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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

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Acknowledgements

2


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

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Table of Contents Abstract ............................................................................................................................................................... 1 Acknowledgements............................................................................................................................................ 2  Authorship .......................................................................................................................................................... 3  Table of Contents .............................................................................................................................................. 4  List of Figures..................................................................................................................................................... 7  List of Tables ...................................................................................................................................................... 8  Executive Summary ........................................................................................................................................... 9  Introduction ......................................................................................................................................................10  Background .......................................................................................................................................................13  2.1 The Architectural Framework of Venice ...........................................................................................13  2.1.1 Origins of the City .........................................................................................................................13  2.1.2 Design of the City ..........................................................................................................................14  2.1.3 The Canals.......................................................................................................................................14  2.1.4 The Streets ......................................................................................................................................15  2.3 Mobility in Venice .................................................................................................................................15  2.3.1 Watercraft in Venice ......................................................................................................................15  2.3.2 Nautical Congestion ......................................................................................................................16  2.3.3 Pedestrian Mobility ........................................................................................................................17  2.3.4 Venetian Bridges ............................................................................................................................18  2.2 Tourism in Venice .................................................................................................................................18  2.4 Environmental Impacts on Mobility ..................................................................................................20  2.5 Venetian Traffic Models .......................................................................................................................20  2.5.1 Past Models .....................................................................................................................................21  2.5.2 Modeling Tools ..............................................................................................................................22 

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2.5.3 How Models Read Data ................................................................................................................22 Methodology .....................................................................................................................................................24  3.1 Proving Assumptions............................................................................................................................24  3.1.1 There are Peak Times ....................................................................................................................25  3.1.2 Weekday Peaks are of Similar Magnitude ...................................................................................26  3.1.3 Weekend Peaks are of Similar Magnitude ..................................................................................26  3.1.4 Peak Times are Consistent Day to Day ......................................................................................26  3.1.5 Specific Bridges Carry the Majority of Traffic Flow .................................................................27  3.1.6 Secondary Bridges Carry an Insignificant Traffic Flow OR Carry a Predictable Percentage of Primary Bridge or Total Traffic Flow ..............................................................................................27  3.2 Quantifying Pedestrian Agents ............................................................................................................28  3.2.1 Focus Area and Key Counting Locations ..................................................................................28  3.2.2 Assigning Agent Types and Identifying Each Type’s Characteristics ....................................28  3.2.3 Counting Tools and Devices ........................................................................................................29  3.2.4 Time Brackets for Performing Field Counts .............................................................................30  3.2.5 Counting Methods at Key Locations ..........................................................................................31  3.3 Determining Video Surveillance Feasibility.......................................................................................31  3.3.1 Video Verification Methods .........................................................................................................31  3.3.2 Verification Analysis ......................................................................................................................32  3.4 Analyzing and Visualizing Collected Data .........................................................................................32  3.4.1 Formatting.......................................................................................................................................32  3.4.2 Field Forms .....................................................................................................................................33  3.4.3 Pedestrian Modeling Techniques .................................................................................................33  3.4.4 Census Tracts..................................................................................................................................34  3.5 Publicizing Data.....................................................................................................................................34  3.5.1 Venipedia .........................................................................................................................................34  5


3.5.2 Deliverables.....................................................................................................................................34 3.5.3 Furthering Models..........................................................................................................................35  Results and Analysis ........................................................................................................................................36  Recommendations ...........................................................................................................................................37  Bibliography ......................................................................................................................................................38  Appendices........................................................................................................................................................40  Appendix 1: Pedestrian Agent Types Flow Chart ..................................................................................40  Appendix 2: Census Data Graphic ...........................................................................................................41  Appendix 3: GIS Cloud Map Layers.........................................................................................................41  3.1 Hotels Layer .......................................................................................................................................41  3.2 Schools Layer .....................................................................................................................................42  3.3 Museums Layer ..................................................................................................................................42  3.4 Churches Layer ..................................................................................................................................43  3.5 Tourist Sites Layer.............................................................................................................................44  Appendix 4: Database Form ......................................................................................................................45  Appendix 5: Field Forms ............................................................................................................................46  5.1 Venetian Field Form .........................................................................................................................46  5.2 Tourist Field Form ............................................................................................................................46  Appendix 6: Establishment Data Form ...................................................................................................48  Appendix 7: B Term Schedule ...................................................................................................................49  Appendix 8: Budget .....................................................................................................................................51 

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List of Figures Figure 1: Area of Study Map ..........................................................................................................................24 Figure 2 - Mechanical Counter.......................................................................................................................29  Figure 3: Flow Cart of Pedestrian Agent Types ..........................................................................................40  Figure 4: Hotel Locations in San Marco.......................................................................................................41  Figure 5: School Locations in Venice ...........................................................................................................42  Figure 6: Museum Locations in Venice ........................................................................................................42  Figure 7: Church Locations in Venice ..........................................................................................................43  Figure 8: Church Locations in San Marco ...................................................................................................43  Figure 9: Major Tourist Sites in Venice ........................................................................................................44  Figure 10: Mobility October Schedule ..........................................................................................................49  Figure 11: Mobility November Schedule......................................................................................................49  Figure 12: Mobility December Schedule ......................................................................................................50 

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List of Tables Table 1: Assumptions ......................................................................................................................................25 Table 2: Pedestrian Agent Type Characteristics ..........................................................................................28  Table 3: Time Brackets for Manual Counts .................................................................................................30  Table 4: On Site Manual Pedestrian Counting Template ..........................................................................33  Table 5: Video Surveillance Data Collection Template..............................................................................33  Table 6: Venetian Resident Density by Age and District (From 2001 Census Data) ............................41  Table 7: Venetian Field Form for Manual Counts ......................................................................................46  Table 8: Tourist Field Form for Manual Counts.........................................................................................46  Table 9: Form for Institution Information ..................................................................................................48 

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Executive Summary

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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 Venice’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 don’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

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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 city’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 water–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,

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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.

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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 8

became great by “accident of position .” 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 influx of population resulted in a profound change in social composition in the lagoon as settlements took form, and many noble family factions began to campaign for rule. After decades of political strife among various settlements vying for supremacy, the islands were placed under the authority of the Italian king Pippin in order to free the islands from Byzantine control10.

8(Morgan

1782) Cosgrove and Foot, Italy 2011) 10(Cessi, Cosgrove and Foot, Italy 2011) 9(Cessi,

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Comment [C1]: Opinion


As this power struggle was taking place, trade was rapidly developing and increased private wealth led to gradual internal stability. By the late 9th century, the group of Rialto islands was officially transformed into civitas Venetiarum, the city of Venice11. 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. The buildings had to be strategically placed,

Comment [C2]: Opinion

taking into account the special environmental conditions of Venice. Weight had to be properly distributed so that there were never too many areas of stress12. 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 of brick covered with mastic for adhesion13. The architecture and design possesses characteristics of permanence and timelessness that is unsurpassable.

Comment [C3]: 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 undeniably circumscribe the original islands, as well as suggest their position, the rest of the water area having been recovered by erecting walls composed of granite along the line of these canals, which laid the foundation for the buildings14. 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 wide15. The sides are lined with palaces and buildings reflecting the Gothic, Romanesque, and Renaissance grandeur from its early development.

11(Cessi,

Cosgrove and Foot, Italy 2011) Were Houses in Ancient Venice Designed and Why? n.d.) 13(How Were Houses in Ancient Venice Designed and Why? n.d.) 14(Morgan 1782) 15(Cessi, Cosgrove and Foot, Italy 2011) 12(How

14

Comment [C4]: Maybe make into a couple different sentences? It’s difficult to understand.


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 Venice16. They too are narrow, short, and crooked, and they penetrate every part of the city. Most of them are just passages about seven feet wide, with the widest of streets not more than twenty-five feet17. 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 underneath18. 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 city19. 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 Venice20.

2.3 MOBILITY IN VENICE The City of Venice was founded in the year 421 A.D. by Italian fishermen and inhabitants of Northern Italy seeking a safe-haven from barbarian attacks21. 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 city’s mobility22. 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. 2.3.1 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 transportation23. These boats are keel-less and used almost exclusively for tourism in this day and

16(Morgan

1782) 1782) 18(Morgan 1782) 19(Morgan 1782) 20(Morgan 1782) 21 (Howard and Quill 2002) 22 (Howard and Quill 2002) 23 (Cessi and Foot, Venice 2011) 17(Morgan

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Comment [C5]: This is in the first background section; don’t need to restate. Just introduce mobility


age24. 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.

Comment [C6]: Take out gondolas – only talk about traghetti and other public transportation

Venetian ferries, called traghetti, also are a more popular form of transportation in Venice, and there are now seven of these ferry crossings across the Grand Canal25. These ferries, which are much like gondolas by design, operate at certain points between bridges on the Grand Canal and shuttle pedestrians across for just 50 cents26. 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 27

became expert shipbuilders . 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

Comment [C7]: Take out Comment [C8]: History first? Before the types of boats?

connected Europe to other continents such as Asia through the use of the Mediterranean Sea28. Venice also had a very well equipped navy, which had the ability to build one war galley per day29. These galleys were handcrafted in shipyards called squeri where all types of traditional boats were crafted, including gondole.

Comment [C9]: Take out

2.3.2 Nautical Congestion

Comment [C10]: Take out. If there’s information that is pertinent to the project, put it in a different section

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 clients’ 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 interests30. These stops can cause a large amount of traffic and affect mobility. The traffic patterns of taxis and gondolas are difficult (Cessi and Foot, Venice 2011) (Drake 2008) 26 (Drake 2008) 27 (Davis and Marvin 2004) 28 (Davis and Marvin 2004) 29 (Davis and Marvin 2004) 30 (Chiu, Jagannath and Nodine 2002) 24 25

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to predict and their destinations are random, therefore their traffic patterns do not significantly influence overall mobility in Venice. Cargo boats, on the other hand, have routes that generally do not change and are predictable. Shipments of goods cause about 36 percent of all boat traffic in Venice31. Many of these shipments occur in the morning; this is because the purpose of most of the shipments is to restock supply and food stores32. In recent years, Interdisciplinary Qualifying Projects have been done by teams of Worcester Polytechnic Institute students analyzing and suggesting modifications to cargo shipment methods in order to decrease congestion33. Originally the cargo was shipped and grouped by type of goods, and required multiple boats to travel to the same locations. These teams proposed modifications which would make cargo shipments through the city cause less congestion. The proposal included having all cargo boats first report to a warehouse near the mainland, reorganize and coordinate the cargo based on destination, rather than by item34. This proposal proved to be successful, and resulted in a reduction of about 90 percent of cargo related traffic35. 2.3.3 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

(Fiorin and Miani 1995) (Fiorin and Miani 1995) 33 (Duffy 2001) 34 (Duffy 2001) 35 (C. Catanese, et al. 2008) 36 (Davis and Marvin 2004) 37 (Davis and Marvin 2004) 31 32

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perpetually since the1950’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. 2.3.4 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 wellknown bridges traverse the Grand Canal, the most notable of which is the Ponte di Rialto. 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.

2.2 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. 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. Mark’s square, is a popular tourist stop, where one can visit St. Mark’s (Van der Borg and Russo, Towards Sustainable Tourism in Venice 2001) (Davis and Marvin 2004) 40 (Contesso 2011) 41 (Contesso 2011) 42 (Contesso 2011) 43 (Contesso 2011) 44 (Riganti and Nijkamp 2008) 38 39

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Comment [C11]: What are the other three? Might as well name them.


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)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. 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

45(Carnevale

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

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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.

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 many years, 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. 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. Raised platforms 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 platforms are narrow and create a difficult passing situation. The cramped space makes the walking rate slow and creates pedestrian 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 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.

48(Davis

and Marvin 2004) et al. 1998) 50(Rameiri, et al. 1998) 49(Rameiri,

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Comment [C12]: 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 [C13]: I don’t know. Weird sentence structure and is it necessary?


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 [C14]: 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 environment51. 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 hotspot52. Though the model created was limited and 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 [C15]: 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 51 52

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

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was utilized included which canals boats entered from and returned to, the time of day, and each boat’s license plate number53. 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 [C16]: 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 [C17]: 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 [C18]:

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

Comment [C19]: 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.

53

(VeniceTable: Interactive Traffic Simulation Table 2010)

22


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.

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Comment [C20]: 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 occured 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 1: 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 24

Comment [C21]: 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. 25


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 days’ peak data set. If there was no significant difference, then weekday peaks are of the same magnitude. It is important to note that only being in Venice for 7 weeks made it impossible to collect 10-12 trials worth of data.

Comment [C22]: 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 days’ peak data set. If there was no significant difference, then weekend peaks are of the same magnitude. It is important to note that only being in Venice for 7 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, we could then specifically focus on the peak times in which field counts should be conducted, and not be concerned 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 for each bridge for each weekday fall in the same three-hour block. If this occurs, one can assume that the assumption is correct and

26


counts can 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

Our team proposed the assumption that of the six bridges connecting San Marco to the rest of historic Venice, not all of them 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 is proven, counts can be focused more on the bridges that are primarily used rather than the ones that are less frequently used. Given our team’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. 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 others, then the assumption was kept and field counts were conducted by prioritizing the primary bridges. This allowed our 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. If held true, it allows 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 has a negligible traffic flow, then data doesn’t need to be collected there at all. If a secondary bridge has a measureable percentage of traffic flow of a primary bridge, than 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.

27

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


3.2 QUANTIFYING PEDESTRIAN AGENTS To accomplish the project objectives, Team Mobility must effectively count pedestrians. To do this, we must take manual counts at key locations, identify between Venetians and tourists using identifying characteristics, and implement specific counting methods. 3.2.1 Focus Area and Key Counting Locations

The 2010 Venice Mobility team previously analyzed congestion in the San Marco district, so our team plans on expanding the data collected in San Marco by focusing on different locations within the district. We will also be considering key tourist locations and traghetto stops. The specific locations that will be analyzed for our project will be evaluated and determined upon arrival in Venice so we can gain first-hand knowledge of where the worst congestion locations are. 3.2.2 Assigning Agent Types and Identifying Each Type’s Characteristics

Following in suit with previous years’ methodologies, the 2011 Mobility Team will be maintain the same agent types. This will provide congruency in the data sets from different years, which will allow for the possibility of combining multiple data streams in models produced in future endeavors. Pedestrian traffic agents will continue to be categorized under tourist and local typesets. Locals have been found to move faster on average as well as follow more direct paths. They also tend to walk alone or in smaller groups. Tourist agent types have been shown in past data sets to move slower and in larger groups54. The summary of type characteristics can be seen in Table 1. Table 2: Pedestrian Agent Type Characteristics

Tourist Has camera in hand or is taking pictures Has map Looks lost, refers to street/bridge signs to orient self Looks up at scenery

Venetian Lacks clear tourist indicators Travels quickly and directly across bridge May stop and greet other pedestrians, usually in Italian, indicating residence May have stroller, dog, shopping cart

Each type has provided a unique element to pedestrian traffic in historical Venice. Much of the locally caused traffic stems from commuting between primarily residential zones and primarily commercial and business zones. Tourist traffic, on the other hand, tends to focus on specific sites and has daily peaks and drops. These tourist attractors, as well as the local start and end locations, 54

(C. Catanese, et al. 2008)

28


can be referred to as sources and sinks when translating our data for model production. Our project’s area of study will focus in a few of these identified sources and sinks. We will be utilizing existing maps to determine where residential zones are, as well as hotels, schools, and other hotspot locations. Also, there are existing surveillance videos which can contribute additional data via manual counts. Visual determination of agents while collecting data on site will be done using the visual recognition methodology from last year’s mobility team. According to their research, their agent determining methodology was found to be statistically accurate and therefore there is no need to recreate another methodology when there is already an effective one in use. The established methodology is based largely on visual markers such as behavior and clothing. 3.2.3 Counting Tools and Devices

In order to accurately quantify the flux of pedestrians at bottleneck locations we will utilize two types of counting methods which will allow us to quickly count a large number of pedestrians. Firstly, we will pair off into teams of two and utilize handheld mechanical counters. Each person in the team will focus on pedestrians moving in a certain direction and distinguish between Venetians and Tourists based on behavior and walking speed. Each team member will have a watch to keep track of time, a counter in each hand to count the two types of pedestrians, and a notebook to store all of our collected data.

Figure 2 - Mechanical Counter

The second method for data collection will involve a TI Calculator program which will allow for the simultaneous counting of two agent types, each in two directions. There are a few benefits to using this calculator program over manually counting with the clickers. Some of these benefits include the ability to have just one team member work at each key location, the ability to do counts at twice as many locations simultaneously, and also have data in a digital form which is already organized into tables. There are also benefits to using to using the manual counters as well. By using the mechanical counters we will have no data loss from unforeseen calculator problems. Some of these problems may occur due to hardware limitations. For example, the TI-84+ series calculator has about 24kB of usable RAM, and only a 15 MHz processor. These hardware specifications may cause delays in data 29


acquisition and retention after a large number of pedestrians have been counted. Both methods will be used and the TI Calculator program’s feasibility will be determined. 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. Venice will experience more traffic in the morning due to Venetians going to work or school and tourists will be embarking towards their tourist destinations. We will also assume that Venetians will leave their homes much earlier in the morning than tourists and thus compose much of the mobility streams throughout the city. Tourists moving about Venice will dominate the afternoon hours, while Venetians remain in their place of work or school. In addition to agent behavior being unique at different times of day, we expect them to be different on each day of the week. For example, the weekdays will consist of many Venetians traveling to work or school, but those Venetians will probably have a more leisurely destination on Saturday, when they have time off from work. On Sundays, Venetians are very likely to be heading to church in the morning. For these reasons, it is necessary to bracket time intervals that appear to have homogenous traffic streams. In order to provide consistency with the data collected from the VE10 team, we will continue to employ the same time brackets and collect counts in the same fifteen minute time intervals. The following table shows the time brackets that we intend to use to collect manual counts for a given day: 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

The previous year’s results demonstrate relatively stable results within each time bracket. Therefore, we will proceed under the same time constraints, conducting manual counts at different locations 30


and on different days of the week to collect as much data on volume and behavioral flow as possible in a seven-week time span. 3.2.5 Counting Methods at Key Locations

By holding one mechanical clicker in each hand, we are able to collect two types of data simultaneously: the number of tourists crossing and the number of Venetians crossing in one direction. In order to determine the bi-directional flux of pedestrians across a bridge, we will require teams of two at each location. The method for counting pedestrians includes each of the two team members standing at the top of the bridge, facing in opposite directions, and record one click using the appropriate hand to count the number of individuals crossing the bridge. These counts will be performed for 15 minute sessions, and will be recorded at the end of each session. If the weather is poor (raining, flooding), the Mobility team will not be conducting manual counts in order to avoid discrepancies in the data. We will count only during ideal conditions, which will provide us with the most accurate pedestrian traffic information.

3.3 DETERMINING VIDEO SURVEILLANCE FEASIBILITY Using video surveillance technology will allow for manual counts to be collected without a person having to be on site. Our team will conduct counts using video clips recorded during the on-field manual counts and compare the two datasets to determine if using cameras as a means for collecting data is a practical method. 3.3.1 Video Verification Methods

To determine the feasibility of using a video surveillance system as a pedestrian tabulator and data collector for a pedestrian model, our team will employ manual counts. When a field count location is determined, two video cameras will be set up with a view of all pedestrians passing through the location, with one camera facing each direction. While the cameras are recording, our team will conduct manual counts for the predetermined time brackets. Once the field counts are complete, the video feed will be reviewed by the team and the pedestrians will be recounted to determine the accuracy of the video counting method. If the reviewed camera feed counts and the team’s manual counts are statistically similar, then the video method is feasible. If the counts are dissimilar, another method should be employed.

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3.3.2 Verification Analysis

Once the video is recorded and the field count time interval is completed, the data will be analyzed. Each team of two will watch the other team’s direction to ensure that the video counting methodology is accurate. For example, if pair A is taking field counts for direction A and pair B is taking counts for direction B, then pair A will watch the video from direction B and pair B will watch that of direction A. This will provide a fresh viewpoint for every feed and an accurate method for data analysis. If the manual field counts are considered the “actual” counts and the counts from the video feed are considered “experimental” counts, then we will be able to calculate the percent error between the actual and experimental data, therefore determining the feasibility of the video method.

3.4 ANALYZING AND VISUALIZING COLLECTED DATA We will be using field forms and other data collection forms to properly format our pedestrian data to accommodate RedFish Group’s modeling preferences. We will also implement 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 must come up with a usable format for tabulating data for the programming capabilities of our collaborators. However, we must also take into account the visual limitations of the counters on field when collecting large amounts of data at once. It is 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 is anticipated to have unusually high traffic volumes, the decision will be made as to whether or not more than two counters should be stationed to that location. Additionally, to verify the efficiency of our model and the accuracy of our on location counts, we will use the same form for our video recording counts. The counts made by each individual would then be collaborated at the end of the time bracket and collected in excel spreadsheets to be submitted to our collaborators and integrated into the

32


pedestrian model. This data will also be 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 will be 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 will be used to collect counts through video clips. This field form will then be used to tabulate data in a form suitable for our collaborators to integrate into an agent-based model. Table 3 shows the columns that will be 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 will use a survey guideline in the field. Key information from these sites would be attendance and hours of operation. If we knew the capacity of specific establishments, we could better model agent interaction with the environment. The information collected will then be 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 lacks the experience to create a NetLogo model based on the data collected, the data will feed models created by the RedFish Group and other organizations. Aside from a working model, the data will also work into several GIS cloud layers. The manual counts will be able to show tourist: Venetian concentrations at collection points and will also allow us to create 33


a ‘heat map’ that shows population density at certain points in time. Once these are overlaid on the GIS map, they can be 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) will show 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 will complement Venetian data that our team collects 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. These different age brackets will help 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 amount 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 is collected, analyzed, and formatted using the techniques outlined above, we will publish 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 will contribute to Venipedia by creating new pages concerning the end results of the project. The new pages will cover our organized data of the main research topics and the visual aids we create. 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 will be an interactive layered GIS cloud map of the city of Venice, with different “layers,” or data sets, that can be displayed on or hidden from the map. The layers will consist of agent type and direction of travel, beginning and ending locations, 34


schools and places of employment, residential and commercial zones, tourist hotspots, hotels, traghetti stops, and other key locations. Ideally, this visual aid will allow the public to see the relationship between agent types and 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 is to collect and format data in such a way as to further the development of agent based modeling systems. We will do this by complying with the correct data format for the models as specified by RedFish Group. We must compile all of our data, sort it into the specific format, and edit it to include the correct dataset for RedFish’s purpose. Ultimately, this will enable the company to develop a model for pedestrian congestion, taking into account traffic flow and congested locations.

35


Results and Analysis

36


Recommendations

37


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. 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. 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 Venice’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.

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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. 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). 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.

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Appendices APPENDIX 1: PEDESTRIAN AGENT TYPES FLOW CHART

Figure 3: Flow Cart of Pedestrian Agent Types

40


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 4: Hotel Locations in San Marco

41


3.2 Schools Layer

Figure 5: School Locations in Venice 3.3 Museums Layer

Figure 6: Museum Locations in Venice

42


3.4 Churches Layer

Figure 7: Church Locations in Venice

Figure 8: Church Locations in San Marco

43


3.5 Tourist Sites Layer

Figure 9: Major Tourist Sites in Venice

44


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

45

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 46

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

47


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

Date

Time

Establishment ID

Location

48

Estimated Attendance

Capacity

Hours of Operation


APPENDIX 7: B TERM SCHEDULE

Figure 10: Mobility October Schedule

Figure 11: Mobility November Schedule

49


Figure 12: Mobility December Schedule

50


APPENDIX 8: BUDGET Team Mobility Budget – Fall Semester 2011 Item Manual Clickers Binder Clipboards

Price/Item $5.00 $12.00 $4.00

Quantity 10 1 4

51

Total Price $50.00 $12.00 $16.00 $83.00

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

Mobility_B11_Report_Draft1  

An Interactive Qualifying Project report submitted to the faculty of WORCESTER POLYTECHNIC INSTITUTE in partial fulfillment of the requireme...

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