Drawing from the literature on the minority game, this study proposes that predictive travel time information provided to drivers to assist with their route choices may not influence network flow; under the condition that route choices are made repeatedly by the same group of drivers, the drivers self-organize to reach the same network equilibrium regardless of the contents of the predictive travel time information given. A numerical example supports this conjecture by showing that improvements in network flow are negligibly small even when precise information on travel time is provided. If no predictive information is present, the results of agent-based route choice simulation studies indicate that higher levels of the cognitive capabilities of drivers tend to produce more self-organization and stable network flows and that information on those routes a driver did not take is important for this self-organization.
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References
1.
Ben-AkivaM.de PalmaA. and KaysiI.. Dynamic Network Models and Driver Information Systems. Transportation Research A, Vol. 25, No. 51991, pp. 251–266.
2.
AdlerJ. L. and McNallyM. G.. In-Laboratory Experiments to Investigate Driver Behavior Under Advanced Traveler Information Systems. Transportation Research C, Vol. 2, No. 31994, pp. 149–164.
3.
SrinivasanK. K. and MahmassaniH. S.. Role of Congestion and Information in Trip-Makers’ Dynamic Decision Processes: Experimental Investigation. In Transportation Research Record: Journal of the Transportation Research Board, No. 1676, TRB, National Research Council, Washington, D.C., 1999, pp. 44–52.
4.
ArnottR.de PalmaA. and LindseyR.. Does Providing Information to Drivers Reduce Traffic Congestion?Transportation Research A, Vol. 25, No. 51991, pp. 309–318.
5.
Ben-AkivaM. and de PalmaA.. Dynamic Network Models and Driver Information Systems. Presented at U.S.–Italy Joint Seminar on Urban Traffic Networks, Naples, Italy, 1989.
6.
MahmassaniH. S. and HermanR.. Interactive Experiments for the Study of Tripmaker Behavior Dynamics in Congested Commuting Systems. In New Developments in Dynamic and Activity-Based Approaches (JonesP., ed.), Avebury, Aldershot, Hampshire, United Kingdom, 1990, pp. 272–298.
7.
EmmerinkR. H. M.AxhausenK. W.NijkampP. and RietveldP.. Effects of Information in Road Transport Networks with Recurrent Congestion. Transportation, Vol. 22, No. 11995, pp. 21–52.
8.
MahmassaniH. S. and JayakrishnanR.. System Performance and User Response Under Real-Time Information in Congested Traffic Corridor. Transportation Research A, Vol. 25, No. 51991, pp. 293–307.
9.
KoutsopoulosH. N. and XuH.. An Information Discounting Routing Strategy for Advanced Traveler Information Systems. Transportation Research C, Vol. 1, No. 31993, pp. 249–264.
10.
HorowitzJ. L.The Stability of Stochastic Equilibrium in a Two-Link Transportation Network. Transportation Research B, Vol. 18, 1984, pp. 13–28.
11.
Ben-AkivaM.de PalmaA. and KanaroglouP.. Dynamic Model of Peak Period Traffic Congestion with Elastic Arrival Rates. Transportation Science, Vol. 20, No. 21986, pp. 164–181.
12.
MahmassaniH. S. and ChangG.-L.. Experiments with Departure Time Choice Dynamics of Urban Commuters. Transportation Research B, Vol. 20, 1986, pp. 297–320.
13.
IidaY.AkiyamaT. and UchidaT.. Experimental Analysis of Dynamic Route Choice Behavior. Transportation Research B, Vol. 26, No. 11992, pp. 17–32.
14.
VaughnK. M.Abdel-AtyM. A.KitamuraR.JovanisP. P.YangH.KrollN. E. A.PostR. B. and OppyB.. Experimental Analysis and Modeling of Sequential Route Choice Under Advanced Traveler Information System in a Simplistic Traffic Network. In Transportation Research Record 1408, TRB, National Research Council, Washington, D.C., 1993, pp. 75–82.
15.
CantarellaG. E. and CascettaE.. Dynamic Processes and Equilibrium in Transportation Networks: Towards a Unifying Theory. Transportation Science, Vol. 29, 1995, pp. 305–329.
16.
ChangG.-L. and MahmassaniH. S.. Travel Time Prediction and Departure Time Adjustment Behavior Dynamics in a Congested Traffic System. Transportation Research B, Vol. 22, No. 31988, pp. 217–232.
17.
IidaY.UchidaT. and UnoN.. Experimental Analysis on the Mechanism of Commuters’ Travel Time Prediction (in Japanese). Proc., Annual Meeting of the JSCE Infrastructure Planning Division, 1990, pp. 335–342.
18.
TongC.-C.MahmassaniH. S. and ChangG. L.. Travel Time Prediction and Information Availability in Commuter Behavior Dynamics. In Transportation Research Record 1138, TRB, National Research Council, Washington, D.C., 1987, pp. 1–7.
19.
KitamuraR.KikuchiA. and UshiwakaK.. Travel Time Bandwidth as a Measure of Perceived Travel Time Variability: Empirical Analysis of Its Effectiveness. Presented at 85th Annual Meeting of the Transportation Research Board, Washington, D.C., 2006.
20.
NakayamaS. and KitamuraR.. Route Choice Model with Inductive Learning. In Transportation Research Record: Journal of the Transportation Research Board, No. 1725, TRB, National Research Council, Washington, D.C., 2000, pp. 63–70.
21.
ArthurW. B.Inductive Reasoning and Bounded Rationality. American Economic Review, Vol. 84, No. 21994, pp. 406–411.
22.
ChalletD. and ZhangY.-C.. Emergence of Cooperation and Organization in an Evolutionary Game. Physica A, Vol. 246, 1997, pp. 407–418.
23.
PlatkowskiT. and RamszaM.. Playing Minority Game. Physica A, Vol. 323, 2003, pp. 726–734.
24.
CavagnaA.Irrelevance of Memory in the Minority Game. Physical Review E, Vol. 59, No. 4, 1999, pp. R3783–R3786.
25.
NakayamaS. A Stochastic User Equilibrium Model with Stochastic Demand. Presented at 4th IMA International Conference on Mathematics in Transport, London, 2005.
