A Methodology for Estimating Vehicle Route Choice from Sparse Flow Measurements in a Traffic Network

Alex A. Kurzhanskiy
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Alex A. Kurzhanskiy: California Partners for Advanced Transportation Technology, University of California, Berkeley, CA 94720, USA

Mathematics, 2022, vol. 10, issue 3, 1-11

Abstract: While traffic speed data and travel time estimates are increasingly more available from commercial vendors, they are not sufficient for proper management and performance evaluation of transportation networks. Effective traffic control and demand management requires information about volumes, which is provided by fixed location sensors, such as loop detectors or cameras, and those are sparse. This paper proposes a method for estimating route choice using sparse flow measurements and estimated speed on the road network based on compressed sensing technology widely used in image processing, where from a handful of scattered pixels, a full image is recovered. What is known includes flows at origins and at selected links of the road network, where the detection is present; speed estimates are available for all network links. We find coefficients that split origin flows among routes starting at those origins. The advantage of the proposed methodology is that it does not rely on simulation that is prone to calibration errors but only on measured data. We also show how vehicle flows can be estimated at links with no detection, which enables computing performance measures for road networks lacking complete sensor coverage. Finally, we propose a method for selecting plausible routes between origins and destinations.

Keywords: traffic measurement; sparse methods; O-D estimation; Tikhonov regularization; LASSO (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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