DynaMIT

Real time simulation of traffic demand-supply interactions within DynaMIT DynaMIT is a simulation-based real-time system designed to estimate the current state of a transportation network, predict future traffic conditions, and provide consistent and unbiased information to travelers. To perform these tasks, efficient simulators have been designed to explicitly capture the interactions between transportation demand and supply. The demand reflects both the OD flow patterns and the combination of all the individual decisions of travelers while the supply reflects the transportation network in terms of infrastructure, traffic flow and traffic control. This paper describes the design and specification of these simulators, and discusses their interactions


References in zbMATH (referenced in 13 articles , 1 standard article )

Showing results 1 to 13 of 13.
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  1. Koch, Ronald; Skutella, Martin: Nash equilibria and the price of anarchy for flows over time (2011)
  2. Russo, Francesco; Vitetta, Antonino: Reverse assignment: calibrating link cost functions and updating demand from traffic counts and time measurements (2011)
  3. Zheng, Qipeng P.; Arulselvan, Ashwin: Discrete time dynamic traffic assignment models and solution algorithm for managed lanes (2011)
  4. Zhang, Xiaoning; Zhang, H.M.: Simultaneous departure time/route choices in queuing networks and a novel paradox (2010)
  5. Liu, Henry X.; He, Xiaozheng; He, Bingsheng: Method of successive weighted averages (MSWA) and self-regulated averaging schemes for solving stochastic user equilibrium problem (2009)
  6. Florian, Michael; Mahut, Michael; Tremblay, Nicolas: Application of a simulation-based dynamic traffic assignment model (2008)
  7. Beuck, Ulrike; Nagel, Kai; Rieser, Marcel; Strippgen, David; Balmer, Michael: Preliminary results of a multiagent traffic simulation for Berlin (2007)
  8. van Woensel, Tom; Vandaele, Nico: Modeling traffic flows with queueing models: a review (2007)
  9. Chrobok, R.; Kaumann, O.; Wahle, J.; Schreckenberg, M.: Different methods of traffic forecast based on real data. (2004)
  10. Magnanti, Thomas L.; Perakis, Georgia: Solving variational inequality and fixed point problems by line searches and potential optimization (2004)
  11. Ben-Akiva, Moshe; Bierlaire, Michel; Koutsopoulos, Haris N.; Mishalani, Rabi: Real time simulation of traffic demand-supply interactions within DynaMIT (2002)
  12. Tremblay, N.; Florian, M.: Temporal shortest paths: Parallel computing implementations (2001)
  13. Wahle, J.; Bazzan, A.L.C.; Klügl, F.; Schreckenberg, M.: Anticipatory traffic forecast using multi-agent techniques (2000)