Bayesian inference for static traffic network flows with mobile sensor data

Zhen Tan, H. Oliver Gao

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

Abstract

Vehicle trajectory information are becoming available from mobile sensors such as onboard devices or smart phones. Such data can provide partial information of origin-destination trips and are very helpful in solving the network flow estimation problem which can be very challenging if only link counts are used. Even with this new information, however, there is still structural bias in the maximum likelihood based approach because of uncertainties in the penetration rates. A Bayesian inference approach in which the earlier link-count-based methods are extended is proposed. We incorporate posterior simulation of route-choice probabilities and penetration rates. The results of a numerical example show that our method can infer network flow parameters effectively. Inclusion of mobile sensor data and prior beliefs based on it can yield much better inference results than when non-informative priors and only link counts are used.

Original languageEnglish
Title of host publicationProceedings of the 51st Annual Hawaii International Conference on System Sciences, HICSS 2018
EditorsTung X. Bui
PublisherIEEE Computer Society
Pages969-978
Number of pages10
ISBN (Electronic)9780998133119
Publication statusPublished - 2018
Externally publishedYes
Event51st Annual Hawaii International Conference on System Sciences, HICSS 2018 - Big Island, United States
Duration: 2 Jan 20186 Jan 2018

Publication series

NameProceedings of the Annual Hawaii International Conference on System Sciences
Volume2018-January
ISSN (Print)1530-1605

Conference

Conference51st Annual Hawaii International Conference on System Sciences, HICSS 2018
Country/TerritoryUnited States
CityBig Island
Period2/01/186/01/18

ASJC Scopus subject areas

  • Engineering (all)

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