Using agents to explore traffic: Part Two-Micro to Macro

Following on from a previous post on traffic modeling with agent-based models, I have been thinking of other work in this area and came across the following movies on Youtube. The first is a traffic simulator from Martin Treiber. What is interesting is the "coffeemeter" that gives an impression of the accelerations and jerks in the traffic. You can investigate this model further here: http://www.traffic-simulation.de/ or watch the movie below.

The question you might be asking in yourselves is, do such models work in reality? The mathematical theory behind these so-called "shockwave" jams was developed more than 15 years ago using models that show jams appear from nowhere on roads carrying their maximum capacity of free-flowing traffic – typically triggered by a single driver slowing down. Below is a movie of the NetLogo Traffic Basic Model exploring this principle.

Hopefully the movie above helps add something to your question. But if not check out the next movie (make sure the sound is on). In which a team of Japanese researchers recreated the phenomenon on a test-track by putting 22 vehicles on a 230-meter single-lane circuit. Drivers were asked to cruise steadily at 30 kilometers per hour, and at first the traffic moved freely. But small fluctuations soon appeared in distances between cars, breaking down the free flow, until finally a cluster of several vehicles was forced to stop completely for a moment. That cluster spread backwards through the traffic like a shockwave. Every time a vehicle at the front of the cluster was able to escape at up to 40 km/h, another vehicle joined the back of the jam. The full article can be read in New Scientist (click here).

Moving away from traffic jams, as the previous post highlighted we can also use agent-based models to look at traffic intersections. The movie shows a more complicated intersection than in the last post and shows how different intersections can be visualized and modeled.

But while the above movie is rather simplistic, agent-based models can be developed from such simple situations to more complex one. For example, if you can model one type of intersection what is stopping you for modeling more? The movie below shows a more complex set of intersections using Paramics (however, this is noted to be a microsimulation model, if you are interested in finding out the difference between microsimulation and ABM see here).

From a local scene we can also turn to exploring more larger scenes such as entire metropolitan regions. The movie below is of that of TRANSIMS microsimulation-agent based model applied to downtown Chicago:

What I find so interesting about such traffic models is how one can go from basic models at the micro level and scale up such models (and of adding more complexity) to explore more macro phenomena such as traffic jams at metropolitan scales.

Using agents to explore traffic

After spending time in the US, I am amazed how much one has to drive and this got me thinking about using agent-based models for traffic simulations (which is a large body of literature accompanying it). It also relates to my interests in urban systems and the fact that as cities have grown, transportation technologies have evolved (from walking, to trains etc), and now the automobile has become the dominant mode of transport for moving within and between cities. Trips range from journeys to work to shopping trips. The wide spread adoption and use of automobile is also one of the contributors to sprawl (in its many shapes and forms) as the car is not restrained by frequent stops or set routes, for instance such as trains are. Thus, if one can understand the relationships between land use and transportation one can investigate issues relating to urban sustainability. This is where agent-based models come in, in the sense they allow one to focus on the behavior of people. For example, how people decide to go to work.

ABM also allows us to explore simple thought experiments and how more aggregate results emerge from individual interactions such as: what is more effective, a four way stop or a traffic lights at a road intersection? The simple agent-based model presented below utilizes MASON and was created by Omar Guerrero of the CSS department at GMU. The rules of the model are simple, in the sense that at a four way stop, the vehicle that is first to arrive, it is first to move, unless two vehicles arrive at the intersection at the same time and then the vehicle has to give way to the car on the right. While at traffic lights vehicles must stop at red lights. The movie below shows part of the graphical user interface for a particular model run of both a four way stop and traffic light.

Even though this is a simple model, one can explore a number of issues such as how these different intersection configurations impact on the flow of traffic under different volumes of traffic. For example, at low traffic volumes in general, the stop sign is more effective (i.e. allows more cars to cross) than the traffic light. However, at greater traffic volumes the traffic lights out performs the four way stop (in the sense there are more cars in queues) but also with high traffic volumes, one can see oscillations in traffic waiting at the lights while the four way stops create long queues of traffic as shown in the figure below:

Moving away from micro patterns of traffic flows one can use ABM to explore daily commuting. For example, traffic models such as TRANSIMS or MATSim allow for the study of entire metropolitan regions and how traffic jams etc. form. To give a simple example of such a movement, the model presented below illustrates how many individuals can cause traffic jams. The model (using GeoMason) is based on commuters working within the Tyson’s Corner area of Virginia which boarders Washington DC. We take road and travel to work data from the US census and use this as the basis for our model.

The road data acts as a basis for our agents (red) to move from their homes (areas shaded green) to Tyson’s corner and the census data provides us with the number of agents who travel to the area on a daily basis. The agents attempt to find the shortest path from their home to the destination with preferential attachment to highways and freeways over smaller country roads. By running the model, cars start at homes and travel towards Tyson’s Corner and as more cars join certain sections of roads, traffic jams start to form (as speed is a function of the number of cars on a specific section of road). For example in the movie above, individual cars can be distinguished when they are not clustered but when traffic density increases, larger clusters develop.

The Business Assessment Model

The Business Assessment Model (BAM) evaluates trajectories of People, Performance and Planet (3P) of a single company as well as of the whole market system as a function of business decision of actors, exogenous events in the broader socioeconomic environment or both.

BAM computes the cumulative di difference between predictions of the perturbed and original 3P trajectories in order to conduct analysis of decisions within the medium run planning horizon.

Figure: Integrated 3P trajectories comparing system-level dynamics of both Baseline and Variant scenarios

Who is behind BAM?

This project has been developed by Robert Axtell and Maciej Latek from the Department of Computational Social Science, at George Mason University and Francesco Cordaro from Mars Corporation. Funding has been provided by Mars Inc. through it's Economics of Mutality initiative.

Where can I see BAM?

The project website https://www.assembla.com/wiki/show/sweetmutuality/ on which you can:

• Read an early working paper (pdf, 13 pages);
• Download self contained, ready to run version of the BAM simulation used in the "Comprehensive Assessment of Businesss Decisions" working paper (zip file) and associated presentation (ppt file) on running and interpreting outputs;
• See Validation Verification experiments we have performed with the current revision of the BAM, not included in the working paper;

BAM is implemented in MASON simulation framework.

Summer Course - Decision Maker Short Course - Computational Social Science & Policy

The Krasnow Institute at George Mason University is offering a Computational Social Science & Policy short course from June 19 - Jun 24, 2011. Click here to see more details.

The course description is as follows:

This six-day, non-credit, course is a unique opportunity to work with a team of experienced computational social scientists to explore and understand the application of new interdisciplinary approaches to modeling and making decisions involving the operations of social systems. Participants will imerse themselves in an intensive tour of the field of Computational Social Science, a broad set of efforts that seek to explain and predict how large-scale human systems from organizations to urban systems, from economies to society as a whole, evolve, react to stresses and stimuli, and cooperate and compete. Participants will hear presentations from experts in the field and engage in intensive dialoguing, demonstrations, and policy scenarios.

For further information and details see: http://krasnow.gmu.edu/DMSC/dmsc-css.html

Computational Social Science Concentration in the Master of Arts in Interdisciplinary Studies

We have recently received approval for a Master of Arts in Interdisciplinary Studies MAIS with a concentration in Computational Social Science starting in the Fall of 2011. Click here to to see the full details or read below.

Computational Social Science (CSS) is a relatively new interdisciplinary science in which social science questions are investigated with modern computational tools. Computational social scientists investigate complex social phenomenon such as economic markets, traffic control, and political systems by simulating the interactions of the many actors in such systems, on computers. They hope to gain insights which will lead to better management of the behavior of the larger social systems, i.e., prevention of market crashes, smoothed traffic flow, or maintenance of political stability. The intractability of many social problems calls for the new approaches provided by computational social science.

CSS is a highly interdisciplinary field that requires teams to plan and complete projects, be they undertaken by government, industry, or non-profit entities. Project managers of such teams, overseeing all elements of project design and execution, tend to hold PhDs. The MAIS concentration will train students to be members of these project teams, able to meaningfully contribute to background research and to project design, execution, and communication.

Prior background should include a bachelor’s degree in one of the social sciences, in computer science, in engineering, or in a relevant discipline, as well as undergraduate courses in these and related areas. Bachelor’s degrees in other areas are also eligible, but the student may be required to take additional courses in social science, mathematics, or computer science as prerequisites to admission.

This concentration will be available in fall 2011.

Concentration Requirements (Catalog Year 2010-2011)

• Six core courses (18 credits)

• Three required courses (9 credits): CSS 600, 605, 610
  

  The required CSS courses provide an understanding of the conceptual, technical, and practical foundations of computational social science.

• Three elective courses (9 credits) chosen from: CSS 620, 625, 645, 692, 739
  

  The electives provide an understanding of the technical foundations and current work in at least two subfields of computational social science.

• One research courses (3 credits) chosen from: CSS 796, 898, 899
  

  The research course provides students with exposure to the most current ongoing research in the field and allows them to further develop their computational research expertise.

• Three-four elective courses (9-12 credits)
  

  The electives allow students to acquire a substantive specialization as well as additional training in social and computational science. Because of the broad spectrum of social science phenomena, methodologies, and student backgrounds, there is a large pool of potential courses. Electives may include any Mason master’s-level course in computational social science, social science, computer science, statistics, or other quantitative methods such as data visualization, information technology, and geographic information science. Electives should be selected in conjunction with and approval of the student’s advisor and the Director of CSS Graduate Studies. If the student does not have prior coursework in multivariate statistical analysis, the electives should include at least one such course relevant for the student’s chosen specialization.
  

  Students who elect to do a 5-credit project or a thesis take 9 elective credits. Students who do a 2-credit project take 12 credits.

• Proposal (1 credit): MAIS 797
  


• Project (2-5 credits) or thesis (5 credits): MAIS 798 or MAIS 799.
  

Total: 36 Credits

Requirements may be different for earlier catalog years. See the University Catalog archives.

Director
Claire Snyder-Hall
mais@gmu.edu

Contact information
Robert Axtell
Head of the Concentration in Computational Social Science

Contact: Karen Underwood
Academic Department Coordinator
Department of Computational Social Science
Research 1, Room 373, MSN 6B2
Fairfax, VA 22030
703-993-9298
cssgrad@gmu.edu