A Multi-agent Simulation Model of a Signalized Intersection Considering Interaction Between Pedestrians and Vehicles

  • H. Hoyos
  • J. Torres
Conference paper


This work develops a multi-agent simulation model of a signalized intersection. The model consists of a scenario that represents the streets and sidewalks of an intersection. In the scenario, pedestrians and vehicles interact according to algorithms and different movement rules. The simulation provides information about the level of service at intersections in terms of the number of pedestrians, average space per pedestrian, average speed of pedestrians and vehicles, and the number of vehicles. The model is validated by comparing some of the output variables to the measurement of these variables at real intersections. Finally, the study presents a hypothetical intersection, from which scenarios are created to measure the impact on the service level in terms of pedestrian flow and the geometry of the sidewalks at the intersection.


Traffic Flow Signalize Intersection Traffic Light Secondary Street Pedestrian Flow 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. Ahmed K.I. (1999). Modeling drivers’ acceleration and lane changing behaviors. PhD thesis, Department of Civil and Environmental Engineering, MIT.Google Scholar
  2. Chandler R., Herman R. & Montroll. (1958). Traffic dynamics: Studies in car following. Operations Research 6, 165–184.Google Scholar
  3. Gazis D., Herman R. & Rothery R. (1961). Nonlinear follow-the-leader models of traffic flow. Operations Research 9, 545–567.MathSciNetzbMATHCrossRefGoogle Scholar
  4. Gipps P.G. (1981). A behavioral car following model for computer simulation. Transportation Research 15B, 101–115.Google Scholar
  5. Herman R., Montroll W., Potts R.B. & Rothery R.W. (1959). Traffic dynamics: Analysis of stability in car following. Operations Research 7, 86–106.MathSciNetCrossRefGoogle Scholar
  6. Highway Capacity Manual (2000). Transportation Research Board. National Research Council, Washington, DC.Google Scholar
  7. Ishaque M. & Noland R. (2007). Trade-offs between vehicular and pedestrian traffic using micro-simulation methods. Centre for Transport Studies, Department of Civil and Environmental Engineering, Imperial College London.Google Scholar
  8. Kaneda T. & Okayama D. (2007). A Pedestrian Agent Model Using Relative Coordinate Systems. Nagoya Institute of Technology, Gokiso, Showa, Nagoya, Japan.Google Scholar
  9. Lee J. & Lam W. (2008). Simulating pedestrian movements at signalized crosswalks in Hong Kong. Transportation Research Part A: Policy and Practice, Vol 42, 1314–1325.CrossRefGoogle Scholar
  10. Nagatani, T. (2005). Chaos and dynamical transition of a single vehicle induced by traffic light and speedup. Statistical Mechanics and its Applications Vol 348, 561–571.CrossRefGoogle Scholar
  11. Nikiforov AM (1994) Algorithm AS 288: Exact two-sample Smirnov test for arbitrary distributions. Applied statistics, 43(1):265–270CrossRefGoogle Scholar
  12. Ozaki H. (1993). Reaction and anticipation in the car following behavior. Proceedings of the 12th International Symposium on the Theory of Traffic Flow and Transportation, 349–366.Google Scholar
  13. Roess R.P. Prassas E.S. & McShane W.E. (2004) Traffic Engineering. Prentice Hall.Google Scholar
  14. Shao, W. & Terzopoulos, D. (2005). Autonomous pedestrians. In Proceedings of the 2005 ACM Siggraph/Eurographics Symposium on Computer.Google Scholar
  15. Toledo T. (2003). Integrated Driving Behavior Modeling. MIT PhDGoogle Scholar
  16. Wastavino, L. Toledo, B. Rogan, J. Zarama, R. Munoz, V & Valdivia, J. (2007). Modeling traffic on crossroads. Statistical Mechanics and its Applications Vol 381, 411–419.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Universidad de los AndesBogotáColombia

Personalised recommendations