Computing travel times from filtered traffic states

Pierre-Emmanuel Mazaré; Olli-Pekka Tossavainen; Daniel B. Work

doi:10.3934/dcdss.2014.7.557

Article Contents

2014, Volume 7, Issue 3: 557-578. Doi: 10.3934/dcdss.2014.7.557

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Computing travel times from filtered traffic states

1.
Telenav, Inc, 950 De Guigne Dr, Sunnyvale, CA 94085
2.
Microsoft, 1065 La Avenida St, Mountain View, CA 94043
3.
University of Illinois at Urbana-Champaign, 1203 Newmark Civil Engineering Laboratory, 205 N. Mathews Ave, Urbana, IL 61801

Received: June 30, 2013

Revised: July 31, 2013

Published: May 2014

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Abstract

This article experimentally assesses the influence of sensor data rates on travel time estimates computed from filtered traffic speed estimates. Using velocity data obtained from GPS smartphones and inductive loop detector data collected during the Mobile Century experiment near Berkeley, CA, and an evolution equation for average velocity along the roadway, an estimate of the traffic state is obtained via ensemble Kalman filtering. A large--scale batch of computations is run to produce estimates of traffic velocity with varying degrees of input data, and instantaneous and a posteriori dynamic travel times are compared to travel times recorded using license plate re-identification. We illustrate that dynamic travel time estimates can be computed with less than 10% error regardless of the data source, and that existing inductive loop detector data can significantly improve the accuracy of travel time estimates when GPS data is sparse.

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Mathematics Subject Classification: Primary: 90B20, 35L65; Secondary: 62L12.

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