Constantinos Antoniou, Ramachandran Balakrishna, Haris N. Koutsopoulos, and Moshe Ben-Akiva
[1] M. Ben-Akiva, M. Bierlaire, H.N. Koutsopoulos, R. Misha-lani, M. Gendreau, and P. Marcotte (Eds.), Real-time sim-ulation of traffic demand-supply interactions within Dyna-MIT, Transportation and network analysis: current trends(Boston/Dordrecht/London: Kluwer Academic Publishers,2002), Miscellenea in honor of Michael Florian, 19–36. [2] M. Ben-Akiva, H.N. Koutsopoulos, C. Antoniou, and R. Bal-akrishna, Fundamentals of traffic simulation, Traffic simula-tion with DynaMIT, in Barcelo, J. (Ed.), Springer, New York,2010, 363–398. [3] H.S. Mahmassani, Dynamic network traffic assignment andsimulation methodology for advanced system managementapplications, Networks and Spacial Economics, 1(3), 2001,267–292. [4] K. Ashok, Estimation and prediction of time–dependent origin–destination flows, PhD thesis, Massachusetts Institute of Tech-nology, 1996. [5] D. Darda, Joint calibration of a microscopic traffic simulatorand estimation of origin-destination flows, Master’s thesis,Massachusetts Institute of Technology, 2002. [6] B.P. Mahanti, Aggregate calibration of microscopic trafficsimulation models, Master’s thesis, Massachusetts Institute ofTechnology, 2004. [7] R. Balakrishna, H.N. Koutsopoulos, and M. Ben-Akiva, H.S.Mahmassani, (Eds) 16th Int. Symp. Transportation and TrafficTheory, chapter calibration and validation of dynamic trafficassignment systems, 407–426 (University of Maryland, CollegePark, 2005). ISBN: 0-08-044680-9. [8] S. Sundaram, Development of a dynamic traffic assignmentsystem for short–term planning applications, Master’s thesis,Massachusetts Institute of Technology, 2002. [9] K. Ashok and M. Ben-Akiva, Alternative approaches for real–time estimation and prediction of time–dependent origin–destination flows, Transportation Science, 34(1), 2000, 21–36. [10] M.L. Hazelton, Estimation of origin-destination matrices fromlink flows on uncongested networks, Transportation Research,34B, 2000, 549–566. [11] E. Cascetta and M.N. Postorino, Fixed point approaches to theestimation of o/d matrices using traffic counts on congestednetworks, Transportation Science, 35(2), 2001, 134–147. [12] M. Scott Ramming, Network knowledge and route choice,PhD thesis, Massachusetts Institute of Technology, Cambridge,2001. [13] L. Leclercq, Calibration of flow-density relationships in urbanstreets, Proc. 84th Annual Meeting of the TransportationResearch Board, Washington, D.C., 2005. [14] M. van Aerde and H. Rakha. Travtek evaluation modelingstudy, Technical report, Federal Highway Administration, USDOT, 1995. [15] K. Kunde, Calibration of mesoscopic traffic simulation modelsfor dynamic traffic assignment, Master’s thesis, MassachusettsInstitute of Technology, 2002. [16] J.C. Spall, Implementation of the simultaneous perturbationalgorithm for stochastic optimization, IEEE Transactions onAerospace and Electronic Systems, 34(3), 1998, 817–823. [17] A. Gupta, Observability of origin-destination matrices fordynamic traffic assignment, Master’s thesis, MassachusettsInstitute of Technology, 2005. [18] E. Cascetta and F. Russo, Calibrating aggregate travel de-mand models with traffic counts: estimators and statisticalperformance, Transportation, 24, 1997, 271–293. [19] T. Toledo, H.N. Koutsopoulos, A. Davol, M.E. Ben-Akiva,W. Burghout, I. Andreasson, T. Johansson, and C. Lundin,Calibration and validation of microscopic traffic simulationtools: Stockholm case study, Transportation Research Record,1831, 2003, 65–75. [20] R. Balakrishna, H.N. Koutsopoulos, and M. Ben-Akiva, Si-multaneous offline demand and supply calibration of dynamictraffic assignment systems, Proc. 85th Annual Meeting of theTransportation Research Board, Washington, D.C., 2006. [21] R. Balakrishna, M. Ben-Akiva, and H.N. Koutsopoulos, Off–line calibration of dynamic traffic assignment: simultaneous de-mand and supply estimation, Transportation Research Record,2003, 2007, 50–58. [22] R. Balakrishna, M. Ben-Akiva, and H.N. Koutsopoulos, Time-dependent origin-destination estimation without assignmentmatrices, Second Int. Symp. of Transport Simulation (ISTS06),Lausanne, Switzerland, 2006. [23] H. Tavana and H. Mahmassani, Estimation and applicationof dynamic speed-density relations by using transfer functionmodels, Transportation Research Record, 1710, 2000, 47–57. [24] N. Huynh, H. Mahmassani, and H. Tavana, Adaptive speedestimation using transfer function models for real-time dynamictraffic assignment operation, Proc. 81st Annual Meeting of theTransportation Research Board, Washington, D.C., 2002. [25] X. Qin and H. Mahmassani, Adaptive calibration of dynamicspeed-density relations for online network traffic estimation232and prediction applications, Proc. 83rd Annual Meeting of theTransportation Research Board, Washington, D.C., 2004. [26] C. Antoniou, M. Ben-Akiva, and H.N. Koutsopoulos, On-line calibration of traffic prediction models, TransportationResearch Record: Journal of the Transportation ResearchBoard, 1934, 2005, 235–245. [27] Y. Wang and M. Papageorgiou, Real-time freeway trafficstate estimation based on Extended Kalman Filter: a generalapproach, Transportation Research Part B, 39, 2005, 141–167. [28] Y. Wang, M. Papageorgiou, and A. Messmer, Real-time freewaytraffic state estimation based on Extended Kalman Filter: acase study, Transportation Science, 41, 2007, 167–181. [29] Y. Wang, M. Papageorgiou, A. Messmer, P. Coppola, A. Tzim-itsi, and A. Nuzzolo, An adaptive freeway traffic state estima-tor, Automatica, 45(1), 2009, 10–24. [30] K. Ashok and M. Ben-Akiva, Dynamic O.D. matrix estimationand prediction for real-time traffic management systems, inC. Daganzo (Ed.), Transportation and traffic theory, 465–484(Oxford: Elsevier Science Publishing, 1993). [31] C. Antoniou, Demand simulation for dynamic traffic assign-ment, Master’s thesis, Massachusetts Institute of Technology,1997. [32] M. Bierlaire and F. Crittin, An efficient algorithm for real–timeestimation and prediction of dynamic OD tables, OperationsResearch, 52(1), 2004, 116–127. [33] X. Zhou and H.S. Mahmassani, A structural state space modelfor real–time origin–destination demand estimation and predic-tion in a day–to–day updating framework, Proc. 83rd AnnualMeeting of the Transportation Research Board, Washington,D.C., 2004. [34] C. Antoniou, On-line calibration for dynamic traffic assignment,PhD thesis, Massachusetts Institute of Technology, Cambridge,2004. [35] C. Antoniou, M. Ben-Akiva, and H.N. Koutsopoulos, Non–linear Kalman filtering algorithms for on–line calibration ofdynamic traffic assignment models, IEEE Transactions onIntelligent Transportation Systems, 8(4), 2007, 661–670. [36] R. Balakrishna, Off-line calibration of dynamic traffic assign-ment models, Ph.D. thesis, Massachusetts Institute of Tech-nology, Cambridge, 2006. [37] C. Antoniou, M. Ben-Akiva, and H.N. Koutsopoulos, Incorpo-rating automated vehicle identification into origin–destinationestimation, Transportation Research Record: Journal of theTransportation Research Board, Washington, D.C., 2004, 1882,37–44. [38] C. Antoniou, M. Ben-Akiva, and H.N. Koutsopoulos, Dynamictraffic demand prediction using conventional and emergingdata sources, IEE Proceedings in Intelligent Transport Systems,153(1), 2006, 97–104. [39] E. Cascetta, D. Inaudi, and G. Marquis, Dynamic estimators oforigin-destination matrices using traffic counts, TransportationScience, 27(4), 1993, 363–373. [40] U.C.Berkeley and Caltrans, Freeway performance measurementsystem (PeMS) 5.4, 2005, ://pems.eecs.berkeley.edu/Public,Accessed: 30 June 2005. [41] R.E. Kalman, A new approach to linear filtering and predictionproblems, Journal of Basic Engineering (ASME), 82D, 1960,35–45. [42] C.K. Chui and G. Chen, Kalman filtering with real-timeapplications (Springer-Verlag, New York, 1999). [43] C. Antoniou, B. Nowotny, A. Rechbauer, and M. Linauer,Calibration of DTA models using floating car data: an appli-cation of DynaMIT in Vienna, Proc. 2nd ISTS Symposium,Lausanne, Switzerland, 2006.
Important Links:
Go Back