their days or going to dinner. Therefore, a weekly schedule for counting was devised, as seen in Table ##. Table 4: Schedule for Bridge Counts

Bridge

Weekday

Weekend

Ponte de la Cortesia

15:30 – 18:30

Ponte San Moise

15:30 – 18:30

Ponte dell’Academia

15:30 – 18:30

Traghetto stops ran on strict operation schedules that had to be worked around, so time brackets for these counts were developed in order to cover all hours of operation for each stop. Table ## shows the operational hours for each traghetti stop. Table 5: Schedule for Traghetto Stops

Traghetti Stop

Monday – Saturday

Sunday

Riva del Carbon – Fondamente del Vin

8:00 – 13:00

Sant’Angelo – San Toma

7:30 – 20:00

8:30 – 19:30

San Samuele – Ca’ Rezzonico

8:30 – 13:30

Closed

Campo del Traghetto – Calle Lanza

9:00 – 18:00

The Campo del Traghetto to Calle Lanza traghetto was closed for work while the team was taking counts, therefore, no was collected for that stop.

3.2 DETERMINING VIDEO SURVEILLANCE FEASIBILITY 3.2.1 Collecting Proof of Concept Sample Video Footage

Looking to provide as accurate a traffic model as possible for the city of Venice, a proof of concept was developed in order to test the feasibility of such a system in Venice. The goal of the proof of concept for the Mobility Team was to provide a variety of video feed samples that represent the complexity and variety of pedestrian traffic in Venice. These video samples provided an appropriate and comprehensive data set for the traffic counting software which, once developed, can be used to test a final proof of concept. The variety of video feeds, once paired with the software, will provide

Comment [G13]: Use same term that is used in background/ vice-versa for continuity

insight into the camera orientations necessary for the best software based data collection. 3.2.2 Statistical Comparison of Manual Counting Methods

The video samples gave a means of remotely counting and verifying field counts with video based manual counts. Counts were conducted using the video by analyzing the sample frame by frame. This process was conducted to give what could qualitatively be considered the most accurate count given that as much time could be taken to count pedestrians traffic. This eliminates the requirement for instant analysis that occurs in field counts. The manual video counts were then statistically compared to the manual field counts to determine how precise the two counting methods were. The resulting statistical analysis showed that there was no significant difference between the video 45

Comment [CS14]: ?

manual counting and the in-field manual counts. This statistical analysis can also be conducted with counting software once developed. If all three counting methods are statistically similar then they collectively provide a precise data set. 3.2.3 Bird’s Eye View Camera Set Up

To collect video samples at bridges, a GoProTM HD Hero Camera was attached to a 12 foot rigging boom measured from the base of the rigging apparatus (rig). The rig was then securely fixed to the side of the bridge being counted using an industrial grade strap. Additional lashings were tied using 1/8 inch rigging line to provide stability and ensure structural integrity throughout the recording process. The rig was attached in such a spot so that would not impede traffic but would still provide a bird’s eye view of the traffic ‘choke-point’. These choke-points directly correspond to the assigned nodes on the Study Area map. The camera lens was aimed parallel to traffic flow with the ‘r4’ video resolution mode set on the HD Hero camera. This setting provided the most vertical viewing area with the maximum overall view. The video was collected in HD 960p resolution. Just like each bridge is unique, each camera set up is different to adapt to the environmental variance. This provided us with a variety of camera angles and camera orientations in the video streams. 3.2.4 Filming Scenarios

The video sample feeds collected were each fifteen minutes in length to provide continuity among pedestrian traffic data collected. The feeds covered a variety of scenarios often seen in Venice so that the proof of concept could reflect the range of possible scenarios that might be present in a permanent software based video traffic counting system. The variety of camera feeds also demonstrated several different camera angles and orientations. The various orientations served to provide a means to determine which camera orientations provided a viable frame of reference for software and video based manual counts. Below are the scenarios that feeds were collected for: Camera Angles Bird’s Eye View Horizontal Straight On (Directly facing the traffic flow) Horizontal Perpendicular (Facing perpendicularly to the traffic flow)

Scenario Good Weather (Clear/Sunny) – Low Volume of Traffic Good Weather (Clear/Sunny) – High Volume of Traffic Night Time – Low Volume of Traffic Night Time – High Volume of Traffic 46

3.2.5 Collecting Control Data Set for Future Software Verification

The development of a video traffic counting software system is a fairly complex task, especially creating an effective software system during the IQP process, given the resources and time constraints at hand. Being that these software based systems have shown much potential thus far, the development of such a system for use in Venice is likely in the future. The development of such a system may not occur ‘on site’ in Venice. Once the software is developed, it will still require the video feeds collected to show effectiveness in a Venice scenario. In addition to the video feeds, the final proof of concept will also require a control set of data to compare the software counts to. This control data will statistically be taken as the actual count of pedestrians at the node pairs and the software counts will provide the experimental data sets. This will then allow for percent error calculations which can statistically determine the effectiveness of the software counting system. The control data sets were collected physically during the collection of the video feeds using the same counting methods developed for manual data collection. This provides each fifteen minute video feed with a corresponding fifteen minute manual count of pedestrian traffic. The forementioned manual counts will provide data for traffic flow in both directions across the node pairs. 3.2.6 Verifying Software with Control Data: Experimental Design

Hypothesis: The OpenCV pedestrian traffic counting software will produce traffic counts as effective as current manual counting methodologies. Null-Hypothesis: The OpenCV pedestrian traffic counting software will not produce traffic counts as effective as current manual counting methodologies. Dependent Variable: Time – 15 minute counting segments. Independent Variable: Pedestrian Counts – Total, Both directions across node pair Number of Trials: 75 trials will be conducted per video sample. Each trial data run will produce a total pedestrian count as well as a count for each direction across the node pair. 25 trials for total pedestrian flow count. 25 trials for direction ‘A’ across node pair. 25 trials for direction ‘B’ across node pair. Each video sample will require 75 trials, split as fore-mentioned. 47

Comment [CS15]: Methodology??

Control: Manual Counts already collected and paired with video samples. Constants    

Same software used for each trial run Each manual count paired only with pre-determined corresponding video sample Carts/ bags/ strollers/ dogs are not considered countable pedestrians Children carried off the ground by adults are not considered countable pedestrians

Experimental Procedure 1. Run software with first video sample footage 2. Record the total pedestrian flow, the flow in direction ‘A’ across the node pair, and the flow in direction ‘B’ across the node pair 3. Repeat steps ‘1’ and ‘2’ 24 more times 4. Repeat steps ‘1’ through ‘3’ for each video sample Analysis: Calculate percent error and use standard deviation and a t-test to determine if the experimental results are statistically similar to the control data. Use a level of significance (alpha) of .05. 3.2.7 Qualitative Video Data Collection

Traffic by its complex nature is qualitative. In order to translate information about traffic to a format readable by the computer model, much of the data collected was quantitative. Recognizing the ability of qualitative data to communicate the realities of mobility in Venice to a large audience, effort was put forth to collect such data.

Comment [CS16]: Que?

3.2.7.1 The Duality of Data

The video sample feeds that were collected primarily to develop a quantitative statistical comparison between manual field counts, manual video counts, and software video counts also served another purpose. When pieced together, sped up, and converted to look more like a time lapse these clips provide an easy way to visually analyze traffic flow and an intuitive way to show traffic density. 3.2.7.2 Frame by Frame

Time lapse cameras were also used to collect qualitative data sets throughout the project. The cameras were set to take pictures at either five or twenty second intervals. Using flexible stands, they were attached to near counting locations to provide key vantage points for viewing pedestrian traffic. The time lapse videos provided visual data sets to pair with counts at bridges. 48

Comment [CS17]: I thought the time lapse was qualitative, not what we counted from. So it shouldn’t be in the methodology?

Figure 15: Frame from time lapse camera taken at Bridge 6 on November 15th 3.2.7.3 A Comprehensive Network

Comment [CS18]: Rec instead of meth?

Currently there are a variety of camera and video systems throughout Venice that are owned and operated by a number of different government organizations. Even without implementing counting software into these camera systems, they can still provide useful qualitative data sets, and helpful visuals for determining key congestion areas.

Figure 16: Current ARGOS camera placements courtesy of the Commune di Venezia

3.3 ANALYZING AND VISUALIZING COLLECTED DATA To provide a streamlined method of data acquisition during pedestrian counts, all data was recorded on prescribed forms. Following this procedure, all data was reorganized into Database Forms in a format which would be cohesive to the programming of the pedestrian model. To accommodate RedFish Groupâ&#x20AC;&#x2122;s modeling preferences, the data was put into terms of nodes, which exist for both sources of pedestrian flow, as well as attractors, or pedestrian destinations. In addition, information 49

was acquired from various alternate sources including field counts from past years, as well as the Census Statistics Office. 3.3.1 Nodular Formatting

To ensure that the agent-based model was performing as anticipated, the team came up with a usable format for tabulating the collected data for the programming requirements of the collaborators. Nodes, or location based entities, were created on the study area map, based on nodes already in existence due to government studies which occurred in the past two decades. These nodes would aid in the directional flow of pedestrian traffic within the model, creating constrictions on how many pedestrians travel from one location to another. In addition, these nodes were placed on either side of the bridges of focus to signify the directional flow of pedestrians traversing the bridge. For example, a pedestrian crossing Bridge 6, could cross from node L on one side of the bridge, to node K, and head towards the Ponte dellâ&#x20AC;&#x2122;Accademia, for visa versa for the direction of the Piazza San Marco. For locations such as residential areas, places of work, schools, and areas which to tend to attract short-term visitors, nodes were also created because these are pertinent to the creation of the most accurate model possible. Much of the information regarding these sources and attractors was based off of data received from the Census Statistics Office. 3.3.2 Rules of Attraction

For the purpose of programming, the most useful format for the data within these spreadsheets was to leave the data in its rawest form, as the counts themselves, in addition to determining the ratio of local and visiting population which frequent these nodes daily. The model originally contained random walkers which were then constricted by different rules for each agent type. These rules contain nodular attraction which, based on a probability, would draw or repel pedestrians. In addition, rules were added to create the chronologic effect of a typical day within the city. This included having pedestrians wake up at various times in the morning at their source node, travel to their respective destinations throughout the day, and end at the same source at various times during the evening. For example, the average 40 year-old Venetian would awake early in the morning and take a direct route to his/her place of labor, spend time there until travelling home, where they may run errands and stop at markets, or other stores on their way back to their residence. Tourists would likely behave much differently, starting their day later, either at an entrance to the city of Venice or a lodging facility, and travel for much of the day, wandering between various sites, and finally

Comment [CS19]: Table of nodes or reference appendix with them

returning to their point of origin. Each location node would have a different attractive force on each of the two agent types. This force of attraction, which was defined as FA, required multiple parameters to be considered in order to accurately model reality. These parameters included the individual’s desire to venture to a destination, as well as the individual’s distance from that destination. Similarly, the electromagnetic attraction and repulsion between subatomic particles is defined by two parameters, including distance and charge. By relating the criteria of desire to electromagnetic charge, a formula which defines FA in a similar manner as Coulomb’s Law was used.

Figure 17 - Coulomb's Law

In order to ensure an accurate number of each agent type arrived at every destination in the model, the “charge” of the location node was determined based on a ratio how many people had already arrived versus the number of people which frequent that destination. This ratio constantly changed throughout the day and existed simultaneously for each location. For modeling purposes, each node then required data to define the number of daily venetians, daily tourists, and continuously calculate the number of Venetians arrived, and tourists arrived. Furthermore, the two ratios would also exist as follows: 1

Figure 18

Figure 19

These two equations explain how, for the purpose of the model, the relationship between the attractive strength of a node had a negative correlation to the arrival of pedestrians. Furthermore, the attractive force, FA, was also modified by distance. To accommodate this, the r2 value was determined by the relative distance between the pedestrian and destination, based on the route of 51

travel. This was important to implement, because a tourist that wishes to go sight-seeing, is more likely to go first to destinations that are both desirable as well as in the vicinity. After combining all these elements, the final equations which describe the relationships between each node and agent are as follows:

Figure 20 - Force of Attraction

In order to implement all the data collected during field counts, which led to the development of the daily pedestrian statistics, Excel spreadsheets were submitted to our collaborators. These spreadsheets, by utilizing these nodular locations and the relationships described, were integrated into the pedestrian model in a format compatible with the programming language HTML5, which was used to create the model. 3.3.3 Census Tracts and Statistical Data

The remaining nodes within the area of study required additional data not provided through pedestrian counts. These nodes included many destinations of the model, such as places of work, as well as sources, such as residencies and various types of lodging facilities. In order to fulfill the requirements of the model and create probability data to appease the rules of agent attraction, the parameter which describes the total number of daily attendees needed to be discovered. Fortunately, census tracks are publicly available by request, and have the added precision of breaking the city down, not only by its districts, but also into almost four-thousand sections. This sectional breakdown allowed for a much more precise organization of data. These tracts contain information regarding the population, with gender and age breakdown, as well as numbers of both residencies and businesses which exist in each section. This supplementary data was organized into a spreadsheet form in order to apply it to the pedestrian model, where it would satisfy the remaining parameters for determining the attractive strengths of many locations, as well as the number of Venetian agents which would start and end their day in each particular section. In order to create a more accurate model, the ages of the local citizens was taken into account, and based on observation, would behave differently in regards to travel. For example, a resident between the ages of 15 and 19 was likely to attend school in the morning, whereas a Venetian of twice that age would be travelling to a place of occupation. 52

3.4 PUBLICIZING DATA AND RESULTS To ensure this project can be expounded upon by future Mobility teams, the 2011 team publicized data utilizing several different sources. Using visual aids, called deliverables, the team was able to present pedestrian traffic data through informative means, including videos, graphs, and maps. The methodology described throughout this section was implemented with the purpose of being used by subsequent project teams to expand upon the pedestrian model that demonstrates walkers’ movement throughout the entire city of Venice. To aid in expanding upon the model, all data and information can be extracted from the team website at sites.google.com/site/ve11mobi. There are separate sections for data, video clips, and graphics that display all of the visual results from the 2011 project, and the information that is in each section on the website can be seen in Table ##. Information

Location on Website

3.4.1 Deliverables

Deliverables are informative visual aids that aptly demonstrate the data collected and analyzed throughout the project. Mobility’s deliverables included graphs, video feeds, a time-lapse video, and a study area map. The graphs can be seen in sections ##, and are further analyzed in section ##. The video feeds that were taken at select bridges during the project contributed to the final pedestrian model, as described in section ##. A time-lapse video of the pedestrian flow over the ## Bridge can be seen in the “Deliverables” section of the 2011 Mobility website. This video shows the difference in the magnitude of people traveling over the bridge between the hours of ## and ##. The study area map, as seen in Figure ## is a Google Map of the San Marco district, with different “layers,” or data sets, that consist of sources and sinks, including residential districts, hotels, traghetto stops, hotels, museums, churches, schools, places of employment, and other key locations. Figure ## illustrates many of these types of sources and sinks.

Comment [CS20]:

Figure 21: Sources and Sinks

Ideally, this visual aid will allow the public to see the congestion locations and reconsider their route across Venice, taking into account the most congested areas as seen on the deliverable map. 3.4.2 Furthering a Pedestrian Model

The end goal of this IQP was to collect data in such a way as to further the development of a pedestrian traffic model that simulates the movement of people throughout the city of Venice. To do this, the team developed and followed the methodology outlined in the previous sections, and compiled all of the collected data. This enabled the programmer from Santa Fe Complex to use accurate data for the foundations of the model. The Results and Recommendations sections describe in detail how the project can be expanded upon by future project teams.

Comment [C21]: Graph or some compilation of data.

Results and Analysis ##.## EVALUATION OF PEDESTRIAN MOBILITY ##.## Traghetti Usage

While the intention was to count passengers at four traghetti stops in the San Marco district, the Campo del Traghetto-to-Calle Lanza crossing was out of operation during our designated time. Therefore, counts were performed at three traghetti crossings in the district and results are given for each traghettoâ&#x20AC;&#x2122;s full hours of operation collected over several days. The following map displays the comparison of passenger usage at each traghetto and the proportion of passengers entering versus the proportion of passengers leaving the San Marco district.

The orange bars on the graph represent the total number of passengers that used each traghetto stop in that day, illustrating that Santâ&#x20AC;&#x2122;Angelo was more frequently used than the others. However, it also operates for more hours throughout the day. The more notable aspect is that more people enter San 55

Marco district using the traghetti than they do leaving it. This is most likely because the district is heavily populated with residential areas along the Grand Canal, as seen in Figure ##. Locals are using the traghetti to get back home after working outside of the district, or after they drop off and pick up their children from school.

RESIDENTIALÂ LOCATIONS 4462 RESIDENTS

Figure 22 Gradient Map of Residential Locations

The following map shows the work locations in the San Marco region. There are less work locations in the district, therefore most of the residents must commute out of the area for work.

WORKÂ LOCATIONS Figure 23 Gradient Map of Work Locations

Overall, the usage of traghetti usage was less than expected, considering most locals would more than likely not want to use the major bridges that tourists frequently cross. An observation made during counts was that almost every passenger was a local, indicating that most tourists have not discovered these convenient and inexpensive gondola boats.

Recommendations 5.1 PEDESTRIAN MOBILITY EVALUATION RECOMMENDATIONS More pedestrian counts can be performed to improve upon the computer model and gain a more comprehensive understanding of pedestrian mobility in Venice. A continuation of the studies of the San Marco district as well as the other five districts of the city will supplement the data collected this year and in previous years for the ultimate goal of creating a model that will be used to prevent traffic congestion. 5.1.1 Continued Raw Data Collection Additional raw data should be collected and compiled to supplement the counts. Each census report and more ACTV data should be archived in the same public location as the data collected this year. More data concerning hotel occupancy, and museum attendance should also be compiled. Also, various stores have automated counters at their entrances that count the number of people who enter and exit. This would be useful information to obtain to also archive.

Figure 24 A counter outside of a stationary store in San Marco. It has the ability to count the number of people entering and exiting the store.

5.1.2 Expansions of Bridge Data Collection The more data that is collected at bridges, the greater the programâ&#x20AC;&#x2122;s ability to model traffic flow. Therefore, further counting should be performed at the three bridges utilized this year. Additionally, counts should be conducted at the other bridges in the San Marco district and throughout Venice.

Data should also be collected at more times during the day. This year’s team chose to collect at peak volume times. Since WPI projects are restricted to the beginning seven weeks of the tourist offseason, collecting more data and at other times of the day would provide a more accurate extrapolation for traffic during the peak tourist months. To provide the model with more data, other times should be considered. Continued studies into the proportions of Venetians and tourists that utilize specific bridges will help provide insight into the bridge choices each agent type is likely to use when there are more than one option. More detailed agent distinction will provide more accurate behavioral patterns for the model. Distinguishing between day tourists and overnight tourists would be very beneficial, each type has a different origin and would behave differently throughout the day. Overnight tourists tend to visit the major attractions and spend more time at each attraction. Day tourists tend to visit secondary attractions due to the city’s accessibility, and visit more attractions, due to the individual’s time limitations.

5.1.3 Intersection of Traghetti and Pedestrian Traffic Further studies of the traghetti stops analyzed by this project should be conducted. The rest of the traghetti stops along the Grand Canal should additionally be studied to better thread together the connection between traghetti transportation and pedestrians on foot. Other useful information that should be collected is the percentages of locals and tourists who use the traghetti. A thorough analysis into whether or not traghetti are a critical mode of transportation for pedestrians would be of use to the sponsors of this project at the City of Venice Department of Mobility. 5.1.4 Study of Other Situations This year’s project team decided to count during ideal circumstances, without unusual weather conditions such as heavy rain, extreme cold, or thick fog or during aqua alta, high tides. These types of weather conditions impair traffic mobility and should be studied in order to gain a complete representation of Venetian mobility in the model. Other mobility impairments that data should be collected for would be pedestrians with handicaps, strollers, and carts. These features slow down an individual’s pace and consequently impair mobility. 59

Age brackets should also be accounted for, because elderly are more likely to have a slower pace than those in younger age brackets. 5.1.5 Video Surveillance Counting Techniques Further fieldwork should be done with video surveillance. More video clips recorded of more traffic situations should be counted. Traffic situations to count would be during heavy or light rainfall, dense or light fog, or festivals. Attempts should be made to obtain surveillance footage from the vigili urbani and the polizi locali to better determine the feasibility of counting from surveillance technology currently implemented in the city. More extensive research in recognition software should be carried out. An autonomous agent-based computer model to eliminate the reliance on man-hours is the ultimate end goal, and efficient software to complement the computer model would achieve that goal.

5.2 COMPUTER MODEL RECOMMENDATIONS

5.3 SMART-PHONE APPLICATION RECOMMENDATIONS

Bibliography Amilcar, Marcus, Amy Bourgeois, Savonne Setalsingh, and Matthew Tassinari. Mobility in the Floating City: A Study of Pedestrian and Water Transportation. Interactive Qualifying Project Report, Worcester: Worcester Polytechnic Institute: Venice Project Center, 2009. Black, Kevin, Sara Migdal, Michael Morin, Dukens Rene, and Nick Vitello. Urban Maintenance and Venetian Accessibility. Interactive Qualifying Project Report, Worcester: Venice Project Center, 2008. Bloisi, D., L. Iocchi, P. Remagnino, and N. Monekosso. "ARGOS-A Video Surveillance System for Boat Traffic Monitoring in Venice." International Journal of Pattern Recognition and Artificial Intelligence, 2009: 1407-1502. Carnevale di Venezia 2012. 2009. http://www.carnevale.venezia.it/ (accessed September 18, 2011). Catanese, Chris, Danise Chou, Bethany Lagrant, and Rudy Pinkham. Floating Around Venice: Developing Mobility Management Tools and Methodologies in Venice. Interactive Qualifying Project Final Report, Worcester: Worcester Polytechnic Institute: Venice Project Center, 2008. Catanese, Christopher D., Danice Yequay Chou, Bethany J. Lagrant, and Rudy E. Pinkham. "Floating Around Venice: Developing Mobility Management Tool." 2008. Centre, Flight. "Italy: A Unique Work of Art." Footprints, 2010. Cessi, Roberto, and John Foot. "Venice." Britannica Academic Edition. 2011. http://www.britannica.com/EBchecked/topic/625298/Venice/24379/Canal-boats-and-bridges (accessed September 16, 2011). Cessi, Roberto, Denis Cosgrove, and John Foot. Italy. 2011. http://www.britannica.com/EBchecked/topic/625298/Venice (accessed September 16, 2011). Chiu, David, Anand Jagannath, and Emily Nodine. "The moto ondoso index: Assessing the effects of boat traffic in the canals of Venice." 2002. Contesso, Lia. Venice Bridges. 2011. (accessed September 19, 2011). Davis, Robert C., and Garry R. Marvin. Venice, the Tourist Maze: A Cultural Critique of the World's Most Touristed City. Berkeley: University of California Press, 2004. 61

Drake, Cathryn. "Venice crossings: A traghetto tour reveals the city's other side." Wall Street Journal, 2008: sec World News. Duffy, J. "Re-Engineering the City of Veniceâ&#x20AC;&#x2122;s Cargo System for the Consorzio Trasportatori Veneziani Riuniti." 2001. Fiorin, Franco, and Giorgio Miani. "Development Plans for Urban Public Transport." Edited by Rinio Bruttomesso and Marta Moretti. Cities on water and transport, 1995: 100-107. How Stuff Works: Geography of Venice. 2011. http://geography.howstuffworks.com/europe/geography-of-venice.htm (accessed September 18, 2011). How Were Houses in Ancient Venice Designed and Why? http://answers.yourdictionary.com/entertainment-arts/architecture/how-were-houses-in-ancientvenice-designed-and-why.html (accessed September 7, 2011). Howard, Deborah, and S Quill. The Architectural History of Venice. Singapore: B.T. Batsford, Ltd., 2002. Italy. https://www.cia.gov/library/publications/the-world-factbook/geos/it.html (accessed September 15, 2011). Lopez, Angela. "Assessment of the Measure to Ease Pedestrian Congestion." Association for European Transport, 2006. Morgan, L. H. "The City of the Sea." Harper's New Monthly Magazine, 1782: 481. Ortalli, Gherardo, and Giovanni Scarabello. A Short History of Venice. Pisa: Pacini, 1999. Rameiri, E., V. Cogo, Mattei, and F.E.E. "Indicators of Sustainable Development for the City and the Lagoon of Venice." (Fondazione Eni Enrico Mattei) 1998. Riganti, Patrizia, and Peter Nijkamp. "Congestion in Popular Tourist Areas: A Multi-Attribute Experimental Choice Analysis of Willingness-to-Wait in Amsterdam." Tourism Economics, 2008: 1-15. Traffic Congestion Factoids. March 2009. http://www.fhwa.dot.gov/congestion/factoids.html (accessed September 14, 2011). 62

Van der Borg, Jan. "Tourism and Urban Development: The Case of Venice, Italy." Tourism Recreation Research 17, no. 2 (1992): 46-56. Van der Borg, Jan, and A. P. Russo. "Towards Sustainable Tourism in Venice." Sustainable Venice: Suggestions for the Future, 2001: 159-193. VeniceTable: Interactive Traffic Simulation Table. 2010. http://redfish.com/SFComplex/projects/veniceTable.html (accessed September 5, 2011). Zanini, Francesco, Fabio Lando, and Manuel Bellio. "Effects of Tourism on Venice: Commercial Changes over 30 Years." Working Papers, 2008.

Appendices APPENDIX 1: PEDESTRIAN AGENT TYPES FLOW CHART

Figure 25: Flow Cart of Pedestrian Agent Types

APPENDIX 2: CENSUS DATA GRAPHIC Table 6: Venetian Resident Density by Age and District (From 2001 Census Data)

APPENDIX 3: MAP LAYERS 3.1 Hotels Layer

Figure 26: Hotel Locations in San Marco

3.2 Schools Layer

Figure 27: School Locations in Venice 3.3 Museums Layer

Figure 28: Museum Locations in Venice

3.4 Churches Layer

Figure 29: Church Locations in Venice

Figure 30: Church Locations in San Marco

3.5 Tourist Sites Layer

Figure 31: Major Tourist Sites in Venice

APPENDIX 4: DATABASE FORM Date Venetians Traveling A to B

Time Venetians Traveling B to A

Location ID

Total Venetians

Tourists Traveling A to B

Tourists Traveling B to A

Total Tourists

APPENDIX 5: FIELD FORMS 5.1 Venetian Field Form Table 7: Venetian Field Form for Manual Counts

Date: Time 7:00 7:00 7:15 7:15 7:30 7:30 7:45 7:45 8:00 8:00 -----16:00 16:00 16:15 16:15 16:30 16:30 16:45 16:45 17:00 17:00

Location: Traveling To A B A B A B A B A B --B A B A B A B A B A

Traveling From B A B A B A B A B A --A B A B A B A B A B

Recorder: Count

5.2 Tourist Field Form Table 8: Tourist Field Form for Manual Counts

Date: Time 7:00 7:00 7:15 7:15 7:30 7:30 7:45 7:45 8:00 8:00 -----16:00

Location: Traveling To A B A B A B A B A B --B

Traveling From B A B A B A B A B A --A 70

Recorder: Count

16:00 16:15 16:15 16:30 16:30 16:45 16:45 17:00 17:00

A B A B A B A B A

B A B A B A B A B

APPENDIX 6: ESTABLISHMENT DATA FORM Table 9: Form for Institution Information

Date

Time

Establishment ID

Location

Estimated Attendance

Capacity

Hours of Operation

APPENDIX 7: B TERM SCHEDULE

Figure 32: Mobility October Schedule

Figure 33: Mobility November Schedule

Figure 34: Mobility December Schedule

APPENDIX 8: BUDGET Team Mobility Budget â&#x20AC;&#x201C; Fall Semester 2011 Item Manual Clickers Binder Clipboards

Price/Item $5.00 $12.00 $4.00

Quantity 10 1 4

Total Price $50.00 $12.00 $16.00 $83.00

Price/Team Member $12.50 $3.00 $4.00 $20.75

APPENDIX 9: CENSUS DATA