Determinants of driver response to variable message sign information in Athens. Issue 4 (1st May 2015)

• Record Type: Journal Article
• Authors: Spyropoulou, Ioanna; Antoniou, Constantinos
• Is Part Of: IET intelligent transport systems. Volume 9:Issue 4(2015)
• Page Start: 453
• Page End: 466
• Publication Date: 2015-05-01
• Subjects: behavioural sciences computing -- driver information systems -- road traffic control; driver response determinants -- variable message sign information -- advanced traveller information systems -- driver behaviour -- route choice -- traffic management strategies -- traffic congestion mitigation -- VMS -- Athens city -- random‐effect ordered probit model -- driver diversion propensity -- incident occurrence information -- message characteristics -- incident type -- alternative route suggestion -- incident impact -- trip characteristics -- vehicle type -- driver characteristics -- driver age -- driver income -- driving population -- driver gender; Intelligent transportation systems -- Periodicals; Electronics in transportation -- Periodicals; 388.31205
• DOI: 10.1049/iet-its.2014.0053
• Languages: English
• ISSNs: 1751-956X

Abstract

Abstract : Variable message signs – which comprise a type of advanced traveller information systems – can affect driver behaviour, especially considering route choice. Hence, their operation is integrated in traffic management strategies for the mitigation of traffic congestion. This research explores the factors determining driver response to variable message signs (VMSs) in the city of Athens. A stated preference questionnaire survey is undertaken and discrete choice analysis is performed towards this aim. More specifically, a random‐effect ordered probit model is estimated that provides insight on the contributory factors that influence driver propensity to divert, when provided with information on incident occurrence via VMSs. Message characteristics, that is, incident type, impact and suggestion for an alternative route, trip characteristics, such as vehicle type, as well as, driver characteristics, such as driver age and income, have been found to affect driver behaviour. Furthermore, appropriate models are also estimated for subsets of the driving population (considering gender and age) and specific similarities and differences between the population behaviours are identified.