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

Tao Xing, Xuesong Zhou, Jeffrey Taylor

Research output: Contribution to journalArticle

29 Citations (Scopus)

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.

Original languageEnglish (US)
Pages (from-to)66-90
Number of pages25
JournalTransportation Research Part B: Methodological
Volume57
DOIs
StatePublished - Nov 2013

Fingerprint

Heterogeneous networks
Travel time
Sensor networks
travel
uncertainty
Sensors
traffic
sampling error
Time measurement
Measurement errors
time
Modeling
quantification
heuristics
Sampling
determinants
Uncertainty
Sensor
methodology
Prediction

Keywords

  • Automatic vehicle identification sensors
  • Automatic vehicle location sensors
  • Sensor network design
  • Travel time prediction

ASJC Scopus subject areas

  • Management Science and Operations Research
  • Transportation

Cite this

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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.",
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