Dynamic OD matrix estimation exploiting bluetooth data in Urban networks

Time-Dependent Origin-Destination (OD) matrices are a key input to Dynamic Traffic Models. Microscopic and Mesoscopic traffic simulators are relevant examples of such models, traditionally used to assist in the design and evaluation of Traffic Management and Information Systems (ATMS/ATIS). Dynamic traffic models can also be used to support real-time traffic management decisions. The typical approaches to time-dependent OD estimation have been based either on Kalman-Filtering or on bi-level mathematical programming approaches that can be considered in most cases as ad hoc heuristics. The advent of the new Information and Communication Technologies (ICT) makes available new types of traffic data with higher quality and accuracy, allowing new modeling hypotheses which lead to more computationally efficient algorithms. This paper presents a Kalman Filtering approach, that explicitly exploit traffic data available from Bluetooth sensors, and reports computational experiments for networks and corridors.

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

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

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

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

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

[6]  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..

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

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

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

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

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

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

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

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

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