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. Venice Project Center Venice Department of Mobility Santa Fe Complex Redfish Group In Collaboration With: Professor Fabio Carrera, Ph.D. Professor Frederick Bianchi, D.A. Project Information: Sponsors: Project Advisors: Abstract 1 Acknowledgements 2
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: [email protected]https://sites.google.com/site/ve11mobi Sponsors: Venice Project Center Venice Department of Mobility In Collaboration With: Santa Fe Complex Redfish Group
Transcript
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: [email protected] https://sites.google.com/site/ve11mobi
Sponsors: Venice Project Center
Venice Department of Mobility
In Collaboration With: Santa Fe Complex
Redfish Group
1
Abstract
2
Acknowledgements
3
AuthorshipThis Interactive Qualifying Project was completed with contributions from each team member.
Table of Contents .............................................................................................................................................. 4
List of Figures ..................................................................................................................................................... 8
List of Tables ...................................................................................................................................................... 9
2.1 The Architectural Framework of Venice ........................................................................................... 15
2.1.1 Origins of the City ......................................................................................................................... 15
2.1.2 Design of the City .......................................................................................................................... 16
2.1.3 The Canals ....................................................................................................................................... 17
2.1.4 The Streets ...................................................................................................................................... 17
2.2 Mobility in Venice ................................................................................................................................. 18
2.2.1 Watercraft in Venice ...................................................................................................................... 18
2.2.2 Water-Based Public Transportation ........................................................................................... 19
2.3 Tourism in Venice ................................................................................................................................. 21
2.3.1 Popular Tourist Sites and Events ................................................................................................ 21
5
2.3.2 Magnitude of Tourists ................................................................................................................... 22
2.4 Environmental Impacts on Mobility .................................................................................................. 22
2.4.1 Acqua Alta ....................................................................................................................................... 23
2.5.1 Past Models ..................................................................................................................................... 24
3.2.1 Focus Area and Key Counting Locations .................................................................................. 31
3.2.2 Counting Tools, Devices, and Methods ..................................................................................... 32
3.2.4 Time Brackets for Performing Field Counts ............................................................................. 33
3.3 Determining Video Surveillance Feasibility ....................................................................................... 34
3.3.1 Camera Set Up ................................................................................................................................ 34
3.3.2 Video Counting Verification ........................................................................................................ 35
3.4.2 Field Forms ..................................................................................................................................... 36
3.4.4 Census Tracts .................................................................................................................................. 37
3.5 Publicizing Data ..................................................................................................................................... 38
Results and Analysis ........................................................................................................................................ 40
Appendix 4: Database Form ...................................................................................................................... 50
Appendix 5: Field Forms ............................................................................................................................ 51
5.1 Venetian Field Form ......................................................................................................................... 51
5.2 Tourist Field Form ............................................................................................................................ 51
Appendix 6: Establishment Data Form ................................................................................................... 53
Appendix 7: B Term Schedule ................................................................................................................... 54
ListofFiguresFigure 1: St. Mark’s Basilica ............................................................................................................................ 15
Figure 2: A Canal Near the Arsenale .............................................................................................................. 17
Figure 3: A Standard Street in Venice ........................................................................................................... 18
Figure 4: Area of Study Map .......................................................................................................................... 27
Figure 5 Google Map of Traghetti Locations and Bridge Locations. Blue anchors simbolize traghetti
stops and red and yellow marker pairs simbolize bridge locations. .......................................................... 32
One of the greatest reasons that traffic is such an issue in Venice is tourism. However, since the end
of the 18th century, the Venetian economy has heavily relied on tourism, and it is a necessary burden
on the city. With a native population of approximately 61 thousand people5, the amount of tourists
flowing through the city on any given day outnumbers the locals in up to a 5:2 ratio6. While the city’s
economy is very firmly bound to tourism and its related industries, these visitors have contributed to
many problems for Venice and its inhabitants. The infrastructure is believed to be in danger of
giving way to the mass amounts of traffic. Some streets and canals are more readily accessible than
others at different times of the day, and mobility becomes increasingly hindered by people with baby
carriages and handicapped people in wheel chairs.
The issue of mobility in Venice is one that has been addressed by the Venetian government in a few
different ways. The Azienda del Consorzio Trasporti Veneziano, or Actv, is a public water–bus
transit system that facilitates the flow of water traffic by centralizing water travel through 20 routes
on the canals. There are also multiple surveillance systems in place, including the Automatic and
Remote Grand Canal Observation System (ARGOS), Hydra, and Security and Facility Expertise
(SaFE). These observational systems are used to implement speed limit laws, and monitor
pedestrians and boats for crime. Our sponsors have developed these systems and implement them
daily in Venice. ARGOS gives the vigili urbani (the Venetian police) the opportunity to routinely
dispatch officers to control traffic and make arrests on the Grand Canal, and Hydra and SaFE allow
authorities to monitor the Venetian ports for potential crime7. In addition to the observational
systems, our sponsors have developed a model for displaying boat traffic in the canals using data
gathered from ARGOS and Hydra. Called the Venice Table, the model is an interactive program
that displays the movements of boats through certain checkpoints on the canal.
The sponsors of this project have completed a sufficient amount of research, and the systems are
being run in an effective manner. There are some holes in the data and execution, however, and
limitations to the observational systems. Individuals in Venice run all of the systems, so there is no
automated system to collect and archive data, costing many man-hours. This also has great potential
to lead to human observational error. The data collection methods should also be fully automated to
ensure that data is continuously being collected. The data is currently being collected manually, in
5(Italy n.d.) 6(Amilcar, et al. 2009) 7(Bloisi, et al. 2009)
14
intervals of minutes at a time, and only in the tourist off-season. The scattered datasets create
difficulty in presenting the data in the modeling systems. Having counts taken only once a year by
the WPI Venice Interactive Qualifying Project groups or the Venetian Center of Mobility does not
take into account how peak tourist times, weather, seasons, events, times of the day, and other
aspects affect pedestrian counts. An efficient, comprehensive model would be one that contains
sufficient amount of data from year-round. Other significant improvements that need to be made
are in the agent identification feature. Agent identification would consist of recognizing the
difference between a Venetian and a tourist. It is important to study the difference in agents because
each different type has its own behavior and will go to different points of attraction, and each one
will have its own mobility stream. While a tourist may drift to a museum or a shopping center, a
Venetian will want to go to straight to and from work or home.
This gap in data collection is where the Venice 2011 Mobility team comes into play. There is
virtually no data or research present on Venice pedestrian traffic. This has provided Team Mobility
with the unique opportunity to pioneer data acquisition into pedestrian mobility streams. We will
collect pedestrian traffic data in Venice with a distinction between agent types, namely Venetians
and tourists. Using this data we will verify the accuracy of any past and future models. Through
analytical processes we will then be able to make suggestions for future autonomous continuous data
collection that can feed into an eventual integrated pedestrian model.
15
BackgroundVenice 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
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 1782) 9(Cessi, Cosgrove and Foot, Italy 2011)
Figure 1: St. Mark’s Basilica
Comment [C1]: Opinion
16
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.)
Comment [C2]: What’s that?
Comment [C3]: Dyes?
Comment [C4]: Opinion
17
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
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.) 13(Morgan 1782) 14(Cessi, Cosgrove and Foot, Italy 2011) 15(Morgan 1782) 16(Morgan 1782)
Figure 2: A Canal Near the Arsenale
Comment [C5]: Opinion
Comment [C6]: Miles and meters, inconsistent
18
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.
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) 18(Morgan 1782) 19(Morgan 1782) 20 (Howard and Quill 2002) 21 (Cessi and Foot, Venice 2011) 22 (Cessi and Foot, Venice 2011) 23 (Drake 2008)
Figure 3: A Standard Street in Venice
19
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.
24 (Drake 2008) 25 (Davis and Marvin 2004) 26 (Davis and Marvin 2004) 27 (Davis and Marvin 2004) 28 (Chiu, Jagannath and Nodine 2002)
20
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 well-
known 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.
29 (Davis and Marvin 2004) 30 (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)
Comment [C7]: What are the other three? Might as well name them.
21
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) 38(Carnevale di Venezia 2012 2009)
22
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) 40(Van der Borg, Tourism and Urban Development: The Case of Venice, Italy 1992) 41(Davis and Marvin 2004) 42(Rameiri, et al. 1998)
23
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) 44 (Davis and Marvin 2004) 45 (Davis and Marvin 2004) 46 (Black, et al. 2008)
24
parts of the walkways. This can cause backups down the walkways and have an overall negative
effect on congestion.
2.5 VENETIAN TRAFFIC MODELS
Looking into future applications of data collection, the creation of an integrated pedestrian traffic
model is necessary to provide an easy means of extracting useful information. Though the
development of such a comprehensive model is out of reach for this year’s Mobility team given the
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 (C. Catanese, et al. 2008) 48 (C. Catanese, et al. 2008)
Comment [C8]: Make sure to tie everything back to our project specifically. The past models don’t matter if they don’t apply to our project
Comment [C9]: I don’t know. Weird sentence structure and is it necessary?
Comment [C10]: Define agents
Comment [C11]: If we’re not doing agents, this needs to be changed
25
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 a lot of 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
Comment [C13]: My senior year English teacher would say that this is a colloquialism…
Comment [C14]: Connect this to our project
Comment [C15]:
Comment [C16]: Word choice?
26
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.
Comment [C17]: Is this defined already?
27
MethodologyOur project mission is to collect pedestrian traffic data for the end goal of developing an agent-
based modeling system that collects and archives data to effectively predict the behavior of
pedestrian mobility streams in Venice.
Project Objectives:
1. To quantify pre-determined pedestrian types at key locations
2. To determine the feasibility of using camera surveillance systems to collect pedestrian traffic
data by verifying video feed counts with manual counts
3. To organize the pedestrian traffic data collected into a format capable of helping develop a
pedestrian agent based model
4. To publicize pedestrian traffic data in a visually intuitive format on an online source
The project occurred over the 2011 fall semester, with preparatory work during A term and on site
work throughout B term. The project was limited to gathering data concerning pedestrian
congestion, taking into account only the predetermined agent typology.
Figure 4: Area of Study Map
3.1 PROVING ASSUMPTIONS
Traffic is an extremely complicated system. It is almost impossible to account for all of the factors
that can affect traffic at once. This makes it very difficult to accurately collect useful data related to
Comment [C18]: We have to do some rewording when we actually prove/disprove the assumptions, and give definite conclusions.
28
traffic flow. In order to develop a simple counting methodology that can be easily repeated while
maintaining efficacy, several assumptions about pedestrian traffic flow in Venice were made. Upon
making these assumptions to develop the counting methodology, an experimental procedure was
then developed to test the validity of these assumptions (Table 1).
Table 1: Assumptions
1 There are peak times 2 Peak times are consistent day to day 3 Weekday peaks are of similar magnitude 4 Weekend peaks are of similar magnitude 5 Specific bridges carry the majority of traffic flow 6 Secondary bridges carry an insignificant flow OR secondary bridges carry predictable
percentages of primary bridge or total traffic flow
Several constants were developed for each of these experiments to help ensure accuracy:
Counts were only conducted during ‘good weather’
o No precipitation
o Temperature above 40 degrees F
o Temperature below 90 degrees F
Counts not conducted during flooding or flood warnings
3.1.1 There are Peak Times
Low volume traffic flow carries significantly less importance from a data value standpoint than high
traffic flow. High traffic volume is what creates poor flow and puts the largest burden on the traffic
infrastructure. This being the case, and the unlikelihood of individuals counting in the future being
able to perpetually conduct traffic counts every second of every day, the methodology for counting
focuses on ‘maximum’ or ‘peak’ times. The peak counts that were conducted were performed in 3
hour blocks around the determined peak time.
Proving this assumption was based on finding this three hour ‘peak block’. To do this, a fifteen
minute data set was collected every 1 to 2 hours at the same bridge throughout a day. By
qualitatively viewing traffic volume on bridges throughout a day we could eliminate large chunks of
time as ‘non-peak blocks’. These negligible times include late at night and early morning. This
process was conducted at multiple bridges on the same day. Once the data was collected it was
graphed and the peaks were assessed.
29
FINDINGS. But really I guess those should be in the results section, so we have to stick strictly to
methodology and you can ignore half of my comments in this section.
3.1.2 Weekday Peaks are of Similar Magnitude
Assuming that weekday peaks are of the same magnitude allowed those conducting counts to collect
during one peak-time block over a week (ignoring weekends) instead of every weekday. In other
words, this assumption states that traffic on weekdays is equivalent.
To prove this assumption, data was collected every weekday during the peak-time blocks. This data
was then compared statistically to see if there was a significant difference between each 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.
3.1.3 Weekend Peaks are of Similar Magnitude
Assuming that weekend peaks are of the same magnitude allowed those conducting counts to collect
during one peak-time block over a weekend instead of every weekend day. In other words, this
assumption states that traffic on weekends is equivalent.
To prove this assumption, data was collected every weekend day during the peak-time blocks. This
data was then compared statistically to see if there was a significant difference between each days’
peak data set. If there was no significant difference, then weekend peaks are of the same magnitude.
It is important to note that only being in Venice for seven weeks made it impossible to collect 10-12
trials worth of data.
3.1.4 Peak Times are Consistent Day to Day
Once it was proven that peak times are the same throughout each weekday, and peak times are the
same throughout each weekend, the team could then specifically focus on the peak times in which
field counts should be conducted, and not be concerned about a particular day. Proof of this
assumption allowed for the maximum amount of data to be collected for a general day.
To prove this assumption, sample counts were collected in fifteen minute time intervals at each hour
throughout each weekday. The same is done for each weekend day. Using a standard deviation
curve, comparisons were made at each peak to see if the peaks at each bridge for each weekday fell
in the same three-hour block. If this occurred, it was determined that the assumption was correct
Comment [C19]: Once we prove, state findings
Comment [C20]: Mention how many we did collect
Comment [C21]: State findings
Comment [C22]: Same as above, put actual results in
30
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
others, so the assumption was kept and field counts were conducted by prioritizing the primary
bridges. This allowed the team to collect more comprehensive data for the foundations of the model
in progress.
3.1.6 Secondary Bridges Carry an Insignificant Traffic Flow OR Carry a Predictable Percentage of
Primary Bridge or Total Traffic Flow
This assumption is an extension of the previous assumption. It allowed those counting to use key
traffic points when counting and to ignore other points 100% or use key counting points to
determine the traffic flow at other points. If a bridge had a negligible traffic flow, then data did not
need to be collected there at all. If a secondary bridge has a measureable percentage of traffic flow
of a primary bridge,then one only has to measure traffic flow at the primary bridge and use
percentages to determine the flow over the secondary bridge in the same time frame.
This assumption was proved by comparing similar data sample ranges of different bridges over the
same general time frame, then determining percent flow of each bridge over the same time frames.
Statistical analysis showed if flow over any bridge is insignificant or if percentages of flow either
compared to another bridge or over total flow is constant from day to day.
Comment [C23]: Are sources, sinks, and nodes defined earlier in the report?
Comment [C24]: Same.
Comment [C25]: Which were?
31
3.2 QUANTIFYING PEDESTRIAN AGENTS
To accomplish the project objectives, Team Mobility accurately counted pedestrians at key locations
in the area of study. To do this, we developed a specific counting method to conduct manual counts
based on direction of flow at key connection points around San Marco.
3.2.1 Focus Area and Key Counting Locations
The 2010 Venice Mobility team previously analyzed congestion in the San Marco district at ten
bridge locations, therefore as the succeeding team, our plans were to expand to different counting
locations, also known as nodes for the purpose of the computer model, within the San Marco
district. After evaluating a map of the area, we decided that our counting would take place at the
four bridges that connects the two sections of land divided by the Rio San Luca, Rio del Barcaroli,
and Rio San Moisè. We also concluded that, because Ponte dell’Accademia is the only bridge on the
Grand Canal that leads to the western part of the San Marco district, it should also be analyzed by
our team. Counts were also performed at the four traghetto stops in the district along the Grand
Canal. The complete list of bridges and traghetto stops can be referenced in Table ##, and the map
of the nodes can be seen in Figure ##.
Table 2: Bridges and Traghetto Stops in the Study Area
Study Area Bridges Study Area Traghetto StopsPonte del Teatro Riva del Carbòn – Fondamente del VinPonte de San Paternian Sant’ Angelo – San TomàPonte de la Cortesia San Samuele – Ca’Rezzónico Ponte dei Barcaroli o del Cuoridoro Campo del Traghetto – Calle LanzaPonte de Piscina Ponte San Moisè Ponte dell’Accademia
Comment [CF26]: We should reference a figure that maps the 2010 counting locations
Comment [CF27]: Insert our study area map
32
Figure 5 Google Map of Traghetti Locations and Bridge Locations. Blue anchors simbolize traghetti stops and red and yellow
marker pairs simbolize bridge locations.
3.2.2 Counting Tools, Devices, and Methods
In order to accurately quantify the flux of pedestrians at bottleneck locations we utilized two types
of counting methods which allowed us to quickly and efficiently count a large number of
pedestrians. The first method was in-field, manual counting. The other was using several types of
video technology to determine the feasibility of collecting data from video streams and clips.
For the first method, we stationed ourselves at node locations and
utilized handheld mechanical counters. One individual can manage a
counter in each hand, one for each direction of traffic flow. However,
depending on the severity of congestion at any given time, we
determined whether it was necessary to station two counters at one
location. Each individual had a timer to keep track of the elapsed time.
If the weather was poor (raining, flooding, or excessively cold), the
Figure 6: Mechanical Tally Counter
33
Mobility team did not be conduct manual counts in order to avoid discrepancies in the data. We
counted only during ideal conditions, which provided us with the most accurate pedestrian traffic
information.
The second method for data collection was using video technology to determine the feasibility of
collecting counts with video clips. For this process, our team used a GoPro camera mounted on a
tripod in order to obtain footage from an aerial perspective, much like the surveillance systems used
by the city municipality. Footage was then downloaded and counted in comparison with manual
counts to prove our camera counting concept. Section 3.3 details the methodology for determining
the feasibility of surveillance technology.
3.2.4 Time Brackets for Performing Field Counts
Our team anticipated that pedestrian mobility in Venice will differ at different times of day and days
of the week, and was able to prove that traffic flow reaches a maximum at a certain time bracket,
and on any given weekday this time bracket remains the same. Once our assumptions were proven,
we chose that peak time to be our data collection interval and conducted manual counts in fifteen
minute intervals for three hours. We also validated the assumption that weekend days had the same
peaks and collected counts at that time bracket as well.
Because the traghetti run at specific times, unlike bridges which can be utilized continuously, our team
was able to count pedestrians at traghetti stops for their entire realm of operation under the same
assumptions concerning days of the week. Venice traghetto stops also experience fluctuations in
traffic patterns throughout the day. In the morning, Venetians travel to work or school and tourists
embark towards their tourist destinations. In the late afternoon, traffic is heavier as locals take lunch
breaks and tourists venture to the various attractions. At night traffic once again reduces because
citizens return home from work and tourists conclude their day.
Additionally, certain bridges will peak at different times than others because some bridges lead to
narrow alleyways while others lead to busy squares and popular attractions. Therefore, our team
determined a schedule of time brackets and intervals to structuralize our counting process so that
counts were recorded consistently. Appendix shows the schedules we used to conduct manual
counts at each node. The following table shows the time brackets that we used to divide any given
day:
Comment [CF28]: We should have a separate appendix section for the traghetti schedules, and the time intervals we collected bridge counts
34
Table 3: Time Brackets for Manual Counts
Bracket Name Start Time End Time
Early Morning 7:00 9:00
Morning 9:00 11:00
Mid-Day 11:00 13:00
Afternoon 13:00 17:00
Evening 17:00 19:00
3.3 DETERMINING VIDEO SURVEILLANCE FEASIBILITY
Development in software and video-feed based counting techniques have provided a much more
comprehensive method of collecting pedestrian traffic data. OpenCV based software, allows users to
analyze either live or recorded video feeds. Through the program, pedestrian counts as well as
direction can be determined. These systems trump human based physical counting for several
reasons. They have the ability to run continuously and provide data with no human input once set
up. This means that where human counting methodologies need to rely on assumptions to account
for gaps in data, video based systems can collect real data and work to eliminate experimental error
due to perceived or estimated data sets. After initial software development costs, the systems can
continually provide a wider range of coverage with each camera investment which gives a broader
data input picture. Looking to provide as accurate a traffic model as possible for the city of Venice, a
proof of concept was developed in order to test the feasibility of such a system in Venice.
The goal of the proof of concept for the Mobility Team was to provide a variety of video feed
samples that represent the complexity and variety of pedestrian traffic in Venice at key counting
locations that were established in the Study Area Map. These video samples provided an appropriate
and comprehensive data set for the Open CV software which, once developed, could be used in
testing. The variety of video feeds, once paired with the software, also provided insight into the
camera orientations necessary for the best software based data collection.
3.3.1 Camera Set Up
To collect pedestrian data at bridges, an HD Go Pro Camera was attached to a tripod rig 12 feet
from the base of the stand. The stand was then placed in an area to the side of the bridge where the
feed was taking place so that it would not impede traffic but would still provide a bird’s eye view of
Comment [C29]: This is background, not methodology
Comment [C30]: All background.
35
the traffic ‘choke-point’. The camera lens was aimed perpendicular to traffic flow. The video feeds
were each 15 minutes in length to provide continuity among pedestrian traffic data collected. The
feeds covered a variety of scenarios often seen in Venice. The scenarios that feeds were collected for
are:
Good Weather (Clear/Sunny) – Low Volume of Traffic
Good Weather (Clear/Sunny) – High Volume of Traffic
Poor Weather (Overcast/Cloudy) – Low Volume of Traffic
Poor Weather (Overcast/Cloudy) – High Volume of Traffic
Rainy Weather – Low Volume of Traffic
Rainy Weather – High Volume of Traffic
Night Time (No Rain) – Low Volume of Traffic
Night Time (No Rain) – High Volume of Traffic
3.3.2 Video Counting Verification
The purpose of using video surveillance technology was to allow for manual counts to be collected
without a person having to be on site. To prove this concept, our team conducted counts using
video technology prepared as previously described, and recorded video clips during the on-field
manual counts, then compared the two datasets to determine if using cameras as a means for
collecting data was a practical method.
3.3.2 Verification Analysis
Once the video was recorded and the field count time interval was completed, the data was analyzed.
Another individual who was not involved in the counts taken at that scenario counted pedestrians
from the video clip to ensure the accuracy of the video counting methodology. This provided an
unbiased viewpoint for every feed. For the purpose of determining the percent error from the video
technology, counts taken from video clips were labeled “experimental” data and counts taken
manual on field were labeled “actual” data. If the percent error calculated is determined to be too
significant, then video surveillance technology was rejected for that scenario.
36
3.4 ANALYZING AND VISUALIZING COLLECTED DATA
We used field forms and other data collection forms to properly format our pedestrian data to
accommodate RedFish Group’s modeling preferences. We also implemented census tracts to further
our traffic datasets and to complement agent analysis.
3.4.1 Formatting
To ensure that the agent-based model our team is contributing to is performing as anticipated, our
team came up with a usable format for tabulating data for the programming capabilities of our
collaborators. However, we also had to take into account the visual limitations of the counters on
field when collecting large amounts of data at once. It was important not to miss any individual
while on field to ensure the least amount of error. The previous team performed preliminary field
counting to determine the limit of one counter, and found that one counter was capable of
recording one direction of flow while distinguishing between Venetian and tourist without being
overwhelmed. Their team decided that two counters per location, one per direction, were necessary
to reduce the risk of data loss. If a certain time or location was anticipated to have unusually high
traffic volumes, the decision was made as to whether or not more than two counters would be
stationed to that location. Additionally, to verify the efficiency of our model and the accuracy of our
on location counts, we used the same form for our video recording counts.
The counts made by each individual was then collaborated at the end of the time bracket and
collected in excel spreadsheets that were submitted to our collaborators and integrated into the
pedestrian model. This data was also converted into a format visible to GIS Cloud for still-time
visualizations. Refer to the following section 3.3.2 for the details on the data collection forms.
3.4.2 Field Forms
To collect all of the data in an organized manner for the utilization of our collaborators, a field
spreadsheet template was created. This was used to collect the number of persons that cross through
a specific station by type of agent, and in which direction of travel. Refer to Appendix 5 for an
example of a field form. The same template was used to collect counts through video clips.
This field form was also used to tabulate data in a form suitable for our collaborators to integrate
into an agent-based model. Table 3 shows the columns that were filled out for collection of all on-
field data.
37
Table 4: On Site Manual Pedestrian Counting Template
Date: Location: Recorder: Time Traveling To Traveling From Count
To collect data such as the number of students enrolled in a school on location, or how many
people buy tickets to a certain museum, or even how many Venetians attend a specific church, we
used a survey guideline in the field. Key information from these sites would be attendance and hours
of operation. Knowing the capacity of specific establishments helped create a better model agent
interaction with the environment. The information collected was then inputted into a spreadsheet
for use in GIS map layers and for the use of our collaborators. Table 4 below provides the intended
information we would hope to acquire from these institutions.
Table 5: Video Surveillance Data Collection Template
Date Time Establishment ID
Location Estimated Attendance
Capacity Hours of Operation
3.4.3 Pedestrian Modeling Techniques
Though the 2011 Mobility Team lacked the experience to create a NetLogo model based on the data
collected, the data fed models created by the RedFish Group and other organizations. Aside from a
working model, the data was also worked into several GIS cloud layers. The manual counts were
able to show tourist:Venetian concentrations at collection points and also allowed us to create a
‘heat map’ that shows population density at certain points in time. Once these were overlaid on the
GIS map, they were then compared to other layers to show correlation. The population density heat
map layer, viewed in conjunction with source and sink layers (e.g. schools, hotels, and museums)
shows the causes of the changes of population density throughout a day.
3.4.4 Census Tracts
Collaborating census data for our region of study is critical for supplementing our agent analysis. To
better understand pedestrian behavior, the origins and endpoints of each agent must be detailed.
The census layers of the GIS map complemented Venetian data that our team collected by providing
a picture of the residence distribution of the Venetian pedestrian agents. For example, Figure 21
shows the amount of adults from ages twenty to sixty-four who live in particular regions in the San
Marco area.
38
These different age brackets helped us understand the destinations of these different agent types.
Agents under twenty years of age would likely leave their homes to go to a school in proximity to
their residency. Census tract layers can also provide the location and quantity of employed Venetians
in a region. Figure 22 shows an example of the employment source location distributions in San
Marco.
3.5 PUBLICIZING DATA
Once the data was collected, analyzed, and formatted using the techniques outlined above, we
published our findings for public viewing through the following means.
3.5.1 Venipedia
Venipedia is an online source created and maintained by Venice IQP project groups. It is the
“Venice Wikipedia” and contains articles on myriads of topics specific to Venice. Our project group
contributed to Venipedia by creating new pages concerning the end results of the project. The new
pages cover our organized data of the main research topics and the visual aids we created. This
allows public access to the information, and can be expounded upon by future groups.
3.5.2 Deliverables
A major component of the Venice projects is deliverables, or visual and interactive aids that aptly
summarize the findings of a project. Our deliverable is an interactive layered Google map of the city
of Venice, with different “layers,” or data sets, that can be displayed on or hidden from the map.
The layers consist of direction of travel, beginning and ending locations, schools and places of
employment, residential and commercial zones, tourist hotspots, hotels, traghetto stops, and other key
locations. Figure ## illustrates the many of these types of sources and sink locations.
39
Figure 7: Sources and Sinks
Ideally, this visual aid will allow the public to see the congestion locations and reconsider their route
across Venice, taking into account the most congested areas as seen on the deliverable map.
3.5.3 Furthering Models
An objective of our project was to collect and format data in such a way as to further the
development of agent-based modeling systems. We did this by complying with the correct data
format for the models as specified by RedFish Group. We compiled all of our data, sorted it into the
specific format, and edited it to include the correct dataset for RedFish’s purpose. Ultimately, this
will enable the company to develop a model for pedestrian congestion, taking into account traffic
flow and congested locations.
40
ResultsandAnalysis
41
Recommendations
42
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