Designing heterogeneous sensor networks for estimating and predicting path travel time dynamics: An information-theoretic modeling approach

Tao Xing, Xuesong Zhou and Jeffrey Taylor

Transportation Research Part B: Methodological, 2013, vol. 57, issue C, 66-90

Abstract: With a particular emphasis on the end-to-end travel time prediction problem, this paper proposes an information-theoretic sensor location model that aims to minimize total travel time uncertainties from a set of point, point-to-point and probe sensors in a traffic network. Based on a Kalman filtering structure, the proposed measurement and uncertainty quantification models explicitly take into account several important sources of errors in the travel time estimation/prediction process, such as the uncertainty associated with prior travel time estimates, measurement errors and sampling errors. By considering only critical paths and limited time intervals, this paper selects a path travel time uncertainty criterion to construct a joint sensor location and travel time estimation/prediction framework with a unified modeling of both recurring and non-recurring traffic conditions. An analytical determinant maximization model and heuristic beam-search algorithm are used to find an effective lower bound and solve the combinatorial sensor selection problem. A number of illustrative examples and one case study are used to demonstrate the effectiveness of the proposed methodology.

Keywords: Travel time prediction; Sensor network design; Automatic vehicle identification sensors; Automatic vehicle location sensors (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (13)

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DOI: 10.1016/j.trb.2013.09.007

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