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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

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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..................................................................................................................................................... 8  List of Tables ...................................................................................................................................................... 9  Executive Summary .........................................................................................................................................10  Pedestrian Traffic Studies ...........................................................................................................................10  Autonomous Agent Computer Model .....................................................................................................10  Conclusions ..................................................................................................................................................11  Introduction ......................................................................................................................................................12  Background .......................................................................................................................................................17  2.1 The Architectural Framework of Venice ...........................................................................................17  2.1.1 Origins of the City .........................................................................................................................17  2.1.2 Design of the City ..........................................................................................................................18  2.1.3 The Canals .......................................................................................................................................19  2.1.4 The Streets ......................................................................................................................................19  2.2 Mobility in Venice .................................................................................................................................20  2.2.1 Watercraft in Venice ......................................................................................................................20  2.2.2 Water-Based Public Transportation ............................................................................................21  2.2.3 Pedestrian Mobility ........................................................................................................................22  2.2.4 Venetian Bridges ............................................................................................................................22  2.3 Tourism in Venice .................................................................................................................................23  2.3.1 Popular Tourist Sites and Events ................................................................................................23 

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2.3.2 Magnitude of Tourists ...................................................................................................................24  2.4 Environmental Impacts on Mobility ..................................................................................................24  2.4.1 Acqua Alta .......................................................................................................................................25  2.4.2 Canal Wall Damage........................................................................................................................25  2.5 Venetian Traffic Models .......................................................................................................................26  2.5.1 Past Models .....................................................................................................................................26  2.5.2 Modeling Tools ..............................................................................................................................27  2.5.3 How Models Read Data ................................................................................................................27  Methodology .....................................................................................................................................................29  3.1 Determining Constants.........................................................................................................................30  3.1.1 There are Peak Times ....................................................................................................................31  3.1.2 Weekday Peaks are of Similar Magnitude ...................................................................................31  3.1.3 Weekend Peaks are of Similar Magnitude ..................................................................................32  3.1.4 Peak Times are Consistent Day to Day ......................................................................................32  3.1.5 Specific Bridges Carry the Majority of Traffic Flow .................................................................32  3.1.6 Secondary Bridges Carry an Insignificant Traffic Flow OR Carry a Predictable Percentage of Primary Bridge or Total Traffic Flow ..............................................................................................33  3.2 Quantifying Pedestrian Agents ............................................................................................................33  3.2.1 Focus Area and Key Counting Locations ..................................................................................33  Map of the Ten Counting Locations Used by the B’10 Team ..................................................................34  Table : Bridges and Traghetto Stops in the Study Area ................................................................................34  3.2.2 Distinguishing Between Agent Types .........................................................................................35  3.2.3 Counting Method ...........................................................................................................................36  Figure : Example of Counting Based on Direction on Bridge 6 ..............................................................37  3.2.4 Field Forms .....................................................................................................................................38  3.2.5 Schedule for Performing Field Counts .......................................................................................39  5


Table : Schedule for Bridge Counts ..............................................................................................................39  3.3 Determining Video Surveillance Feasibility.......................................................................................40  3.3.1 Collecting Proof of Concept Sample Video Footage ...............................................................40  3.3.2 Statistical Comparison of Manual Counting Methods..............................................................40  3.3.3 Camera Set Up ................................................................................................................................40  3.3.4 Filming Scenarios ...........................................................................................................................41  3.3.5 Collecting Control Data Set for Future Software Verification................................................41  3.3.6 Verifying Software with Control Data: Experimental Design.................................................42  3.3.7 Qualitative Video Data Collection...............................................................................................43  3.4 Analyzing and Visualizing Collected Data .........................................................................................44  3.4.1 Nodular Formatting .......................................................................................................................44  3.4.2 Rules of Attraction .........................................................................................................................45  3.4.3 Census Tracts and Statistical Data ...............................................................................................46  3.5 Publicizing Data.....................................................................................................................................46  3.5.1 Deliverables.....................................................................................................................................47  3.5.2 Furthering a Pedestrian Model .....................................................................................................48  Results and Analysis ........................................................................................................................................49  Recommendations ...........................................................................................................................................50  Bibliography ......................................................................................................................................................51  Appendices........................................................................................................................................................54  Appendix 1: Pedestrian Agent Types Flow Chart ..................................................................................54  Appendix 2: Census Data Graphic ...........................................................................................................55  Appendix 3: GIS Cloud Map Layers.........................................................................................................55  3.1 Hotels Layer .......................................................................................................................................55  3.2 Schools Layer .....................................................................................................................................56  3.3 Museums Layer ..................................................................................................................................56  6


3.4 Churches Layer ..................................................................................................................................57  3.5 Tourist Sites Layer.............................................................................................................................58  Appendix 4: Database Form ......................................................................................................................59  Appendix 5: Field Forms ............................................................................................................................60  5.1 Venetian Field Form .........................................................................................................................60  5.2 Tourist Field Form............................................................................................................................60  Appendix 6: Establishment Data Form ...................................................................................................62  Appendix 7: B Term Schedule ...................................................................................................................63  Appendix 8: Budget .....................................................................................................................................65 

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List of Figures Figure 1: A Map of the Framework of Venice ............................................................................................13  Figure 2: A Map of the ACTV Routes for Public Transportation ...........................................................14  Figure 3: Weather conditions greatly impair traffic flow in Venice..........................................................15  Figure 4: Bridges are key bottleneck locations for collecting traffic data ................................................16  Figure 5: St. Mark’s Basilica ............................................................................................................................17  Figure 6: A Canal Near the Arsenale ..............................................................................................................19  Figure 7: A Standard Street in Venice ...........................................................................................................20  Figure 8: Area of Study Map ..........................................................................................................................30  Figure 9: Frame from time lapse camera taken at Bridge 6 on November 15th ....................................43  Figure 10: Current ARGOS camera placements courtesy of the Commune di Venezia ......................44  Figure 1 ..............................................................................................................................................................45  Figure 2 ..............................................................................................................................................................46  Figure 11: Sources and Sinks ..........................................................................................................................48  Figure 12: Flow Cart of Pedestrian Agent Types ........................................................................................54  Figure 13: Hotel Locations in San Marco ....................................................................................................55  Figure 14: School Locations in Venice .........................................................................................................56  Figure 15: Museum Locations in Venice ......................................................................................................56  Figure 16: Church Locations in Venice ........................................................................................................57  Figure 17: Church Locations in San Marco .................................................................................................57  Figure 18: Major Tourist Sites in Venice ......................................................................................................58  Figure 19: Mobility October Schedule ..........................................................................................................63  Figure 20: Mobility November Schedule......................................................................................................63  Figure 21: Mobility December Schedule ......................................................................................................64 

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List of Tables Table 1: Assumptions ......................................................................................................................................30  Table 4: On Site Manual Pedestrian Counting Template ..........................................................................38  Table 5: Video Surveillance Data Collection Template..............................................................................38  Table 6: Venetian Resident Density by Age and District (From 2001 Census Data) ............................55  Table 7: Venetian Field Form for Manual Counts ......................................................................................60  Table 8: Tourist Field Form for Manual Counts.........................................................................................60  Table 9: Form for Institution Information ..................................................................................................62 

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Executive Summary Venice, Italy is a city composed of islands that are separated by canals and connected by bridges. It can only be traversed by means of boat travel or on foot. These two modes of transportation are greatly independent, but coincide at public transportation boat stops. The main focus of this project was pedestrian traffic, which is often impeded by narrow walkways, bridges, street vendors, and public tourist attractions. These factors can impede movement and contribute to congestion. The maximum carrying capacity reported for Venice has been determined to be approximately 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 occasionally outnumbers the local population by more than double. Consequently, many residents relocate to the mainland to escape the high volumes of tourists. The overwhelming amount of pedestrians that flow in and out of the city creates a need for a better understanding of traffic flow and efforts towards improving mobility efficiency within the city’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

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CONCLUSIONS

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Introduction Cities worldwide adopt a negative reputation for their mobility issues. Many travelers avoid city traffic to save time on their trip, and those who cannot avoid traveling through cities must plan ahead accordingly. Mobility is the freedom to move about, and when mobility is impeded, people are forced to interrupt their routes and pace and accommodate for their lost time. 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. This creates a need for traffic congestion solutions that specifically apply to Venice’s unique situation.

1(Traffic 2(Lopez

Congestion Factoids 2009) 2006)

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Figure 1: A Map of the Framework of Venice

Built on a lagoon and properly titled Laguna Veneta, Venice is made up of 117 small islands with 150 canals and 409 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. 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

3(Centre

2010) 1782) 5(Italy n.d.) 6(Amilcar, et al. 2009) 4(Morgan

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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, as seen in Figure 2. There are also multiple surveillance systems in place, including the Automatic and Remote Grand Canal Observation System (ARGOS), Hydra, and Security and Facility Expertise (SaFE). These observational systems are used to implement speed limit laws, and monitor pedestrians and boats for crime. The 2011 Venice Mobility sponsors have developed these systems and implement them daily in Venice. ARGOS gives the vigili urbani (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, the 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.

Figure 2: A Map of the ACTV Routes for Public Transportation

7(Bloisi,

et al. 2009)

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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 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 does not take into account how peak tourist times, weather, seasons, events, times of the day, and other aspects affect pedestrian counts.

Figure 3: Weather conditions greatly impair traffic flow in Venice

An efficient, comprehensive model would be one that contains sufficient amount of data from yearround. 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.

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Figure 4: Bridges are key bottleneck locations for collecting traffic data

This gap in data collection is where the Venice 2011 Mobility team comes into play. A specific methodology has been established that can be executed by future traffic improvement teams, and pedestrian traffic data has been collected in Venice with a distinction between agent types, namely Venetians and tourists, at key bottleneck locations around the San Marco district. This data will be integrated into a computer model designed by Team Mobility’s collaborators as well as archived for use by future project teams. Through analytical processes, suggestions were made for furthering the efficiency of the pedestrian agent computer model. Another issue is that there is so much data on traffic in Venice, but there is no centralized location where all of this data is archived. To solve this concern, Team Mobility has collected all imperative census data and counting data and placed it in easily accessed sources to facilitate the continuation of the creation of a 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 became great by “accident of position8.” The city began as a collection of inhospitable

Figure 5: St. Mark’s Basilica

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)

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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 Byzantine Empire. It was not until the collapse of the empire in 751 that independence was accelerated. While Venice was dealing with political strife and continuous turnovers of power, it was also taking advantage of the opportunities offered by the sea and commerce. Trades passing through the city included dyes, leathers, spices, and many other goods. The lagoon province was the bridge between the European west, and the Islamic and Byzantine territories in the east. By the first half of the sixteenth century, Venice was the “great metropolis” that it is well-known for today. It hosted a variety of activities, trade continued on a large scale, and people came from all over the world. 2.1.2 Design of the City

What once was a group of islands with wooden houses resting on poles staked into unstable clay soil gradually morphed into an elegant and romantic city of stone. The buildings had to be strategically placed, taking into account the special environmental conditions of Venice. Weight had to be properly distributed so that there were never too many areas of 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.)

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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. 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 6: 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

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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 7: 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 city’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

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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 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 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

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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 the1950’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 dell’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

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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. Mark’s square, is a popular tourist stop, where one can visit St. Mark’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.

37

(Riganti and Nijkamp 2008) di Venezia 2012 2009)

38(Carnevale

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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

24


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

25


parts of the walkways. This causes backups down the walkways and has an overall negative effect on congestion.

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

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

26


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. 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 time, expertise, and data. The implementation of an autonomous data collection system will allow the collection of data with minimal human interaction. There are several tools present that can make this type of continuous autonomous data collection a possibility. One of those tools is Open CV, which is a software approach that uses video to autonomously recognize, track, and record traffic and distinguish physical differences, as well as velocity. 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 the environment, Venice, developed in the model. The environment itself is made up of two main 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

49

(VeniceTable: Interactive Traffic Simulation Table 2010)

27


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. 28


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 pedestrian traffic at key locations 2. To provide sample footage of pedestrian traffic to be used in a proof of concept. This will determine the feasibility of using camera surveillance systems to collect pedestrian traffic data in future years. 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 This project focused on pedestrian movement throughout the district of San Marco in Venice, Italy. The 2010 Mobility team collected data at select bridges in that area, and this year’s project expanded upon the collection locations. Data was collected at the six bridges that span the San Luca, del Barcaroli, and San Moisè canals, as well as at the Ponte dell’Accademia. A methodology was developed for accurately counting pedestrians and the feasibility of using video feeds to count pedestrians was investigated. The data that was collected was integrated into an agent-based model developed by the team’s collaborators. Using real time pedestrian counts ensures that the walkers in the model will have appropriate timing and destinations. Employing the methodologies that have been developed during this project in future years for the other five districts of Venice will ensure a complete understanding of pedestrian movement in the city. 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.

29


Figure 8: Area of Study Map

3.1 DETERMINING CONSTANTS 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 traffic flow. In order to develop a simple counting methodology that can be easily repeated while maintaining efficacy, a wide spectrum of data was initially collected to determine if there were any constants in pedestrian traffic data. Based on these constants, several assumptions about pedestrian traffic in Venice were made to allow for an easier and more consistent counting methodology. Upon review of the data and constants, the following assumptions were made (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 environmental constants were also developed for each of these experiments to help ensure accuracy:

30


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

Counts were 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. The resulting peak times can be viewed in Appendix ##. 3.1.2 Weekday Peaks are of Similar Magnitude

Assuming that weekday peaks are of equivalent magnitude allowed those conducting counts to collect during one peak-time block over a week (Monday through Friday) instead of every weekday. In other words, this assumption states that there is a similar amount of pedestrian traffic throughout each day of the week. 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 seven weeks made it impossible to collect 10-12 trials worth of data.

31


3.1.3 Weekend Peaks are of Similar Magnitude

Assuming that weekend peaks are of equivalent 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 throughout the day Saturday is similar to traffic on Sunday. 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 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, Team Mobility 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 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 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 32


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.

3.2 QUANTIFYING PEDESTRIAN AGENTS To accomplish the project objectives, Team Mobility counted pedestrians at key locations in the area of study. This counting data was then collected and integrated into a computer model for traffic analysis. To do this, we developed a specific counting method to conduct manual counts based on direction of flow and pedestrian type at key connection points around San Marco. This counting method is meant to continue to be used by future teams in order to ensure consistent data sets. 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, as seen in Figure ##. As the succeeding team, 2011 Mobility expanded to different counting locations, also known as nodes for the purpose of the computer model, within the San Marco district. We also determined that we wanted to narrow our focus to fewer strategically placed bridges in the San Marco district in order to provide critical data for the foundations of the computer model.

33


Map of the Ten Counting Locations Used by the B’10 Team 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 into 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. These eleven counting locations covered all locations for pedestrians on foot into and out of the western half of the San Marco district, allowing for a clear starting point for the computer model. The complete list of bridges and traghetto stops are referenced in Table ##, and the map of each of these is seen in Figure ##. Table : 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

34


Google Map of Traghetto Locations and Bridges Locations. Blue Anchors Symbolize Traghetti Stops and Red and Yellow Marker Pairs Symbolize Bridge Locations 3.2.2 Distinguishing Between Agent Types

A useful feature of the pedestrian model is the distinction between pedestrian agent types, such as Venetians and tourists. Each type of pedestrian behaves differently. Locals will leave their house to go to the market or work. They typically know where they are going, and are usually on a schedule. Tourists will wander the streets with an idea of where they would like to go, but may stop in a shop or look at a sight along the way. Their movements are less structured. In order to reflect this different behavior in the agent-based computer model it important to collect data based on type of pedestrian type. Distinction was based mainly on visual cues determined by the individual conducting the count. As previously mentioned, Venetians had more of a direct route, so their pace was steadier, while tourists had more of a random behavior. Tourists can also be singled out by whether or not they are holding cameras, or if they are in tourist groups led by a guide. Venetians will have pets with them, or pull dollies. Businessmen and women or employees will be in business attire, and tourists will wear more leisurely clothes. A complete list of the classifications used is in Table ##. 35


Tourists “Wandering” walking pattern Carries a camera or takes pictures Led by a tour guide Speak in another language Window shop Looking at a Map

Venetians More direct walking pattern Business or uniform attire Briefcase or cart Walking a pet

At each bridge, three project members counted tourists while one project member counted total flow for 15-minute intervals for two-hour blocks during the peak volume time. This data was recorded in field forms and averages were taken of the three tourist counts. A percentage of tourist attendance at each bridge was calculated. These percentages were applied to the computer model along with the rest of the bridge data collected by the 2011 Mobility Team and can be seen in Table/Figure ##. 3.2.3 Counting Method

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

36


Figure : Example of Counting Based on Direction on Bridge 6 At the end of each fifteen minute interval, the number read on the clicker was recorded into a field form (see Appendix ##) which was later placed into spreadsheets to be submitted for integration into the computer model. If flow at the peak time was determined to be too heavy for one individual to count, then two individuals were stationed at that location and each individual counted only one direction of flow. This ensured the accuracy of the data collected. The same method was used for counting at traghetti stops. A clicker in each hand representeds the direction of traffic boarding a boat or leaving a boat. The time and count number is was recorded each time a boat dockeds and departsed. The field form for traghetti counts can be viewed in Appendix ##. 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 37


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. 3.2.4 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 2: 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 3: Video Surveillance Data Collection Template

Date

Time

Establishment ID

Location

38

Estimated Attendance

Capacity

Hours of Operation


3.2.5 Schedule for Performing Field Counts

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

Weekday 15:30 – 18:30 15:30 – 18:30 15:30 – 18:30

Weekend 15:30 – 18:30 15:30 – 18:30 15:30 – 18:30

Along with a time bracket for bridge counts, traghetti stops ran on strict operation schedules. This allowed for data to be collected for the entire span of the time. Table ## shows the schedule for each traghetti stop. Table : Schedule for Traghetto Stops Traghetti Stop Riva del Carbon – Fondamente del Vin Sant’Angelo – San Toma San Samuele – Ca’ Rezzonico Campo del Traghetto – Calle Lanza

Monday – Saturday 8:00 – 13:00 7:30 – 20:00 8:30 – 13:30 9:00 – 18:00

Sunday 8:00 – 13:00 8:30 – 19:30 Closed 9:00 – 18:00

The Campo del Traghetto to Calle Lanza traghetto was closed for work while the team was taking counts, however, so there is no data for that stop.

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3.3 DETERMINING VIDEO SURVEILLANCE FEASIBILITY 3.3.1 Collecting Proof of Concept Sample Video Footage

Looking to provide as accurate a traffic model as possible for the city of Venice, a proof of concept was developed in order to test the feasibility of such a system in Venice. The goal of the proof of concept for the Mobility Team was to provide a variety of video feed samples that represent the complexity and variety of pedestrian traffic in Venice at key counting locations that were established in the Study Area Map. These video samples provided an appropriate and comprehensive data set for the traffic counting software which, once developed, can be used to test a final proof of concept. The variety of video feeds, once paired with the software, will provide insight into the camera orientations necessary for the best software based data collection. 3.3.2 Statistical Comparison of Manual Counting Methods

Said samples also gave a means of remotely counting and verifying field counts with video based manual counts. Counts were conducted using the video by analyzing the sample frame by frame. This process was conducted to give us what could qualitatively be considered the most accurate count given that as much time could be taken to count pedestrians traffic. This eliminates the requirement for instant analysis that occurs in field counts. The manual video counts were then statistically compared to the manual field counts to determine how precise the two counting methods are. The resulting statistical analysis showed that there is no significant difference between the video manual counting and the in-field manual counts. This statistical analysis can also be conducted with counting software once developed. If all three counting methods are statistically similar then they collectively provide a precise data set. 3.3.3 Camera Set Up

To collect video samples at bridges, a GoProTM HD Hero Camera was attached to a 12 foot rigging boom measured from the base of the rigging apparatus (rig). The rig was then securely fixed to the side of the bridge being counted using an industrial grade strap. Additional lashings were tied using 1/8 inch rigging lied to provide stability and ensure structure integrity throughout the recording process. The rig was attached in such a spot so that would not impede traffic but would still provide a bird’s eye view of the traffic ‘choke-point’.

These choke-points directly correspond to the

assigned nodes on the Study Area map. The camera lens was aimed parallel to traffic flow with the ‘r4’ video resolution mode set on the HD Hero camera. This setting provided the most vertical viewing area with the maximum overall view. The video was collected in HD 960p resolution. 40


Just like each bridge is unique, each camera set up is different to adapt to the environmental variance. This provided us with a variety of camera angles and camera orientations in the video streams. 3.3.4 Filming Scenarios

The video sample feeds collected were each fifteen minutes in length to provide continuity among pedestrian traffic data collected. The feeds covered a variety of scenarios often seen in Venice so that the proof of concept could reflect the range of possible scenarios that might be present in a permanent software based video traffic counting system.

The variety of camera feeds also

demonstrates several different camera angles and orientations. The various orientations serve to provide a means to determine which of said orientations provide a viable frame of reference for software and video based manual counts. Below are 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) – Low Volume of Traffic Poor Weather (Overcast) – High Volume of Traffic Night Time – Low Volume of Traffic Night Time – High Volume of Traffic

3.3.5 Collecting Control Data Set for Future Software Verification

The development of a video traffic counting software system is a fairly complex task especially to create an effective software system during the IQP process given the resources and time constraints at hand.

Being that these software based systems have shown much potential thus far, the

development of such a system for use in Venice is likely in the future. The development of such a system may not occur ‘on site’ in Venice. Once the software is developed, it will still require the video feeds collected to show effectiveness in a Venice scenario. On top of the video feeds, the final proof of concept will also require a control set of data to compare the software counts to. This control data will statistically be taken as the actual count of pedestrians at the node pairs and the software counts will provide the experimental data sets. This will then allow for percent error calculations which can statistically determine the effectiveness of the software counting system. The control data sets were collected physically during the collection of the video feeds using the same counting methods developed for manual data collection. This provides each fifteen minute 41


video feed with a corresponding fifteen minute manual count of pedestrian traffic. The forementioned manual counts will provide data for traffic flow in both directions across the node pairs. 3.3.6 Verifying Software with Control Data: Experimental Design

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

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

Experimental Procedure 1. Run software with first video sample footage 2. Record the total pedestrian flow, the flow in direction ‘A’ across the node pair, and the flow in direction ‘B’ across the node pair 3. Repeat steps ‘1’ and ‘2’ 24 more times 4. Repeat steps ‘1’ through ‘3’ for each video sample Analysis: Calculate percent error and use standard deviation and a t-test to determine if the experimental results are statistically similar to the control data. Use a level of significance (alpha) of .05. 42


3.3.7 Qualitative Video Data Collection

Traffic by its complex nature is qualitative. In order to translate information about traffic to a format readable by the computer model much of the data collected as been quantitative. Recognizing the ability of qualitative data to communicate the realities of mobility in Venice to a large audience, effort was put forth to collect such data. 3.3.7.1 The Duality of Data

The video samples feeds that were collected primarily to develop a quantitative statistical comparison between manual field counts, manual video counts, and software video counts also serve another purpose. When pieced together, sped up, and converted to look more like a time lapse these clips provide an easy way to visually analyze traffic flow and an intuitive way to show traffic density. 3.3.7.2 Frame by Frame

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

Figure 9: Frame from time lapse camera taken at Bridge 6 on November 15th 3.3.7.3 A Comprehensive Network

Currently there are a variety of camera and video systems throughout Venice that are owned and operated by a number of different government organizations. Even without implementing counting software into these camera systems, they can still provide useful qualitative data sets, and helpful visuals for determining key congestion areas. 43


Figure 10: Current ARGOS camera placements courtesy of the Commune di Venezia

3.4 ANALYZING AND VISUALIZING COLLECTED DATA In order to provide a streamlined method of data acquisition during pedestrian counts, all data was recorded on prescribed forms. Following this procedure, all data was reorganized into Database Forms in a format which would be cohesive to the programming of the pedestrian model. In order to accommodate RedFish Group’s modeling preferences; the data was put into terms of nodes, which exist for both sources of pedestrian flow, as well as attractors, or pedestrian destinations. In addition, information was acquired from various alternate sources including field counts from past years, as well as the Census Statistics Office. 3.4.1 Nodular Formatting

To ensure that the agent-based model is performing as anticipated, our team came up with a usable format for tabulating the collected data for the programming requirements of our collaborators. Nodes, or location based entities, were created on our study area map, based on nodes already in existence due to government studies which occurred in the past two decades, at all important places of study. These nodes would aid in the directional flow of pedestrian traffic within the model, creating constrictions on how many pedestrians will travel from one location to another. In addition, these nodes were placed on either side of the bridges of focus to signify the directional flow of pedestrians traversing the bridge. For example, a pedestrian crossing Bridge 6, could cross from node L on one side of the bridge, to node K, and head towards the Ponte dell’Accademia, for visa versa for the direction of the Square of San Marco. For locations such as residential areas, places of 44


work, schools, and areas which to tend to attract short-term visitors, nodes were also created because these are pertinent to the creation of the most accurate model possible. Much of the information regarding these sources and attractors was based off of data received from the Census Statistics Office. 3.4.2 Rules of Attraction

For the purpose of programming, the most useful format for the data within these spreadsheets was to leave the data in its rawest form, as the counts themselves, in addition to determining the ratio of local and visiting population which frequent these nodes daily. The model would originally contain random walkers which were then constricted by different rules for each agent type. These rules contain nodular attraction, which, based on a probability would draw or repel pedestrians. In addition, rules were added to create the chronologic effect of a typical day within the city, including having pedestrians wake up at various times in the morning at their source node, travel to their respective destinations throughout the day, and end at the same source at various times within the evening. For example, the average 40 year-old Venetian will awake early in the morning and take a direct route to his/her place of labor, spend time there until travelling home, where they may run errands and stop at markets, or other stores on their way back to their residence. Tourists would likely behave much differently, starting their day later, either at an entrance to the city of Venice or a lodging facility, and travel for much of the day, wandering between various sites, and finally returning to their point of origin. Obviously, each location node would have a different attractive force on each of the two agent types. In order to ensure an accurate number of people, and which type of pedestrian, arrive at each destination in the model, the attractor’s probability was determined based on a ratio how many people had already arrived versus the number of people which frequent that destination, this ratio would be constantly changing throughout the day. This ratio would exist simultaneously for each agent. For modeling purposes, each node would then require a few parameters, defined as daily venetians, daily tourists, venetians arrived, and tourists arrived; furthermore, the two ratios would also exist as follows: 1

Figure 11

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1

Figure 12

These equations explain how, for the purpose of the model, the relationship between the attractive strength of a node has a negative correlation to the arrival of pedestrians, and that the end of the day will occur when the number of pedestrians who have arrived is equal to the number of daily pedestrians at all location nodes. In order to implement all the data collected during field counts, which lead to the development of the daily pedestrian statistics, Excel spreadsheets were submitted to our collaborators. These spreadsheets, by utilizing these nodular locations and relationships, were integrated into the pedestrian model in a format compatible with the programming language HTML5, which was used to create the model. 3.4.3 Census Tracts and Statistical Data

The remaining nodes within the area of study required additional data not provided through pedestrian counts. These nodes included many destinations of the model, such as places of work, as well as sources, which included both residencies, and various types of lodging facilities. In order to fulfill the requirements of the model and create probability data to appease the rules of agent attraction, the parameter which describes the total number of daily attendees would need to be discovered.

3.5 PUBLICIZING DATA To ensure this project can be expounded upon by future Mobility teams, the 2011 group publicized data utilizing several different sources. Using visual aids, called deliverables, the team was able to present pedestrian traffic data through informative means, including videos, graphs, and maps. The methodology described throughout this section was implemented with the purpose of being used by subsequent project teams to expand upon the pedestrian model that demonstrates walkers’ movement throughout the entire city of Venice. To aid in expanding upon the model, all data and information can be extracted from the team website at sites.google.com/site/ve11mobi. There are separate sections for data, video clips, and 46


graphics that display all of the visual results from the 2011 project, and the information that is in each section on the website can be seen in Table ##. Information

Location on Website

3.5.1 Deliverables

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

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Figure 13: 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. 3.5.2 Furthering a Pedestrian Model

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

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Results and Analysis

49


Recommendations

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Bibliography Amilcar, Marcus, Amy Bourgeois, Savonne Setalsingh, and Matthew Tassinari. Mobility in the Floating City: A Study of Pedestrian and Water Transportation. Interactive Qualifying Project Report, Worcester: Worcester Polytechnic Institute: Venice Project Center, 2009. Black, Kevin, Sara Migdal, Michael Morin, Dukens Rene, and Nick Vitello. Urban Maintenance and Venetian Accessibility. Interactive Qualifying Project Report, Worcester: Venice Project Center, 2008. Bloisi, D., L. Iocchi, P. Remagnino, and N. Monekosso. "ARGOS-A Video Surveillance System for Boat Traffic Monitoring in Venice." International Journal of Pattern Recognition and Artificial Intelligence, 2009: 1407-1502. Carnevale di Venezia 2012. 2009. http://www.carnevale.venezia.it/ (accessed September 18, 2011). Catanese, Chris, Danise Chou, Bethany Lagrant, and Rudy Pinkham. Floating Around Venice: Developing Mobility Management Tools and Methodologies in Venice. Interactive Qualifying Project Final Report, Worcester: Worcester Polytechnic Institute: Venice Project Center, 2008. Catanese, Christopher D., Danice Yequay Chou, Bethany J. Lagrant, and Rudy E. Pinkham. "Floating Around Venice: Developing Mobility Management Tool." 2008. Centre, Flight. "Italy: A Unique Work of Art." Footprints, 2010. Cessi, Roberto, and John Foot. "Venice." Britannica Academic Edition. 2011. http://www.britannica.com/EBchecked/topic/625298/Venice/24379/Canal-boats-and-bridges (accessed September 16, 2011). Cessi, Roberto, Denis Cosgrove, and John Foot. Italy. 2011. http://www.britannica.com/EBchecked/topic/625298/Venice (accessed September 16, 2011). Chiu, David, Anand Jagannath, and Emily Nodine. "The moto ondoso index: Assessing the effects of boat traffic in the canals of Venice." 2002. Contesso, Lia. Venice Bridges. 2011. (accessed September 19, 2011). Davis, Robert C., and Garry R. Marvin. Venice, the Tourist Maze: A Cultural Critique of the World's Most Touristed City. Berkeley: University of California Press, 2004. 51


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. How Stuff Works: Geography of Venice. 2011. http://geography.howstuffworks.com/europe/geography-of-venice.htm (accessed September 18, 2011). How Were Houses in Ancient Venice Designed and Why? http://answers.yourdictionary.com/entertainment-arts/architecture/how-were-houses-in-ancientvenice-designed-and-why.html (accessed September 7, 2011). Howard, Deborah, and S Quill. The Architectural History of Venice. Singapore: B.T. Batsford, Ltd., 2002. Italy. https://www.cia.gov/library/publications/the-world-factbook/geos/it.html (accessed September 15, 2011). Lopez, Angela. "Assessment of the Measure to Ease Pedestrian Congestion." Association for European Transport, 2006. Morgan, L. H. "The City of the Sea." Harper's New Monthly Magazine, 1782: 481. Ortalli, Gherardo, and Giovanni Scarabello. A Short History of Venice. Pisa: Pacini, 1999. Rameiri, E., V. Cogo, Mattei, and F.E.E. "Indicators of Sustainable Development for the City and the Lagoon of Venice." (Fondazione Eni Enrico Mattei) 1998. Riganti, Patrizia, and Peter Nijkamp. "Congestion in Popular Tourist Areas: A Multi-Attribute Experimental Choice Analysis of Willingness-to-Wait in Amsterdam." Tourism Economics, 2008: 1-15. Traffic Congestion Factoids. March 2009. http://www.fhwa.dot.gov/congestion/factoids.html (accessed September 14, 2011). 52


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 14: Flow Cart of Pedestrian Agent Types

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APPENDIX 2: CENSUS DATA GRAPHIC Table 4: Venetian Resident Density by Age and District (From 2001 Census Data)

APPENDIX 3: GIS CLOUD MAP LAYERS 3.1 Hotels Layer

Figure 15: Hotel Locations in San Marco

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3.2 Schools Layer

Figure 16: School Locations in Venice 3.3 Museums Layer

Figure 17: Museum Locations in Venice

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3.4 Churches Layer

Figure 18: Church Locations in Venice

Figure 19: Church Locations in San Marco

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3.5 Tourist Sites Layer

Figure 20: Major Tourist Sites in Venice

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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

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Tourists Traveling B to A

Total Tourists


APPENDIX 5: FIELD FORMS 5.1 Venetian Field Form Table 5: 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 6: 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 60

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

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APPENDIX 6: ESTABLISHMENT DATA FORM Table 7: Form for Institution Information

Date

Time

Establishment ID

Location

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Estimated Attendance

Capacity

Hours of Operation


APPENDIX 7: B TERM SCHEDULE

Figure 21: Mobility October Schedule

Figure 22: Mobility November Schedule

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Figure 23: Mobility December Schedule

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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

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Total Price $50.00 $12.00 $16.00 $83.00

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


Mobility_B11_Report_Draft3