Modelling drivers’ en-route diversion behaviour under variable message sign messages using real detected traffic data

Author(s): T.D. Xu ; L.J. Sun ; Z.R. Peng ; Y. Hao
DOI: 10.1049/iet-its.2011.0060

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Author(s): T.D. Xu ^{1, 2} ; L.J. Sun ² ; Z.R. Peng ^{2, 3} ; Y. Hao ⁴
- Affiliations: 1: Institute of Transportation Engineering, Zhejiang University, Hangzhou, People's Republic of China
  2: School of Transportation Engineering, Tongji University, Shanghai, People's Republic of China
  3: College of Design, Construction and Planning, University of Florida, Gainesville, USA
  4: College of Design, Construction and Planning, Urban Transport Institute, China Academy of Urban Planning and Design, Beijing, People's Republic of China
Source: Volume 5, Issue 4, December 2011, p. 294 – 301
DOI: 10.1049/iet-its.2011.0060 , Print ISSN 1751-956X, Online ISSN 1751-9578

The study aims to develop a new method suitable for analysing en-route diversion behaviour. A corresponding probit model is used to analyse and quantify the impact of various variable message sign (VMS) messages and other factors involved in traffic diversion based on real-time detected traffic data in Shanghai, China. Traffic data from loop detectors, used since 2003, and vehicle license plate readers, used since 2008, are used to analyse the impact of VMS messages on the drivers’ en-route diversion behaviour and develop an aggregated en-route diversion behaviour model. The results indicate that drivers are more sensitive to travel time information than traffic congestion information. Therefore drivers will benefit if they will be able to choose the right route if information suggesting alternate routes is provided and neighbouring VMSes are coordinated. Moreover, time factors, off-ramp conditions and visibility of downstream congestion significantly influence en-route diversion behaviour, which can elucidate the significant difference between the result from en-route diversion behaviour model based on stated preference survey and the real traffic system.

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Modelling drivers’ en-route diversion behaviour under variable message sign messages using real detected traffic data

Modelling drivers’ en-route diversion behaviour under variable message sign messages using real detected traffic data

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