A Kalman Filter Approach for Exploiting Bluetooth Traffic Data When Estimating Time-Dependent OD Matrices

Time-dependent origin–destination (OD) matrices are essential input for dynamic traffic models such as microscopic and mesoscopic traffic simulators. Dynamic traffic models also support real-time traffic management decisions, and they are traditionally used in the design and evaluation of advanced traffic traffic management and information systems (ATMS/ATIS). Time-dependent OD estimations are typically based either on Kalman filtering or on bilevel mathematical programming, which can be considered in most cases as ad hoc heuristics. The advent of the new information and communication technologies (ICT) provides new types of traffic data with higher quality and accuracy, which in turn allows new modeling hypotheses that lead to more computationally efficient algorithms. This article presents ad hoc, Kalman filtering procedures that explicitly exploit Bluetooth sensor traffic data, and it reports the numerical results from computational experiments performed at a network test site.

[1]  Henk J van Zuylen,et al.  The most likely trip matrix estimated from traffic counts , 1980 .

[2]  Alexandre M. Bayen,et al.  An ensemble Kalman filtering approach to highway traffic estimation using GPS enabled mobile devices , 2008, 2008 47th IEEE Conference on Decision and Control.

[3]  Kay W. Axhausen,et al.  Optimization of Large Transport Networks Using the Ant Colony Heuristic , 2009, Comput. Aided Civ. Infrastructure Eng..

[4]  Keechoo Choi,et al.  Dynamic Origin–Destination Estimation Using Dynamic Traffic Simulation Model in an Urban Arterial Corridor , 2009 .

[5]  Martin Treiber,et al.  Reconstructing the Traffic State by Fusion of Heterogeneous Data , 2009, Comput. Aided Civ. Infrastructure Eng..

[6]  Jaume Barceló,et al.  Adjustment of O-D trip matrices from observed volumes: An algorithmic approach based on conjugate directions , 2004, Eur. J. Oper. Res..

[7]  E. Cascetta.  Estimation of trip matrices from traffic counts and survey data: A generalized least squares estimator , 1984 .

[8]  Serge P. Hoogendoorn,et al.  A Robust and Efficient Method for Fusing Heterogeneous Data from Traffic Sensors on Freeways , 2010, Comput. Aided Civ. Infrastructure Eng..

[9]  Oriol Serch,et al.  Exploring Link Covering and Node Covering Formulations of Detection Layout Problem , 2012 .

[10]  J. Barceló,et al.  TRAVEL TIME FORECASTING AND DYNAMIC OD ESTIMATION IN FREEWAYS BASED ON BLUETOOTH TRAFFIC MONITORING , 2009 .

[11]  James V. Krogmeier,et al.  Estimation of Dynamic Assignment Matrices and OD Demands Using Adaptive Kalman Filtering , 2001, J. Intell. Transp. Syst..

[12]  Moshe E. Ben-Akiva,et al.  Alternative Approaches for Real-Time Estimation and Prediction of Time-Dependent Origin-Destination Flows , 2000, Transp. Sci..

[13]  Hai Yang,et al.  Optimal traffic counting locations for origin–destination matrix estimation , 1998 .

[14]  Laurence R. Rilett,et al.  Real‐Time OD Estimation Using Automatic Vehicle Identification and Traffic Count Data , 2002 .

[15]  Enrique F. Castillo,et al.  The Observability Problem in Traffic Network Models , 2008, Comput. Aided Civ. Infrastructure Eng..

[16]  Michael Florian,et al.  A hybrid optimization-mesoscopic simulation dynamic traffic assignment model , 2001, ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585).

[17]  Lídia Montero Mercadé,et al.  The detection layout problem , 2012 .

[18]  Bin Ran,et al.  Perspectives on Future Transportation Research: Impact of Intelligent Transportation System Technologies on Next-Generation Transportation Modeling , 2012, J. Intell. Transp. Syst..

[19]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[20]  Anders Peterson,et al.  A heuristic for the bilevel origin-destination-matrix estimation problem , 2008 .

[21]  Carlos Carmona,et al.  Travel Time Forecasting and Dynamic Origin-Destination Estimation for Freeways Based on Bluetooth Traffic Monitoring , 2010 .

[22]  Lídia Montero Mercadé,et al.  A Kalman-filter approach for dynamic OD estimation in corridors based on bluetooth and Wi-Fi data collection , 2010 .

[23]  Gang-Len Chang,et al.  A generalized model and solution algorithm for estimation of the dynamic freeway origin-destination matrix , 2007 .

[24]  Rudi Hamerslag,et al.  IMPROVED KALMAN FILTERING APPROACH FOR ESTIMATING ORIGIN-DESTINATION MATRICES FOR FREEWAY CORRIDORS , 1994 .

[25]  Gang-Len Chang,et al.  Recursive estimation of time-varying origin-destination flows from traffic counts in freeway corridors , 1994 .

[26]  Haris N. Koutsopoulos,et al.  Non-linear kalman filtering algorithms for on-line calibration of dynamic traffic assignment models , 2006, ITSC.

[27]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[28]  Naomi Ehrich Leonard,et al.  Proceedings of the 47th IEEE Conference on Decision and Control , 2008, CDC 2008.

[29]  N. Geroliminis,et al.  An analytical approximation for the macropscopic fundamental diagram of urban traffic , 2008 .

[30]  Hani S. Mahmassani,et al.  Dynamic origin-destination demand estimation using automatic vehicle identification data , 2006, IEEE Transactions on Intelligent Transportation Systems.

[31]  Haris N. Koutsopoulos,et al.  Nonlinear Kalman Filtering Algorithms for On-Line Calibration of Dynamic Traffic Assignment Models , 2006, IEEE Transactions on Intelligent Transportation Systems.