TY - JOUR
T1 - Designing heterogeneous sensor networks for estimating and predicting path travel time dynamics
T2 - An information-theoretic modeling approach
AU - Xing, Tao
AU - Zhou, Xuesong
AU - Taylor, Jeffrey
PY - 2013/11
Y1 - 2013/11
N2 - 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.
AB - 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.
KW - Automatic vehicle identification sensors
KW - Automatic vehicle location sensors
KW - Sensor network design
KW - Travel time prediction
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U2 - 10.1016/j.trb.2013.09.007
DO - 10.1016/j.trb.2013.09.007
M3 - Article
AN - SCOPUS:84885081923
SN - 0191-2615
VL - 57
SP - 66
EP - 90
JO - Transportation Research Part B: Methodological
JF - Transportation Research Part B: Methodological
ER -