Arterial travel time estimation based on vehicle re-identification using wireless magnetic sensors Karric Kwong a,1 , Robert Kavaler a,1 , Ram Rajagopal b,2 , Pravin Varaiya b, * a Sensys Networks, Inc., 2560 Ninth Street, Berkeley, CA 94710, United States b Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720-1700, United States article info Article history: Received 24 July 2008 Received in revised form 1 April 2009 Accepted 2 April 2009 Keywords: Real-time travel time estimation Vehicle re-identification Arterial performance measures Queue length Discharge rate Magnetic signature abstract A practical system is described for the real-time estimation of travel time across an arterial segment with multiple intersections. The system relies on matching vehicle signatures from wireless sensors. The sensors provide a noisy magnetic signature of a vehicle and the precise time when it crosses the sensors. A match (re-identification) of signatures at two locations gives the corresponding travel time of the vehicle. The travel times for all matched vehicles yield the travel time distribution. Matching results can be processed to provide other important arterial performance measures including capacity, volume/capac- ity ratio, queue lengths, and number of vehicles in the link. The matching algorithm is based on a statistical model of the signatures. The statistical model itself is estimated from the data, and does not require measurement of ‘ground truth’. The procedure does not require measurements of signal settings; in fact, signal settings can be inferred from the matched vehicle results. The procedure is tested on a 1.5 km (0.9 mile)-long segment of San Pablo Avenue in Albany, CA, under different traffic conditions. The segment is divided into three links: one link spans four intersections, and two links each span one intersection. Ó 2009 Elsevier Ltd. All rights reserved. 1. Introduction and previous work Estimating arterial travel time is difficult. Since the movement of vehicles is interrupted by signals, estimates based on speeds measured by loop detectors or radar are inaccurate. Approaches for estimating travel times on arterial links include speed vs. volume to capacity ratio relationships or procedures based on the Highway Capacity Manual. The latter calculates average travel time as the sum of the running time, based on arterial design characteristics, and the intersection delay, based on a deterministic point delay model. These approaches are not suited for real-time applications with variable traffic conditions. Statistical models have been proposed for estimating travel times from surveillance data. For example, Zhang (1999) esti- mates link-speed as a function of volume to capacity ratio and volume and occupancy measured by loop detectors. Since the estimation itself requires collection of travel times, the model is site-specific and impractical to implement. By contrast, Skabardonis and Geroliminis (2005) develop a generally applicable kinematic wave model to construct a link travel time estimate from 30-s flow and occupancy data collected at an upstream loop detector, together with the exact 0968-090X/$ - see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.trc.2009.04.003 * Corresponding author. Tel.: +1 510 642 5270; fax: +1 510 642 1785. E-mail addresses: karric@sensysnetworks.com (K. Kwong), kavaler@sensysnetworks.com (R. Kavaler), ramr@eecs.berkeley.edu (R. Rajagopal), varaiya@eecs.berkeley.edu (P. Varaiya). 1 Tel.: +1 510 548 4620; fax: +1 510 548 8264. 2 Tel.: +1 510 642 5270; fax: +1 510 642 1785. Transportation Research Part C 17 (2009) 586–606 Contents lists available at ScienceDirect Transportation Research Part C journal homepage: www.elsevier.com/locate/trc