International Journal of Automotive Technology

The Korean Society of Automotive Engineers (한국자동차공학회)
권호 표지

DEVELOPMENT OF MATDYMO (MULTI-AGENT FOR TRAFFIC SIMULATION WITH VEHICLE DYNAMICS MODEL) I: DEVELOPMENT OF TRAFFIC ENVIRONMENT

  • CHOI K. Y. (Graduate School of Mechanical Engineering, Sungkyunkwan University) ;
  • KWON S. J. (Graduate School of Mechanical Engineering, Sungkyunkwan University) ;
  • SUH M. W. (School of Mechanical Engineering, Sungkyunkwan University)
  • Published : 2006.02.01
Download PDF
⟨ Previous Next ⟩

Abstract

For decades, simulation technique has been well validated in areas such as computer and communication systems. Recently, the technique has been much used in the area of transportation and traffic forecasting. Several methods have been proposed for investigating complex traffic flows. However, the dynamics of vehicles and diversities of driver characteristics have never been considered sufficiently in these methods, although they are considered important factors in traffic flow analysis. In this paper, we propose a traffic simulation tool called Multi-Agent for Traffic Simulation with Vehicle Dynamics Model (MATDYMO). Road transport consultants, traffic engineers and urban traffic control center managers are expected to use MATDYMO to efficiently simulate traffic flow. MATDYMO has four sub systems: the road management system, the vehicle motion control system, the driver management system, and the integration control system. The road management system simulates traffic flow for various traffic environments (e.g., multi-lane roads, nodes, virtual lanes, and signals); the vehicle motion control system constructs the vehicle agent by using various vehicle dynamic models; the driver management system constructs the driver agent capable of having different driving styles; and lastly, the integrated control system regulates the MATDYMO as a whole and observes the agents running in the system. The vehicle motion control system and driver management system are described in the companion paper. An interrupted and uninterrupted flow model were simulated, and the simulation results were verified by comparing them with the results from a commercial software, TRANSYT-7F. The simulation result of the uninterrupted flow model showed that the driver agent displayed human-like behavior ranging from slow and careful driving to fast and aggressive driving. The simulation of the interrupted flow model was implemented as two cases. The first case analyzed traffic flow as the traffic signals changed at different intervals and as the turning traffic volume changed. Second case analyzed the traffic flow as the traffic signals changed at different intervals and as the road length changed. The simulation results of the interrupted flow model showed that the close relationship between traffic state change and traffic signal interval.

Keywords

References

  1. Aerde, M. and Yager, S. (1988). Dynamic integrated freeway/traffic signal networks: Problems and proposed solutions. Transportation Research Part A, 22, 435-443 https://doi.org/10.1016/0191-2607(88)90047-7
  2. Akiva, M., Bierlaire, M., Koutsopoulous, H. and Mishalani, R. (1998). A simulation-based system for traffic prediction. Paper presented at TRISTAN III, San Juan, Porto Rico
  3. Arentze, T. A. and Timmermans, H. J. P. (2005). Information gain, novelty seeking and travel: a model of dynamic activity-travel behavior under conditions of uncertainty. Transportation Research Part A, 39, 2, 125-145 https://doi.org/10.1016/j.tra.2004.08.002
  4. Aycin, M. F. and Benekohal, R. F. (1998). Linear acceleration car-following model development and validation. Transportation Research Record 1644, National Research Council, Washington, DC, 10-19
  5. Chan, Y. (1974). Configuring a transportation route network via the method of successive approximation. Computers & Operations Research 1, 3, 385-420 https://doi.org/10.1016/0305-0548(74)90060-4
  6. Cho, K. Y., Kwon, S. J. and Suh, M. W. (2005). Vehicle dynamics approach to multi-agent for traffic simulation. II: Development of vehicle and driver agent. Int. J. Automotive Technology, Accepted
  7. Drake, J. S., Schofer, J. L. and May, A. D. (1967). A statistical analysis of speed density hypothesis. Highway Research Record, 154, 53-87
  8. Elmarakbi, A. M. and Zu, J. W. (2004). Dynamic modeling and analysis of vehicle smart structures for frontal collision improvement. Int. J. Automotive Technology 5, 4, 247-255
  9. Fellendorf, M. (1994). VISSIM: A microscopic simulation tool to evaluate actuated signal control including bus priority. Technical Paper, Session 32, 64th ite Annual Meeting, Dallas
  10. Garcia, O. A., Amin, J. M. and Wootton, R. (1995). Intelligent transportation systems-Enabling technologies. Mathematical and Computer Modelling 22, 4, 11-81
  11. Hernandez, J., Cuena, J. and Molina, M. (1999). Realtime traffic management through knowledge-based models: the TRYS approach, tutorial on intelligent traffic management models. the 11th Mini-Euro Conf. Artificial Intelligence in Transportation Systems and Science, Helsinki, Finland
  12. Hu, T. Y. and Mahmassani, H. S. (1995). Evolution of network flows under real-time information: Day-today dynamic simulation assignment framework. Transportation Research Record, 1493, 46-56
  13. Huddart, K. W. (1969). The importance of stops in traffic signal progressions. Transportation Research 3, 1, 143-150 https://doi.org/10.1016/0041-1647(69)90111-7
  14. Kukla, R., Kerridge, J., Willis, A. and Hine, J. (2001). PEDFLOW: Development of an autonomous agent model of pedestrian flow. Transportation Research Record No. 1774 Transportation Research Board, 1117
  15. Kuri, J., Puech, N., Gagnaire, M. and Dotaro, E. (2002). Routing foreseeable lightpath demands using a tabu search meta-heuristic. Global Telecommunications Conf., 2803-2807
  16. Lin, M. T. and Gan, A. C. (1999). Signal timing optimization for oversaturated networks using TRANSYT-7F. Transportation Research Record, 1683, 118-126
  17. Maes, P. (1995), Artificial life meets entertainment: life like autonomous agents. Communications of the ACM 38, 11, 108-114
  18. Michalopoulos, P. G. and Pisharody, V. B. (1981). Derivation of delays based on improved macroscopic traffic models. Transportation Research Part B, 15, 5, 299-317 https://doi.org/10.1016/0191-2615(81)90016-3
  19. Michalopoulos, P. G., Beskos, D. E. and Lin, J. K. (1984). Analysis of interrupted traffic flow by finite difference methods. Transportation Research Part B, 18, 4, 409-421 https://doi.org/10.1016/0191-2615(84)90021-3
  20. Nagel, K. and Rickert, M. (2001). Parallel implementation of the TRANSlMS micro-simulation. Parallel Computing, 27, 1611-1639 https://doi.org/10.1016/S0167-8191(01)00106-5
  21. Nakamiti, G. and Freitas, R. (2002). Adaptive, real-time traffic control management. Int. J. Automotive Technology 3, 3, 89-94
  22. Newell, G. F. (1961). Nonlinear effects in the dynamics of car following. Operations Research, 9, 209-229 https://doi.org/10.1287/opre.9.2.209
  23. Noland, R. B. (2001). Relationships between highway capacity and induced vehicle travel. Transportation Research Part A, 35, 1, 47-72
  24. Patrick, M., Sebald, A. V., Smith, N. T. and Quinn, M. L. (1988). An efficient AI based algorithm for validating pulsatile arterial pressure waveforms. Proc. Annual Int. Conf. IEEE, 4-7
  25. Peter, J. (1993). Development in Dynamic and activity-based approaches to travel analysis. Transportation Research Part A, 27, 154-156
  26. Pipes, L. A. (1953). An operational analysis of traffic dynamics. J. Applied Physics 24, 3, 274-281 https://doi.org/10.1063/1.1721265
  27. Polak, J. K. and Huang, X. (1999). Agent-based modeling system for activity-based models. Presented at the 78th Annual Meeting of the Transportation Research Board, Washington, DC
  28. Pue, A. J. (1982). Macroscopic traffic models for vehicle-follower automated transportation systems. Transportation Research Part B, 16, 2, 125-142 https://doi.org/10.1016/0191-2615(82)90031-5
  29. Quadstone. (1999). Paramics Modeler V3.0 Reference Manual. Quadstone Ltd., Edinburgh, UK
  30. Roozemond, D. A. (1999). Using intelligent agents of urban traffic control systems. Proc. 11th Mini-Euro Conf. Artificial Intelligence in Transportation Systems and Science, Helsinki University of Technology, Espoo, Finland
  31. Russell, S. J. and Norvig, P. (1995), Artificial Intelligence: A Modern Approach. Englewood Cliffs. Prentice Hall. NJ. USA
  32. Salvini, A., Fulginei, F. R. and Coltelli, C. (2003). A neuro-genetic and time-frequency approach to maicromodeling dynamic hysteresis in the harmonic regime. IEEE Trans., 39, 1401-1404