METROPOLIS proposes an interactive environment that simulates automobile traffic in large urban areas. The core of the system is a dynamic simulator that integrates commuters’ departure time and route choice behaviors over large networks: Drivers are assumed to minimize a generalized travel cost function that depends on travel time and schedule delay. This simulator is based on a behavioral driver information process. It allows real-time and off-line simulations. The system also includes a scenario builder and a graphical results viewer. The main ideas underlying METROPOLIS are presented, and preliminary computer simulation experiments are discussed for Geneva, Switzerland.
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References
1.
BeckmannM., McGuireC. B., and WinstenC. B.. Studies in the Economics of Transportation. Yale University Press, New Haven, Conn., 1956.
FlorianM., and HearnD.. Network Equilibrium Models and Algorithms. In Network Routing. Handbooks in RO and MS (BallM.O.et al., eds.), Elsevier, 1995, pp. 485–550.
4.
ChinA.Influences on Commuter Trip Departure Time Decisions in Singapore. Transportation Research A, Vol. 24A, No. 5, 1990, pp. 321–333.
5.
Ben-AkivaM., KoutsopoulosH., and MukundanA.. A Dynamic Traffic Model System for ATMS/ATIS Operations. IVHS Journal, Vol. 2, No. 1, 1994, pp. 1–19.
6.
Ben-AkivaM., KoutsopoulosH., MishalaniR., and YangQ.. Integrated Simulation Framework for Evaluating Dynamic Traffic Management Systems. Proc., 1st World Congress on Applications of Transport Telematics and Intelligent Transportation Systems, Paris, 1994.
7.
VickreyW. S.Congestion Theory and Transport Investment. American Economic Review (Papers and Proceedings), Vol. 59, 1969, pp. 251–261.
8.
ArnottR., de PalmaA., and LindseyR.. A Structural Model of Peak-Period Congestion: A Traffic Bottleneck with Elastic Demand. American Economic Review, Vol. 83, No. 1, 1993, pp. 161–179.
9.
ArnottR., de PalmaA., and LindseyR.. Congestion: A Dynamic Approach. MIT Press, Cambridge, Mass., in press.
10.
MahmassaniH., and HermanR.. Dynamic User Equilibrium Departure Time and Route Choice on Idealized Traffic Arterials. Transportation Science, Vol. 18, 1984, pp. 362–384.
11.
Ben-AkivaM., CynaM., and de PalmaA.. Dynamic Model of Peak-Period Congestion. Transportation Research B, Vol. 18, 1984, pp. 339–355.
12.
Ben-AkivaM., de PalmaA., and KanaroglouP.. Dynamic Model of Peak-Period Traffic Congestion with Elastic Arrival Rates. Transportation Science, Vol. 20, 1986, pp. 164–181.
13.
VythoulkasP.Two Models for Predicting Dynamic Stochastic Equilibria in Urban Transportation Networks. Proc., 11th International Symposium on Transportation and Traffic Theory. Elsevier Science, Amsterdam, 1990, pp. 253–272.
14.
LeblancL. J., RanB., and BoyceD. E.. Dynamic Travel Choice Models for Urban Transportation Networks. Proc., 1992 IEEE International Conference on Systems Man and Cybernetics, Vol. 1, 1992, pp. 225–231.
15.
RanB., and BoyceD.. Dynamic Urban Transportation Network Models, Theory and Implications for Intelligent Vehicle-Highway Systems. Springer-Verlag, Berlin, 1994.
16.
HendricksonD., and KocurG.. Schedule Delay and Departure Time Decisions in a Deterministic Model. Transportation Science, Vol. 15, 1981, pp. 62–77.
17.
MerchantD. K., and NemhauserG. L.. A Model and an Algorithm for the Dynamic Traffic Assignment Model. Transportation Science, Vol. 12, 1987, pp. 183–199.
18.
PapageorgiouM.Dynamic Modeling, Assignment, and Route Guidance in Traffic Networks. Transportation Research, Vol. 24B, No. 6, 1990, pp. 471–495.
19.
WieB.-W., TobinR., FrieszT., and BernsteinD.. A Discrete Time, Nested Cost Operator Approach to the Dynamic Network User Equilibrium Problem. Transportation Science, Vol. 29, No. 1, 1995, pp. 79–92.
20.
FrieszT. C., BernsteinD., SmithT. E., TobinR. L., and WieB. W.. A Variational Inequality Formulation of the Dynamic Network User Equilibrium Problem. Operations Research, Vol. 41, 1993, pp. 179–191.
21.
ChenP. S., and MahmassaniH.. Dynamic Interactive Simulator for Studying Commuter Behavior Under Real-Time Traffic Information Supply Strategies. In Transportation Research Record 1413, TRB, National Research Council, Washington, D.C., 1992, pp. 12–21.
22.
WatlingD.Urban Traffic Network Models and Dynamic Driver Information Systems. Transportation Reviews, Vol. 14, No. 3, 1994, pp. 219–246.
23.
ZiliaskopoulosA. K., and MahmassaniH.. A Time-Dependent Shortest-Path Algorithm for Real-Time Intelligent Vehicle/Highway System Applications. In Transportation Research Record 1408, TRB, National Research Council, Washington, D.C., 1993, pp. 94–104.
24.
de PalmaA., and RochatD.. Congestion Urbaine et Comportement des Usagers: Analyse de la Composante Horaire. Revue d’Economie Régionale et Urbaine, Vol. 3, in press.
25.
De PalmaA., and RochatD.. Impact of Adverse Weather Conditions on Travel Decisions: Experience from a Behavioral Survey in Geneva. International Journal of Transport Economics, in press.
26.
KhattakA. J., and de PalmaA.. Commuters Propensity to Change Transportation Decisions in Adverse Travel Conditions: Results from a Behavioral Survey in Brussels. Transportation Research A, 1996.
27.
MahmassaniH. S., and ChangG. L.. On Boundedly-Rational User Equilibrium in Transportation Systems. Transportation Research, Vol. 25A, No. 5, 1987, pp. 293–308.
28.
SmallK.The Scheduling of Consumer Activities: Work Trips. American Economic Review, Vol. 72, 1982, pp. 467–794.
29.
YangQ., and KoutsopoulosH.. A Microscopic Traffic Simulation for Evaluation of Dynamic Traffic Management Systems. Unpublished draft. Department of Civil Engineering, Massachusetts Institute of Technology, Cambridge, 1995.
30.
NagelK.High-Speed Microsimulations of Traffic Flow. Ph.D. thesis. Department of Mathematics, Köln University, Cologne, Germany, 1994.
31.
de PalmaA., and MarchalF.. METROPOLIS: Un Outil de Simulation du Trafic Urbain. Revue Transports, Vol. 378, July1996.
32.
ChoppartB., LuthiP. O., and QuelozP.-A.. Cellular Automata Model of Car Traffic in a Two-Dimensional Street Network. Journal of Physics, Vol. 29A, 1996, pp. 1–12.
