Origin-Destination Demand Reconstruction Using Observed Travel Time under Congested Network

Chao Sun (), Yulin Chang, Xin Luan, Qiang Tu and Wenyun Tang
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Chao Sun: Jiangsu University
Yulin Chang: Jiangsu University
Xin Luan: Southeast University
Qiang Tu: Southeast University
Wenyun Tang: Nanjing Forestry University

Networks and Spatial Economics, 2020, vol. 20, issue 3, No 4, 733-755

Abstract: Abstract Two bi-level models to reconstruct origin-destination (O-D) demand under congested network are explored in terms of the observed link and route travel times, where one model inputs the known trajectories of observed route travel times and the other model uses both known and unknown trajectories of observed route travel times. The proposed models leverage both the link and route traffic information to determine the network O-D demand that minimizes the distances between the observed and estimated traffic information (O-D, link and route travel times) in the upper-level, and optimize the stochastic user equilibrium (SUE) in the lower-level. Meanwhile, the observed information of travel time can capture the relationships between traffic flow and travel cost/time in congested network. The K-means (hard assignment) and Gaussian mixture model (GMM, soft assignment) clustering methods are presented to identify the trajectories of observed route travel times. An iterative solution algorithm is proposed to solve the built O-D reconstruction models, where the method of gradient descent, the method of successive average and Expectation-Maximization (EM) algorithm are used to solve the upper-level model, lower level model, and GMM, respectively. Results from numerical experiments demonstrate the superiority of the travel time based model over the traditional flow based method in congested traffic network, and also suggest that using both the route and link information outperforms only using link information in the reconstruction of O-D demand.

Keywords: Origin-destination demand reconstruction; Link travel time; Unknown trajectory; Route travel time; K-means method; Gaussian mixture model (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11067-020-09496-4

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