Mobility_B11_Report_Draft2 by Team Mobility

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

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..................................................................................................................................................... 8 List of Tables ...................................................................................................................................................... 9 Executive Summary .........................................................................................................................................10 Pedestrian Traffic Studies ...........................................................................................................................10 Autonomous Agent Computer Model .....................................................................................................10 Conclusions ..................................................................................................................................................11 Introduction ......................................................................................................................................................12 Background .......................................................................................................................................................15 2.1 The Architectural Framework of Venice ...........................................................................................15 2.1.1 Origins of the City .........................................................................................................................15 2.1.2 Design of the City ..........................................................................................................................16 2.1.3 The Canals.......................................................................................................................................17 2.1.4 The Streets ......................................................................................................................................17 2.2 Mobility in Venice .................................................................................................................................18 2.2.1 Watercraft in Venice ......................................................................................................................18 2.2.2 Water-Based Public Transportation ...........................................................................................19 2.2.3 Pedestrian Mobility ........................................................................................................................20 2.2.4 Venetian Bridges ............................................................................................................................20 2.3 Tourism in Venice .................................................................................................................................21 2.3.1 Popular Tourist Sites and Events ................................................................................................21

2.3.2 Magnitude of Tourists ...................................................................................................................22 2.4 Environmental Impacts on Mobility ..................................................................................................22 2.4.1 Acqua Alta .......................................................................................................................................23 2.4.2 Canal Wall Damage........................................................................................................................23 2.5 Venetian Traffic Models .......................................................................................................................24 2.5.1 Past Models .....................................................................................................................................24 2.5.2 Modeling Tools ..............................................................................................................................25 2.5.3 How Models Read Data ................................................................................................................25 Methodology .....................................................................................................................................................27 3.1 Proving Assumptions............................................................................................................................27 3.1.1 There are Peak Times ....................................................................................................................28 3.1.2 Weekday Peaks are of Similar Magnitude ...................................................................................29 3.1.3 Weekend Peaks are of Similar Magnitude ..................................................................................29 3.1.4 Peak Times are Consistent Day to Day ......................................................................................29 3.1.5 Specific Bridges Carry the Majority of Traffic Flow .................................................................30 3.1.6 Secondary Bridges Carry an Insignificant Traffic Flow OR Carry a Predictable Percentage of Primary Bridge or Total Traffic Flow ..............................................................................................30 3.2 Quantifying Pedestrian Agents ............................................................................................................31 3.2.1 Focus Area and Key Counting Locations ..................................................................................31 3.2.2 Counting Tools, Devices, and Methods .....................................................................................32 3.2.4 Time Brackets for Performing Field Counts .............................................................................33 3.3 Determining Video Surveillance Feasibility.......................................................................................34 3.3.1 Camera Set Up ................................................................................................................................34 3.3.2 Video Counting Verification ........................................................................................................35 3.3.2 Verification Analysis ......................................................................................................................35 3.4 Analyzing and Visualizing Collected Data .........................................................................................36 5

3.4.1 Formatting.......................................................................................................................................36 3.4.2 Field Forms .....................................................................................................................................36 3.4.3 Pedestrian Modeling Techniques .................................................................................................37 3.4.4 Census Tracts..................................................................................................................................37 3.5 Publicizing Data.....................................................................................................................................38 3.5.1 Venipedia .........................................................................................................................................38 3.5.2 Deliverables.....................................................................................................................................38 3.5.3 Furthering Models..........................................................................................................................39 Results and Analysis ........................................................................................................................................40 Recommendations ...........................................................................................................................................41 Bibliography ......................................................................................................................................................42 Appendices........................................................................................................................................................45 Appendix 1: Pedestrian Agent Types Flow Chart ..................................................................................45 Appendix 2: Census Data Graphic ...........................................................................................................46 Appendix 3: GIS Cloud Map Layers.........................................................................................................46 3.1 Hotels Layer .......................................................................................................................................46 3.2 Schools Layer .....................................................................................................................................47 3.3 Museums Layer ..................................................................................................................................47 3.4 Churches Layer ..................................................................................................................................48 3.5 Tourist Sites Layer.............................................................................................................................49 Appendix 4: Database Form ......................................................................................................................50 Appendix 5: Field Forms ............................................................................................................................51 5.1 Venetian Field Form .........................................................................................................................51 5.2 Tourist Field Form ............................................................................................................................51 Appendix 6: Establishment Data Form ...................................................................................................53 Appendix 7: B Term Schedule ...................................................................................................................54 6

Appendix 8: Budget .....................................................................................................................................56Â

List of Figures Figure 1: St. Mark’s Basilica ............................................................................................................................15 Figure 2: A Canal Near the Arsenale ..............................................................................................................17 Figure 3: A Standard Street in Venice ...........................................................................................................18 Figure 4: Area of Study Map ..........................................................................................................................27 Figure 5 Google Map of Traghetti Locations and Bridge Locations. Blue anchors simbolize traghetti stops and red and yellow marker pairs simbolize bridge locations...........................................................32 Figure 6: Mechanical Tally Counter ..............................................................................................................32 Figure 7: Sources and Sinks ............................................................................................................................39 Figure 8: Flow Cart of Pedestrian Agent Types ..........................................................................................45 Figure 9: Hotel Locations in San Marco.......................................................................................................46 Figure 10: School Locations in Venice .........................................................................................................47 Figure 11: Museum Locations in Venice ......................................................................................................47 Figure 12: Church Locations in Venice ........................................................................................................48 Figure 13: Church Locations in San Marco .................................................................................................48 Figure 14: Major Tourist Sites in Venice ......................................................................................................49 Figure 15: Mobility October Schedule ..........................................................................................................54 Figure 16: Mobility November Schedule......................................................................................................54 Figure 17: Mobility December Schedule ......................................................................................................55

List of Tables Table 1: Assumptions ......................................................................................................................................28 Table 2: Bridges and Traghetto Stops in the Study Area ..............................................................................31 Table 3: Time Brackets for Manual Counts .................................................................................................34 Table 4: On Site Manual Pedestrian Counting Template ..........................................................................37 Table 5: Video Surveillance Data Collection Template..............................................................................37 Table 6: Venetian Resident Density by Age and District (From 2001 Census Data) ............................46 Table 7: Venetian Field Form for Manual Counts ......................................................................................51 Table 8: Tourist Field Form for Manual Counts.........................................................................................51 Table 9: Form for Institution Information ..................................................................................................53

Executive Summary Venice is a city composed of many islands connected by canals and bridges that can only be traversed by means of boat travel or on foot. These two modes of transportation are mainly independent of one another. The main focus of this project is pedestrian traffic, which is challenged by narrow walkways, stairs, 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 30,000 tourists per day, which is frequently surpassed and leads to the issue of traffic congestion in the city. The native population is approximately 61 thousand people, and the amount of tourists can at its worst outnumber the local population by more than double. Consequently, many residents relocate to the mainland to escape the high volumes of tourists. This overwhelming amount of pedestrians that flow in and out of the city creates a need for a better understanding of 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. Therefore, 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.

PEDESTRIAN TRAFFIC STUDIES

AUTONOMOUS AGENT COMPUTER MODEL

CONCLUSIONS

Introduction Cities worldwide adopt a bad reputation for their mobility issues. Many travelers avoid city traffic to save time on their trip. 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. 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 20071.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 sidewalks2. 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 has prevented the invasion of automobile traffic, but has consequently made water transport and travel on foot the two main modes of transportation, thus creating a need for similar traffic congestion solutions that apply more to Veniceâ&#x20AC;&#x2122;s more unique situation. Built on a lagoon, properly titled Laguna Veneta, Venice is made up of 117 small islands with 150 canals and 409 bridges3. The branch canals range from 10 to 30 feet in width, and the intricate network of streets are mainly made up of mere lanes of no more than seven feet wide; the widest donâ&#x20AC;&#x2122;t exceed twenty feet4. 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. 1(Traffic

Congestion Factoids 2009) 2006) 3(Centre 2010) 4(Morgan 1782) 2(Lopez

One of the greatest reasons that traffic is such an issue in Venice is tourism. However, since 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 people5, the amount of tourists flowing through the city on any given day outnumbers the locals in up to a 5:2 ratio6. While the cityâ&#x20AC;&#x2122;s economy is very firmly bound to tourism and its related industries, these visitors have 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. 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. 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. Our sponsors have developed these systems and implement them daily in Venice. 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 crime7. In addition to the observational systems, our sponsors have 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. 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 data is currently being collected manually, in

5(Italy

n.d.) et al. 2009) 7(Bloisi, et al. 2009) 6(Amilcar,

intervals of minutes at a time, and only in the tourist off-season. The scattered datasets create difficulty in presenting the data in the modeling systems. Having counts taken only once a year by the WPI Venice Interactive Qualifying Project groups or the Venetian Center of Mobility 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. Other significant improvements that need to be made are in the agent identification feature. Agent identification would consist 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. This gap in data collection is where the Venice 2011 Mobility team comes into play. There is virtually no data or research present on Venice pedestrian traffic. This has provided Team Mobility with the unique opportunity to pioneer data acquisition into pedestrian mobility streams. We will collect pedestrian traffic data in Venice with a distinction between agent types, namely Venetians and tourists. Using this data we will verify the accuracy of any past and future models. Through analytical processes we will then be able to make suggestions for future autonomous continuous data collection that can feed into an eventual integrated 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 web-work of walkways, overcrowding, areas and events that attract tourists, an inconvenient water bus schedule, and severe weather conditions. For the

uninformed, moving through Venice can be an unnecessary crusade.

2.1 THE ARCHITECTURAL FRAMEWORK OF VENICE In order to understand the significance of using agent-based modeling of mobility in Venice, it is important to study its infrastructure and its origins, and how its status as a major tourist attraction came to be. The city was not meant to hold as many people as it sometimes does. Because of Venice’s physical limitations, it has a difficult time accommodating for the congestion issues that result from overpopulation. 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 position8.” Figure 1: St. Mark’s Basilica

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 workers9. 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 8(Morgan 9(Cessi,

1782) Cosgrove and Foot, Italy 2011)

Comment [C1]: Opinion

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. 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 system10. 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

Comment [C2]: What’s that?

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

Comment [C3]: Dyes?

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 stress11. Population and manufactures grew exponentially 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. 10

(Ortalli 1999)

11(How

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

Comment [C4]: Opinion

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 of brick covered with mastic for adhesion12. 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.

Comment [C5]: Opinion

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 buildings13. The branch canals off of the Grand Canal are

Figure 2: A Canal Near the Arsenale

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 wide14. The sides are lined with palaces and buildings reflecting the Gothic, Romanesque, and Renaissance grandeur from its early development. 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 Venice15. 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 feet16. Some terminate abruptly and turn at sharp angles. Every street is covered with pavement, and 12(How

Were Houses in Ancient Venice Designed and Why? n.d.) 1782) 14(Cessi, Cosgrove and Foot, Italy 2011) 15(Morgan 1782) 16(Morgan 1782) 13(Morgan

Comment [C6]: Miles and meters, inconsistent

on each side are gutter stones to pass surface water or rain into conduits underneath17. 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 city18. Figure 3: A Standard Street in Venice

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 Venice19.

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 mobility20. 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. 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 transportation21. These boats are keel-less and used almost exclusively for tourism in this day and age22. 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 Canal23.

17(Morgan

1782) 1782) 19(Morgan 1782) 20 (Howard and Quill 2002) 21 (Cessi and Foot, Venice 2011) 22 (Cessi and Foot, Venice 2011) 23 (Drake 2008) 18(Morgan

These ferries operate at certain points between bridges on the Grand Canal and shuttle pedestrians across for just 50 cents24. 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 shipbuilders25. 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 Sea26. Venice also had a very well equipped navy, which had the ability to build one war galley per day27. 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. 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 interests28. 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.

(Drake 2008) (Davis and Marvin 2004) 26 (Davis and Marvin 2004) 27 (Davis and Marvin 2004) 28 (Chiu, Jagannath and Nodine 2002) 24 25

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 across29. 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 demands30. Pedestrian traffic demands have been growing perpetually since the1950â&#x20AC;&#x2122;s due to the overwhelming influx of tourists31. 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 bridges32. 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 basis33. Four of the most wellknown 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 Rialto34. This bridge was ultimately replaced in 1265 by a fixed bridge which later collapsed35. The Ponte di Rialto remained the only location to cross the Grand Canal until 185436. (Davis and Marvin 2004) (Davis and Marvin 2004) 31 (Van der Borg and Russo, Towards Sustainable Tourism in Venice 2001) 32 (Davis and Marvin 2004) 33 (Contesso 2011) 34 (Contesso 2011) 35 (Contesso 2011) 36 (Contesso 2011) 29 30

Comment [C7]: What are the other three? Might as well name them.

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 destinations37. 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 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)38. 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.

(Riganti and Nijkamp 2008) di Venezia 2012 2009)

38(Carnevale

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 years39. 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 day40. 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.”41 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.

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 Venice42. This has been a problem for centuries, and the occurrence of tides high

39(Zanini,

Lando and Bellio 2008) der Borg, Tourism and Urban Development: The Case of Venice, Italy 1992) 41(Davis and Marvin 2004) 42(Rameiri, et al. 1998) 40(Van

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 today43. 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 streets44. 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 congested45. 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. 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 wakes46. 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

43(Rameiri,

et al. 1998) (Davis and Marvin 2004) 45 (Davis and Marvin 2004) 46 (Black, et al. 2008) 44

parts of the walkways. This can cause backups down the walkways and have 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 [C8]: 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 [C9]: 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

Comment [C10]: Define agents

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 model using NetLogo, an agent based modeling environment47. 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 hotspot48. Though the model created was limited and

47 48

(C. Catanese, et al. 2008) (C. Catanese, et al. 2008)

Comment [C11]: If we’re not doing agents, this needs to be changed

didn’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 [C12]: 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 boat’s license plate number49. 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 a lot of time, expertise, and data. The implementation of an autonomous data collection system will allow the collection of data with

Comment [C13]: My senior year English teacher would say that this is a colloquialism…

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 [C14]: Connect this to our project

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 [C15]:

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

Comment [C16]: 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

(VeniceTable: Interactive Traffic Simulation Table 2010)

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. 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. 26

Comment [C17]: Is this defined already?

Methodology Our project mission is to collect pedestrian traffic data for the end goal of developing an agentbased modeling system that collects and archives data to effectively predict the behavior of pedestrian mobility streams in Venice. Project Objectives: 1. To quantify pre-determined pedestrian types at key locations 2. To determine the feasibility of using camera surveillance systems to collect pedestrian traffic data by verifying video feed counts with manual counts 3. To organize the pedestrian traffic data collected 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 The project occurred over the 2011 fall semester, with preparatory work during A term and on site work throughout B term. The project was limited to gathering data concerning pedestrian congestion, taking into account only the predetermined agent typology.

Figure 4: Area of Study Map

3.1 PROVING ASSUMPTIONS Traffic is an extremely complicated system. It is almost impossible to account for all of the factors that can affect traffic at once. This makes it very difficult to accurately collect useful data related to 27

Comment [C18]: We have to do some rewording when we actually prove/disprove the assumptions, and give definite conclusions.

traffic flow. In order to develop a simple counting methodology that can be easily repeated while maintaining efficacy, several assumptions about pedestrian traffic flow in Venice were made. Upon making these assumptions to develop the counting methodology, an experimental procedure was then developed to test the validity of these assumptions (Table 1). Table 1: Assumptions

1 2 3 4 5 6

There are peak times Peak times are consistent day to day Weekday peaks are of similar magnitude Weekend peaks are of similar magnitude Specific bridges carry the majority of traffic flow Secondary bridges carry an insignificant flow OR secondary bridges carry predictable percentages of primary bridge or total traffic flow

Several constants were developed for each of these experiments to help ensure accuracy: 

Counts were only conducted during ‘good weather’ o No precipitation o Temperature above 40 degrees F o Temperature below 90 degrees F



Counts not conducted during flooding or flood warnings

3.1.1 There are Peak Times

Low volume traffic flow carries significantly less importance from a data value standpoint than high traffic flow. High traffic volume is what creates poor flow and puts the largest burden on the traffic infrastructure. This being the case, and the unlikelihood of individuals counting in the future being able to perpetually conduct traffic counts every second of every day, the methodology for counting focuses on ‘maximum’ or ‘peak’ times. The peak counts that were conducted were performed in 3 hour blocks around the determined peak time. Proving this assumption was based on finding this three hour ‘peak block’. To do this, a fifteen minute data set was collected every 1 to 2 hours at the same bridge throughout a day. By qualitatively viewing traffic volume on bridges throughout a day we could eliminate large chunks of time as ‘non-peak blocks’. These negligible times include late at night and early morning. This process was conducted at multiple bridges on the same day. Once the data was collected it was graphed and the peaks were assessed. 28

FINDINGS. But really I guess those should be in the results section, so we have to stick strictly to methodology and you can ignore half of my comments in this section. 3.1.2 Weekday Peaks are of Similar Magnitude

Assuming that weekday peaks are of the same magnitude allowed those conducting counts to collect during one peak-time block over a week (ignoring weekends) instead of every weekday. In other words, this assumption states that traffic on weekdays is equivalent. To prove this assumption, data was collected every weekday during the peak-time blocks. This data was then compared statistically to see if there was a significant difference between each daysâ&#x20AC;&#x2122; peak data set. If there was no significant difference, then weekday peaks are of the same magnitude. It is

Comment [C19]: Once we prove, state findings

important to note that only being in Venice for seven weeks made it impossible to collect 10-12 trials worth of data.

Comment [C20]: Mention how many we did collect

3.1.3 Weekend Peaks are of Similar Magnitude

Assuming that weekend peaks are of the same magnitude allowed those conducting counts to collect during one peak-time block over a weekend instead of every weekend day. In other words, this assumption states that traffic on weekends is equivalent. To prove this assumption, data was collected every weekend day during the peak-time blocks. This data was then compared statistically to see if there was a significant difference between each daysâ&#x20AC;&#x2122; peak data set. If there was no significant difference, then weekend peaks are of the same magnitude.

Comment [C21]: State findings

It is important to note that only being in Venice for seven weeks made it impossible to collect 10-12 trials worth of data. 3.1.4 Peak Times are Consistent Day to Day

Once it was proven that peak times are the same throughout each weekday, and peak times are the same throughout each weekend, the team could then specifically focus on the peak times in which field counts should be conducted, and not be concerned about a particular day. Proof of this assumption allowed for the maximum amount of data to be collected for a general day. To prove this assumption, sample counts were collected in fifteen minute time intervals at each hour throughout each weekday. The same is done for each weekend day. Using a standard deviation curve, comparisons were made at each peak to see if the peaks at each bridge for each weekday fell in the same three-hour block. If this occurred, it was determined that the assumption was correct 29

Comment [C22]: Same as above, put actual results in

and counts could be collected anywhere on Monday through Friday, and on Saturday or Sunday for weekend data. 3.1.5 Specific Bridges Carry the Majority of Traffic Flow

The team proposed the assumption that not all of the six bridges connecting San Marco to the rest of historic Venice, carry the burden of most of the traffic. Some bridges lead to narrow alleyways and therefore are less utilized than the ones that lead to streets that contain shops and restaurants. Once this assumption was proven, counts were focused more on the bridges that are primarily used rather than the ones that are less frequently used. Given the teamâ&#x20AC;&#x2122;s time constraint of seven weeks, it was impossible to collect data for all of the sources and sinks around San Marco, therefore it was in our best interest to prioritize specific nodes.

Comment [C23]: Are sources, sinks, and nodes defined earlier in the report?

To validate this assumption, sample ranges of all of the bridges over the same time frame were compared. If the outcome illustrated that two or three of the bridges are more heavily used than the

Comment [C24]: Same.

others, so the assumption was kept and field counts were conducted by prioritizing the primary bridges. This allowed the team to collect more comprehensive data for the foundations of the model in progress. 3.1.6 Secondary Bridges Carry an Insignificant Traffic Flow OR Carry a Predictable Percentage of Primary Bridge or Total Traffic Flow

This assumption is an extension of the previous assumption. It allowed those counting to use key traffic points when counting and to ignore other points 100% or use key counting points to determine the traffic flow at other points. If a bridge had a negligible traffic flow, then data did not need to be collected there at all. If a secondary bridge has a measureable percentage of traffic flow of a primary bridge,then one only has to measure traffic flow at the primary bridge and use percentages to determine the flow over the secondary bridge in the same time frame. This assumption was proved by comparing similar data sample ranges of different bridges over the same general time frame, then determining percent flow of each bridge over the same time frames. Statistical analysis showed if flow over any bridge is insignificant or if percentages of flow either compared to another bridge or over total flow is constant from day to day.

Comment [C25]: Which were?

3.2 QUANTIFYING PEDESTRIAN AGENTS To accomplish the project objectives, Team Mobility accurately counted pedestrians at key locations in the area of study. To do this, we developed a specific counting method to conduct manual counts based on direction of flow at key connection points around San Marco. 3.2.1 Focus Area and Key Counting Locations

The 2010 Venice Mobility team previously analyzed congestion in the San Marco district at ten bridge locations, therefore as the succeeding team, our plans were to expand to different counting locations, also known as nodes for the purpose of the computer model, within the San Marco

Comment [CF26]: We should reference a figure that maps the 2010 counting locations

district. After evaluating a map of the area, we decided that our counting would take place at the four bridges that connects the two sections of land divided by the Rio San Luca, Rio del Barcaroli, and Rio San Moisè. We also concluded that, because Ponte dell’Accademia is the only bridge on the Grand Canal that leads to the western part of the San Marco district, it should also be analyzed by our team. Counts were also performed at the four traghetto stops in the district along the Grand Canal. The complete list of bridges and traghetto stops can be referenced in Table ##, and the map of the nodes can be seen in Figure ##.

Comment [CF27]: Insert our study area map

Table 2: Bridges and Traghetto Stops in the Study Area

Study Area Bridges Ponte del Teatro Ponte de San Paternian Ponte de la Cortesia Ponte dei Barcaroli o del Cuoridoro Ponte de Piscina Ponte San Moisè Ponte dell’Accademia

Study Area Traghetto Stops Riva del Carbòn – Fondamente del Vin Sant’ Angelo – San Tomà San Samuele – Ca’Rezzónico Campo del Traghetto – Calle Lanza

Figure 5 Google Map of Traghetti Locations and Bridge Locations. Blue anchors simbolize traghetti stops and red and yellow marker pairs simbolize bridge locations. 3.2.2 Counting Tools, Devices, and Methods

In order to accurately quantify the flux of pedestrians at bottleneck locations we utilized two types of counting methods which allowed us to quickly and efficiently count a large number of pedestrians. The first method was in-field, manual counting. The other was using several types of video technology to determine the feasibility of collecting data from video streams and clips. For the first method, we stationed ourselves at node locations and utilized handheld mechanical counters. One individual can manage a counter in each hand, one for each direction of traffic flow. However, depending on the severity of congestion at any given time, we determined whether it was necessary to station two counters at one location. Each individual had a timer to keep track of the elapsed time. If the weather was poor (raining, flooding, or excessively cold), the Figure 6: Mechanical Tally Counter

Mobility team did not be conduct manual counts in order to avoid discrepancies in the data. We counted only during ideal conditions, which provided us with the most accurate pedestrian traffic information. The second method for data collection was using video technology to determine the feasibility of collecting counts with video clips. For this process, our team used a GoPro camera mounted on a tripod in order to obtain footage from an aerial perspective, much like the surveillance systems used by the city municipality. Footage was then downloaded and counted in comparison with manual counts to prove our camera counting concept. Section 3.3 details the methodology for determining the feasibility of surveillance technology. 3.2.4 Time Brackets for Performing Field Counts

Our team anticipated that pedestrian mobility in Venice will differ at different times of day and days of the week, and was able to prove that traffic flow reaches a maximum at a certain time bracket, and on any given weekday this time bracket remains the same. Once our assumptions were proven, we chose that peak time to be our data collection interval and conducted manual counts in fifteen minute intervals for three hours. We also validated the assumption that weekend days had the same peaks and collected counts at that time bracket as well. Because the traghetti run at specific times, unlike bridges which can be utilized continuously, our team was able to count pedestrians at traghetti stops for their entire realm of operation under the same assumptions concerning days of the week. Venice traghetto stops also experience fluctuations in traffic patterns throughout the day. In the morning, Venetians travel to work or school and tourists embark towards their tourist destinations. In the late afternoon, traffic is heavier as locals take lunch breaks and tourists venture to the various attractions. At night traffic once again reduces because citizens return home from work and tourists conclude their day. Additionally, certain bridges will peak at different times than others because some bridges lead to narrow alleyways while others lead to busy squares and popular attractions. Therefore, our team determined a schedule of time brackets and intervals to structuralize our counting process so that counts were recorded consistently. Appendix

shows the schedules we used to conduct manual

counts at each node. The following table shows the time brackets that we used to divide any given day:

Comment [CF28]: We should have a separate appendix section for the traghetti schedules, and the time intervals we collected bridge counts

Table 3: Time Brackets for Manual Counts

Bracket Name

Start Time

End Time

Early Morning

7:00

9:00

Morning

9:00

11:00

Mid-Day

11:00

13:00

Afternoon

13:00

17:00

Evening

17:00

19:00

3.3 DETERMINING VIDEO SURVEILLANCE FEASIBILITY Development in software and video-feed based counting techniques have provided a much more comprehensive method of collecting pedestrian traffic data. OpenCV based software, allows users to analyze either live or recorded video feeds. Through the program, pedestrian counts as well as direction can be determined. These systems trump human based physical counting for several

Comment [C29]: This is background, not methodology

reasons. They have the ability to run continuously and provide data with no human input once set up. This means that where human counting methodologies need to rely on assumptions to account for gaps in data, video based systems can collect real data and work to eliminate experimental error due to perceived or estimated data sets. After initial software development costs, the systems can continually provide a wider range of coverage with each camera investment which gives a broader data input picture. 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 at key counting locations that were established in the Study Area Map. These video samples provided an appropriate and comprehensive data set for the Open CV software which, once developed, could be used in testing. The variety of video feeds, once paired with the software, also provided insight into the camera orientations necessary for the best software based data collection. 3.3.1 Camera Set Up

To collect pedestrian data at bridges, an HD Go Pro Camera was attached to a tripod rig 12 feet from the base of the stand. The stand was then placed in an area to the side of the bridge where the feed was taking place so that it would not impede traffic but would still provide a birdâ&#x20AC;&#x2122;s eye view of 34

Comment [C30]: All background.

the traffic ‘choke-point’. The camera lens was aimed perpendicular to traffic flow. The video feeds were each 15 minutes in length to provide continuity among pedestrian traffic data collected. The feeds covered a variety of scenarios often seen in Venice. The scenarios that feeds were collected for are: 

Good Weather (Clear/Sunny) – Low Volume of Traffic



Good Weather (Clear/Sunny) – High Volume of Traffic



Poor Weather (Overcast/Cloudy) – Low Volume of Traffic



Poor Weather (Overcast/Cloudy) – High Volume of Traffic



Rainy Weather – Low Volume of Traffic



Rainy Weather – High Volume of Traffic



Night Time (No Rain) – Low Volume of Traffic



Night Time (No Rain) – High Volume of Traffic

3.3.2 Video Counting Verification

The purpose of using video surveillance technology was to allow for manual counts to be collected without a person having to be on site. To prove this concept, our team conducted counts using video technology prepared as previously described, and recorded video clips during the on-field manual counts, then compared the two datasets to determine if using cameras as a means for collecting data was a practical method. 3.3.2 Verification Analysis

Once the video was recorded and the field count time interval was completed, the data was analyzed. Another individual who was not involved in the counts taken at that scenario counted pedestrians from the video clip to ensure the accuracy of the video counting methodology. This provided an unbiased viewpoint for every feed. For the purpose of determining the percent error from the video technology, counts taken from video clips were labeled “experimental” data and counts taken manual on field were labeled “actual” data. If the percent error calculated is determined to be too significant, then video surveillance technology was rejected for that scenario.

3.4 ANALYZING AND VISUALIZING COLLECTED DATA We used field forms and other data collection forms to properly format our pedestrian data to accommodate RedFish Groupâ&#x20AC;&#x2122;s modeling preferences. We also implemented census tracts to further our traffic datasets and to complement agent analysis. 3.4.1 Formatting

To ensure that the agent-based model our team is contributing to is performing as anticipated, our team came up with a usable format for tabulating data for the programming capabilities of our collaborators. However, we also had to take into account the visual limitations of the counters on field when collecting large amounts of data at once. It was important not to miss any individual while on field to ensure the least amount of error. The previous 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 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 to that location. Additionally, to verify the efficiency of our model and the accuracy of our on location counts, we used the same form for our video recording counts. The counts made by each individual was then collaborated at the end of the time bracket and collected in excel spreadsheets that were submitted to our collaborators and integrated into the pedestrian model. This data was also converted into a format visible to GIS Cloud for still-time visualizations. Refer to the following section 3.3.2 for the details on the data collection forms. 3.4.2 Field Forms

To collect all of the data in an organized manner for the utilization of our collaborators, a field spreadsheet template was created. This was used to collect the number of persons that cross through a specific station by type of agent, and in which direction of travel. Refer to Appendix 5 for an example of a field form. The same template was used to collect counts through video clips. This field form was also used to tabulate data in a form suitable for our collaborators to integrate into an agent-based model. Table 3 shows the columns that were filled out for collection of all onfield data.

Table 4: On Site Manual Pedestrian Counting Template

Date: Time

Location: Traveling To

Traveling From

Recorder: Count

To collect data such as the number of students enrolled in a school on location, or how many people buy tickets to a certain museum, or even how many Venetians attend a specific church, we used a survey guideline in the field. Key information from these sites would be attendance and hours of operation. Knowing the capacity of specific establishments helped create a better model agent interaction with the environment. The information collected was then inputted into a spreadsheet for use in GIS map layers and for the use of our collaborators. Table 4 below provides the intended information we would hope to acquire from these institutions. Table 5: Video Surveillance Data Collection Template

Date

Time

Establishment ID

Location

Estimated Attendance

Capacity

Hours of Operation

3.4.3 Pedestrian Modeling Techniques

Though the 2011 Mobility Team lacked the experience to create a NetLogo model based on the data collected, the data fed models created by the RedFish Group and other organizations. Aside from a working model, the data was also worked into several GIS cloud layers. The manual counts were able to show tourist:Venetian concentrations at collection points and also allowed us to create a â&#x20AC;&#x2DC;heat mapâ&#x20AC;&#x2122; that shows population density at certain points in time. Once these were overlaid on the GIS map, they were then compared to other layers to show correlation. The population density heat map layer, viewed in conjunction with source and sink layers (e.g. schools, hotels, and museums) shows the causes of the changes of population density throughout a day. 3.4.4 Census Tracts

Collaborating census data for our region of study is critical for supplementing our agent analysis. To better understand pedestrian behavior, the origins and endpoints of each agent must be detailed. The census layers of the GIS map complemented Venetian data that our team collected by providing a picture of the residence distribution of the Venetian pedestrian agents. For example, Figure 21 shows the amount of adults from ages twenty to sixty-four who live in particular regions in the San Marco area. 37

These different age brackets helped us understand the destinations of these different agent types. Agents under twenty years of age would likely leave their homes to go to a school in proximity to their residency. Census tract layers can also provide the location and quantity of employed Venetians in a region. Figure 22 shows an example of the employment source location distributions in San Marco.

3.5 PUBLICIZING DATA Once the data was collected, analyzed, and formatted using the techniques outlined above, we published our findings for public viewing through the following means. 3.5.1 Venipedia

Venipedia is an online source created and maintained by Venice IQP project groups. It is the “Venice Wikipedia” and contains articles on myriads of topics specific to Venice. Our project group contributed to Venipedia by creating new pages concerning the end results of the project. The new pages cover our organized data of the main research topics and the visual aids we created. This allows public access to the information, and can be expounded upon by future groups. 3.5.2 Deliverables

A major component of the Venice projects is deliverables, or visual and interactive aids that aptly summarize the findings of a project. Our deliverable is an interactive layered Google map of the city of Venice, with different “layers,” or data sets, that can be displayed on or hidden from the map. The layers consist of direction of travel, beginning and ending locations, schools and places of employment, residential and commercial zones, tourist hotspots, hotels, traghetto stops, and other key locations. Figure ## illustrates the many of these types of sources and sink locations.

Figure 7: 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. 3.5.3 Furthering Models

An objective of our project was to collect and format data in such a way as to further the development of agent-based modeling systems. We did this by complying with the correct data format for the models as specified by RedFish Group. We compiled all of our data, sorted it into the specific format, and edited it to include the correct dataset for RedFishâ&#x20AC;&#x2122;s purpose. Ultimately, this will enable the company to develop a model for pedestrian congestion, taking into account traffic flow and congested locations.

Results and Analysis

Recommendations

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Appendices APPENDIX 1: PEDESTRIAN AGENT TYPES FLOW CHART

Pedestrian Agent Types

Venetians

Students

Adults

Tourists

Elderly

One Day Excursionists

Figure 8: Flow Cart of Pedestrian Agent Types

Extended Visitors

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

APPENDIX 3: GIS CLOUD MAP LAYERS 3.1 Hotels Layer

Figure 9: Hotel Locations in San Marco

3.2 Schools Layer

Figure 10: School Locations in Venice 3.3 Museums Layer

Figure 11: Museum Locations in Venice

3.4 Churches Layer

Figure 12: Church Locations in Venice

Figure 13: Church Locations in San Marco

3.5 Tourist Sites Layer

Figure 14: 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 51

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 15: Mobility October Schedule

Figure 16: Mobility November Schedule

Figure 17: 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