A Robust Framework for the Estimation of Dynamic OD Trip Matrices for Reliable Traffic Management

Origin-Destination (OD) trip matrices describe the patterns of traffic behavior across the network and play a key role as primary data input to many traffic models. OD matrices are a critical requirement, either in static or dynamic models for traffic assignment. However, OD matrices are not yet directly observable; thus, the current practice consists of adjusting an initial or a priori matrix from link flow counts, speeds, travel times and other aggregate demand data. This information is provided by an existing layout of traffic counting stations, as the traditional loop detectors. The availability of new traffic measurements provided by Information and Communications Technologies (ICT) applications offers the possibility to formulate and develop more efficient algorithms, especially suited for real-time applications. However, the efficiency strongly depends, among other factors, on the quality of the seed matrix. This paper proposes an integrated computational framework in which an off-line procedure generates the time-sliced OD matrices, which are the input to an on-line estimator. The paper also analyzes the sensitivity of the on-line estimator with respect to the available traffic measurements.

[1]  H Speiss,et al.  Modelling the daily traffic flows on an hourly basis , 1991 .

[2]  Oriol Serch,et al.  ROBUSTNESS AND COMPUTATIONAL EFFICENCY OF A KALMAN FILTER ESTIMATOR OF TIME DEPENDENT OD MATRICES EXPLOITING ICT TRAFFIC MEASUREMENTS , 2013 .

[3]  Jaume Barceló,et al.  A Kalman Filter Approach for the Estimation of Time Dependent OD Matrices Exploiting Bluetooth Traffic Data Collection , 2012 .

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

[5]  Srinivas Peeta,et al.  Identification of vehicle sensor locations for link-based network traffic applications , 2009 .

[6]  Ennio Cascetta,et al.  Transportation Systems Engineering: Theory and Methods , 2001 .

[7]  Lídia Montero Mercadé,et al.  A DUE based bilevel optimization approach for the estimation of time sliced OD matrices , 2014 .

[8]  Tamara Djukic,et al.  Advanced traffic data for dynamic OD demand estimation: The , 2015 .

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

[10]  Haris N. Koutsopoulos,et al.  Incorporating Automated Vehicle Identification Data into Origin-Destination Estimation , 2004 .

[11]  Oriol Serch,et al.  Robustness and Computational Efficiency of Kalman Filter Estimator of Time-Dependent Origin–Destination Matrices , 2013 .

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

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

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

[16]  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.

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