Incident detection based on short-term travel time forecasting

Hing Keung William Lam, Mei Lam Tam, Agachai Sumalee, Chung Lun Li, Wu Chen, S. K. Kwok, Zhilin Li, Wai Ting Ngai

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)

Abstract

Prediction of short-term future traffic condition is an important element for route guidance and incident management systems. In this paper, a solution algorithm is proposed for short-term travel time forecasting in congested urban roads of Hong Kong. The travel times in the next 5-minute interval are predicted by using the historical travel time estimates together with their updated temporal variance-covariance relationships. The territory-wide historical travel time database is generated by the real-time travel information system (RTIS) using the automatic vehicle identification data available in Hong Kong. Based on the travel time forecasts and the RTIS travel time estimates, traffic incident can be detected by comparing their differences on the road section before and after the incident. Case studies are presented to evaluate the performance of the proposed algorithms for short-term travel time prediction and incident detection.
Original languageEnglish
Title of host publicationProceedings of the 13th International Conference of Hong Kong Society for Transportation Studies
Subtitle of host publicationTransportation and Management Science
Pages83-92
Number of pages10
Publication statusPublished - 1 Dec 2008
Event13th International Conference of Hong Kong Society for Transportation Studies: Transportation and Management Science - Kowloon, Hong Kong
Duration: 13 Dec 200815 Dec 2008

Conference

Conference13th International Conference of Hong Kong Society for Transportation Studies: Transportation and Management Science
Country/TerritoryHong Kong
CityKowloon
Period13/12/0815/12/08

ASJC Scopus subject areas

  • Transportation

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