Improving Pedestrian Flow through Modeling and Space Syntax within GIS

Improving Pedestrian Flow: Modeling and Space Syntax within GISA Case Study of the Wings Over Pittsburgh Air Show

Benjamin Shelton - MGIS CandidateAdvised by Dr. Alexander Klippel Hi I am Ben Shelton and for my capstone project I am trying to Improve Pedestrian Flow through the use of Modeling and Space Syntax analysis. To demonstrate how these two aspects can be used to improve the flow of pedestrian traffic, I am going to use the Wings over Pittsburgh Air Show, that I have had the opportunity to work on for the past few years, as a general case study. In one given year, over 200,000 people may visit the air show over the entire weekend so any improvements to how well they are able to move through the space can make a huge impact on the overall show.

1Motivation

Photo Courtesy of http://www.pittsburgh.afrc.af.mil/Wings Over Pittsburgh Air ShowThis picture shows part of the layout for the 2009 Air Show. The major layout aspects that need to be planned for are the static aircraft displays and the various tents or vendors. Currently the planning process involves formulating static plans and maps to account for all these aspects. However, the one factor that is difficult to account for and probably has the largest impact on the success of the show is the flow of spectators through the area. 2MotivationPhoto Courtesy of http://www.pittsburgh.afrc.af.mil/Wings Over Pittsburgh Air Show

This view is part of the layout from last years air show just as the first day was beginning. As you can probably tell, many different logistical and other factors go into how to locate the air craft throughout the apron area. The influence on spectators and the movement around them is near the bottom of the list of these factors, but if there was evidence from spatial analysis, small adjustments could be made that could have a big difference on the impact these aircraft have on guiding spectators around the space.3Improve the placement of static displays, tents, and vendors.Improve accessibility for general public.Identify major thoroughfares to ease flow of pedestrians.Improve egress routes for disaster/emergency preparedness.MotivationOverall, the general motivation for this project in the beginning was to improve the placement of static displays, tents, and vendors, to improve accessibility, and also to improve emergency preparedness plans.Approaching this topic I really wanted to formulate an easy and accessible way to incorporate the movement of people into the planning process. Not just mapping out the space but incorporating the movement through this space into the decision making processes. This can benefit many of the functional areas that have a hand in the air show: security forces, safety, emergency management, and the services and vendors. while researching, it was interesting to find out that pedestrian movement is of interest to so many different fields: Transportation Planning, Urban planning and design, the retail industry, law enforcement, emergency management, and even the video game industry have an interest in visualizing and analyzing pedestrian movement. 4Research QuestionHow can geospatial modeling and analytical methods to represent pedestrian movement be incorporated to improve the flow of pedestrians and accessibility of a large public event?

-Flow measured through use of Agent Based Modeling scenarios.-Accessibility measured in terms of Space Syntax parameters.My overall research question moving forward shown here is. How can geospatial modeling and analytical methods to represent pedestrian movement be incorporated to improve the pedestrian flow (measured through the use of Agent-Based Modeling Scenarios) and to improve the accessibility (which will be measured with space syntax parameters ).I want to also try and combine these two measures so the Space Syntax measures can then be used to show causality for what is observed in the Agent Based Modeling scenarios. I am hoping to be able to validate the Space Syntax results with the Agent Based Modeling scenarios. This will hopefully provide some insight that will demonstrate more than just how these measures can apply to this specific case study. It will also show how well these two techniques for modeling and visualizing movement can complement one another.5MethodologyDetermine best methods for visualizing and analyzing pedestrian movement through a space.Determine how to best incorporate into GIS.Assessment in real world scenario using past Air Show data.To address the research question, my process moving forward with the project will consist of the following 3 main elements: First, I will compare the different techniques for both modeling the pedestrian movement and for analyzing this movement. Second, I need to address how this can be incorporated into the existing GIS that are already utilized in the planning process from year to year. Third, applying the methods and analysis toward the actual real world scenario of the Wings Over Pittsburgh Air show will allow me to compare layouts from past years and determine how changes in the positioning of certain elements can influence the overall flow of spectators. This will also allow me to hopefully draw some conclusions on the general relationship between a layout design and its effects on pedestrian movement. 61. Methods for visualizing & analyzing pedestrian flowSpace SyntaxScience-based approach to urban planning, design, and architecture.Characterizes spaces to analyze movement, interactions, land uses, and many other aspects.

Axial Lines:Used to represent flow of movement along lines of visibility from point to point through a space.Used to measure spatial connectivity and integration.

The first method that will provide me with the measures for accessibility of the given environment is Space Syntax. This is a well-developed field that takes a science based approach to urban planning, design, and architecture. It characterizes different spaces to analyze movement, interactions, and other aspects. It originated at the University College London and is still a major topic there at the Center for Advanced Spatial Analysis.Most of Space Syntax analyses have been done with the use of axial lines, which are used to represent flow of movement along lines of visibility. Certain values, such as integration and connectivity, can then be measured with these lines that give insight into how well certain areas are connected with surrounding areas. The first value, integration, refers to a measure of how well certain spots are linked to all the other locations in the study area. It gives a numerical result that reflects how easily an axial line is reached from all other axial lines throughout the space. Research into space syntax has shown that Movement patterns are powerfully shaped by the spatial layout ( Space Syntax Network website). The use of axial lines and parameters like integration are the tools that allow the analysis of these movement patterns and how they are influenced by the layout of the area.71. Methods for visualizing & analyzing pedestrian flowSpace Syntax Example

[2011 Air Show Layout]

Here is a quick example of space syntax and the development of axial lines. This map shows the general layout of the 2011 air show. The pink line to the southwest and west is the main crowd barrier and you can see the layout of the static aircraft displays throughout the spectator area. Most space syntax analyses either deals with a regular street network or interior building spaces. Manually drawing axial lines in these types of environments is a much more straight forward process than in a large open area like this air show layout. Therefore, using some space syntax software, I chose to allow the software to automatically generate the axial line maps based on the layout I imported.

81. Methods for visualizing & analyzing pedestrian flow

Space Syntax Example[2011 Integration]

The results are shown here and the integration values across the area are displayed. As you can see the redder the line, the higher the integration value which means that those areas are more easily reached from all other areas. To reiterate, this integration measure tries to give insight into how easily each line can be reached from every other axial line. It does this by calculating the average number of areas a person must traverse to reach the particular line from all other lines throughout the area. This average is then used to calculate the integration value to determine whether an area is well integrated, red, or fairly segregated, blue. As you can see, there is a large integrated area running from the northeast to the south of the area. There is also another corridor with a high integration running directly north to south. There are also some segregated areas to the northeast in blue.

91. Methods for visualizing & analyzing pedestrian flowSpace Syntax Example

[2009 Integration]

This map shows the same integration measure, but for the 2009 air show layout. As you can see there are fewer areas that have a high integration value in this layout compared with the 2011 space. This could be a result of the placement of aircraft or vendors, or it could be due to the fact that the 2009 air show had quite a few more static display aircraft than what was present at the 2011 show. The comparison clearly shows that areas such as the northeastern section, which is the main entrance and exit point, are much less integrated with the rest of the space. Being less integrated, the exit is not as easily reached from all the other areas in the given space. This should have an impact on the flow of pedestrians in the event of an evacuation is required. The north-south corridor of high integration still remains, but there are more areas at the edges that have much lower integration values.

101. Methods for visualizing & analyzing pedestrian flowSpace Syntax Example

[2009 Connectivity]

Integration is not the only measure derived from the space syntax analysis. Another important one is the connectivity. This slide displays the connectivity for the 2009 air show. The connectivity measure tells us how many other lines are directly connected to a particular line. As with the integration values, a higher level of connectivity correlates to the level of accessibility for particular areas. The idea is that the higher the integration and connectivity values are, the more accessible these areas are, and the more accessible they are, the more foot traffic or use they acquire. Therefore, these darker areas in this map and the previous maps will in theory attract more pedestrian traffic due to the affects the layout have on the natural movement throughout the area.So areas in red here and orange here will theoretically have more traffic and use than areas in blue such as here and here. These types of parameters provide a lot of good analysis for planning events like this because generally there are areas that we want to funnel traffic to like recruiting centers or vendors. There are also areas that it would be preferred to limit the foot traffic such as here where there might be need for access by emergency vehicles that are normally placed on the other side of this crowd line in case of emergency. 111. Methods for visualizing & analyzing pedestrian flowSpace Syntax Example

[2011 Alternate Layout Integration]

ADJUSTED

ORIGINAL

During this project, I plan on not only analyzing past air show datasets, but also modifying these datasets to analyze what affects on these measures can be achieved through adjustments in layout elements. This slide demonstrates that by taking the 2011 air show data and by modifying certain elements, changes in the integration values for certain areas can be significantly influenced. To create this map, several of the larger aircraft were moved from the center of the layout and relocated further to the southeast and replaced with smaller aircraft. Also, some vendor locations near the center were moved and combined with the general vendor area that is located just off the edge of the spectator area. As you can plainly see through just a visual comparison, this greatly increased the integration values for a large portion of the overall area. By increasing the integration values for a large portion of the area, this should in theory allow pedestrian traffic to be more dispersed throughout the area instead of funneling people into one or two major corridors. By spreading out this traffic, it should also increase the speed and efficiency of evacuations or other scenarios like that. This is just one hypothetical example of how these type of comparisons can bring about a more complete understanding of what influence the layout of the air show has on the accessibility and movement patterns of pedestrians.

121. Methods for visualizing & analyzing pedestrian flowAgent-Based Model (ABM)Agent: Autonomous, goal-directed data points that are interactive, both with the environment and with other agents. Pros:More realistic than previous density or group-level approaches for pedestrian behavior .Modeling at the individual level provides insight into complex local interactions that influence the overall movement pattern.Flexible to fit many scenarios and environments.Cons:Requires high computer processing to handle a large number of agents.Difficult to encompass all the complexities of human behavior into an agent.The second method utilized will be Agent-Based modeling to simulate the movement through the given environment. The definition of an Agent varies depending on the field or specialty of those doing the modeling. For this study, the general definition will be that Agents are autonomous, goal-directed data points that are interactive, both with the environment and with other agents. There are a very wide-range of disciplines implementing ABM, as well as a wide range of complexity in the models. Some disciplines that have implemented agent based models include Artificial Intelligence (for modeling computer systems), ecology (for modeling migration and population patterns), epidemiology (to model the spread of infectious diseases), and archaeology (to modeling the spread and movement of ancient civilizations). These disciplines apply models ranging from generic agents simulated in a uniform environment to a simulation that uses multiple classifications of agents behavior levels and a very realistic, complex environment.Listed here are some pros and cons to this type of model. They tend to be much more realistic than density or group level approaches. Observing local interactions at an individual level that influence the big-picture movement pattern is a major advantage as well. Also, it is very flexible and can be rerun to test results or hypotheses. The negatives are that it requires a large amount of processing power and the fact that humans tend to be unpredictable and complex.

132. Incorporating into GISAgent Based Models:Hurdles to implementing ABM completely within GIS:Difficulty of GIS in handling temporal data.Inability of GIS to handle the execution of a model of agent behaviors for individual data points.GIS-centric vs. ABM-centricGIS-centric: External tools and scripts used within GIS software to develop an Agent-Based Model.ABM-centric: GIS data used within external Agent-Based Modeling software.**PedestriansV1 ABM example downloaded from NetLogo User Community Models Website. Originally created and submitted by Singhathip Mickael in 2008.

To go along with some of the cons from the previous slide, major hurdles that come from implementing agent behavior directly within a program like ArcGIS are the difficulty of incorporating temporal data within ArcGIS and the fact that most Geographic Information Systems process data as a whole instead of being able to apply differing agent behaviors to multiple points all at once. A data point at one point may have dissimilar influences than data points at other locations. The fact that ABM software and tools can use algorithms and scripts to handle this change over time more efficiently is the major motivation in using a combination of both GIS and an ABM software or toolkit. When coupling the two software platforms, there are generally two main approaches: a GIS-centric approach and an ABM-centric approach. A GIS-centric approach means that the software or toolkit that performs the actual modeling is brought into the GIS user interface and data model. As you can guess, the ABM-centric approach is the exact opposite, where the GIS data is exported into the ABM interface. The agent based model example shown here shows how an ABM software package, in this case NetLogo, can be used to simulate people moving at different speeds through a finite space.

14SwarmOpen sourceObjective C or Python

Repast (Recursive Porous Agent Simulation Toolkit)Open sourceJava or PythonAgent Analyst toolbox for ArcGIS Latest release is for 9.2 (GIS-centric example)

MASON (Multi-agent Simulator of Neighborhoods)Open SourceJava

NetLogoOpen SourceHas GIS extension that provides support for vector and raster data integration.

Possible ABM Software Solutions: I just wanted to highlight a few of the Agent-Based Modeling programs that are out there. I wont go into a great amount of detail about each one, but I just wanted to show that there are many different solutions besides the one I selected. When I began looking into this project, I did not expect to find so many readily available possibilities. This list is only a tiny fraction of them.Although all the options listed here are open source, the first three solutions listed here entail a high learning curve for non-programmers or novice programmers like me. However, they may be able to increase the complexity of the types of agent simulations possible and provide a more realistic view of pedestrian flow. The SWARM and Repast options are becoming more accessible as they incorporate more support for developing models in Python. The Repast platform has the most potential for coupling with GIS because of the development of the Agent Analyst toolbox. This tool enables the use of the robust Repast agent based model toolkit directly within ArcMap, but currently the latest release is for ArcGIS 9.2. Once a version is released for ArcGIS 10, it will definitely become a great tool for this project in order run the simulation within the current data model. However, since my work will be done on a computer that has version 10, an external software solution is necessary to run the simulations and then export and incorporate the results back into the data model.Currently, NetLogo seems like the best fit for my programming level and the level of complexity the models require in this project. I will be able to import my layouts with the use of the GIS extension that provides support for vector and raster data within NetLogo. Also, there is a large model library available to provide a good starting off point to creating the models I want to use. The pedestrian model from the previous slide is one such available model that can then be taken and customized to generate the behaviors or scenarios I need.

15UCL DepthmapStandalone product.Able to import geometry of environment and export space syntax results into MapInfo or .DXF file format .ConfeegoIntegrated with MapInfo.AxwomanWorks as an extension within ArcGIS (Originally for ArcView 3, but latest release works within ArcGIS10).

Others include Syntax2D, Place Syntax Tool, Segmen

Currently, External software provide more robust analysis and documentation than any GIS-based Space Syntax solutions.Possible Space Syntax Software Solutions:This list shows some of the available space syntax platforms. UCL Depthmap and Confeego are probably the two most prominent platforms for this. Confeego would be a good option for those with spatial data already in MapInfo since it is directly integrated for that GIS platform. Axwoman is an extension for ArcGIS that was developed by Dr. Bin Jiang from the University of Galve, Sweden. It is useful to be able to perform the functions directly within ArcMap, however, the lack of documentation and difficult implementation make other options more attractive. Currently, External software provide more robust analysis and documentation than any GIS-based Space Syntax solutions that I have found. The axial line map examples from the previous slides were created using the UCL Depthmap program and this is the program that I am going to proceed with because of its ease of use and it is a little more documented than others.

16UCL DepthmapNetLogoExport LayoutExport LayoutABM SimulationVisibility Graph AnalysisPedestrian FlowSimulated gate countsIntegration ValuesConnectivity2011 Air Show GIS LayoutArcGISProcess:Agent-Based Model:Used to model scenarios of pedestrian movement through the air show space

Space Syntax:Used to characterize the layout of the air show space and explain the movement shown in the Agent-Based Model.

The plan going forward is to use the NetLogo software to model scenarios of generic agents moving through the air show space. The results of these scenarios can then be imported back into ArcGIS. Exactly what form these results will be in is still uncertain. It could be rates of flow at certain points, paths of individual agent movement, crowd density or locations of agents at snapshots through time. Next, UCL Depthmap will be used to derive the space syntax parameters. These parameters can provide some context to explain what is seen in the previous agent based model step. Looking at the results of each step individually can provide a great deal of insight about the layout. However, exporting the results of both the ABM and space syntax results will allow for correlations to be identified and further spatial analysis. 17Perform Space Syntax AnalysisPerform ABM SimulationPerform Space Syntax AnalysisPerform ABM SimulationPerform Space Syntax AnalysisPerform ABM SimulationPerform Space Syntax AnalysisPerform ABM Simulation-Integration Values-Connectivity-Pedestrian Flow Model-Integration Values-Connectivity-Pedestrian Flow Model-Integration Values-Connectivity-Pedestrian Flow Model-Integration Values-Connectivity-Pedestrian Flow ModelChange in Integration/Connectivity & ModelChange in Integration/Connectivity & ModelChange in Integration/Connectivity & ModelFind Correlations201120092008ALTProcess:Taking the diagram of the process for one particular layout and building upon that, I came up with this overall process diagram. Taking layouts from the past shows and alternative layouts, the space syntax and agent based model results can then be compared to find correlations and make some conclusions on not just how these layouts can be improved, but also how well the syntax values can show some basis for the agent based model results.I think the unique nature of this air show offers some advantages to drawing some conclusions about how changes in layout elements affect the various outcomes of both space syntax and ABM simulations. The fact that the overall area generally remains the same from air show to air show in relation to the main crowd barriers and the airfield pavement used, allows for comparisons of accessibility and pedestrian simulations that correlate directly to the changes in statics and vendor locations. As seen by the previous example, modifying any of the previous air show data can create alternative layouts to test hypotheses and note the changes that result compared to any of the previous years. This has a lot of possibility in not only improving the air show layouts in the future, but also to make some conclusions about the syntax parameters and their relation to the ABM simulation results. As stated before, I am hoping to be able to validate the results of a space syntax analysis with the simulation from an agent based model by showing if there is a correlation between an increase in connectivity or integration and an increase in rate of flow or some other result from the simulation.18Conference Presentation / Project Timeline7th International Conference on Geographic Information Science (GIScience 2012)September 18-21, 2012

Deadline for Extended Abstract Submittal :May 4, 2012My timeline is generally based around the conference dates shown here. While I have made some progress in initializing the Space Syntax parameters, there is still a lot of work to be done in relation to Agent Based Modeling of the air show layout and correlating the results. Although the methodology and detailed process needs to be in place to write the extended abstract by May 4th, the goal is to have the project completed and conclusions done by the June/July timeframe in order to be ready for the conference presentation.19Questions?And now I am ready for any Questions if anyone has any for me?20ReferencesBatty, M., Desyllas, J., & Duxbury, E. (2003). Safety in Numbers? Modeling Crowds and Designing Control for the Notting Hill Carnival. Urban Studies, 40: 1573. DOI:10.1080/0042098032000094432. Berryman, Matthew (2008). Review of Software Platforms for Agent Based Models. Land Operations Division, Defense Science and Technology Organization, Edinburgh, Australia. Available at http://handle.dtic.mil/100.2/ADA485784.Castle, Christian and Crooks, Andrew (2006). Principles and Concepts of Agent-Based Modeling for Developing Geospatial Simulations. Center for Advanced Spatial Analysis (University College London): Working Paper 110. London. Available at http://www.bartlett.ucl.ac.uk/casa/pdf/paper110.pdf.Crooks, A., Castle, C, & Batty, M. (2007). Key Challenges in Agent-Based Modeling for Geo-Spatial Simulation. Center for Advanced Spatial Analysis (University College London): Working Paper 121. London. Available at http://bartlett.ucl.ac.uk/casa/pdf/paper121.pdf. Gimblett, H. Randy (Ed.) (2002). Integrating Geographic Information Systems and Agent-Based Modeling Techniques for Stimulating Social and Ecological Processes. Cary, NC: Ocford University Press.Jiang, Bin & Claramunt, Christophe (2002). Integration of Space Syntax into GIS: New Perspectives for Urban Morphology. Transactions in GIS, 6: 3, 295-309. DOI: 10.1111/1467-9671.00112.OSullivan, David (2008). Geographical Information Science: agent-based models. Progress in Human Geography 32: 4, 541-440. DOI: 10.1177/0309132507086879.Tang, Wenwu (2008). Simulating Complex Adaptive Geographic Systems: A Geographically Aware Intelligent Agent Approach. Cartography and Geographic Information Science, 35: 4, 239-263. DOI: 10.1559/152304008786140551. Torrens, Paul (2012). Moving Agent Pedestrians Through Space and Time. Annals of the Association of American Geographers, 102:1, 35-66. Available at http://dx.doi.org/10.1080/00045608.2011.595658. Turner, Alasdair (Ed.) (2007). Proceedings of Workshop on New Developments in Space Syntax software. 6th International Space Syntax Symposium. Istanbul Technical University: Istanbul, Turkey. Available at http://discovery.ucl.ac.uk/4109/1/4109.pdf.

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