Quantifying impact of traffic images applications (APPS) on travel choices

Abstract

The advent of smartphone technologies has caused revolution to the manner of dissemination of traffic information. Development of traffic image applications (apps) allows drivers to obtain updated traffic information by accessing Closed Circuit Television Camera (CCTV) images mounted along the roadways. This facilitates drivers to carry out pre-trip or en-route trip planning to avoid congestion while authorities manage travel demand more efficiently. Previous studies are mostly focused on quantifying driver perception of road congestion level based on the traffic images shown, but failed to scrutinize their responses. The manner in which traffic image applications (apps) could influence driver travel choices are yet to be established. This study is thus carried out to gratify the research gap by investigating drivers' likeliness to change their travel choices by virtue of route, departure time, mode, and willingness to cancel a planned trip upon viewing the traffic conditions via apps. A questionnaire study is piloted to gather drivers' opinion while fuzzy logic is adopted to model their responses. It was established that drivers are more sensitive to changes in density rather than speed and flow when perceiving traffic congestion levels. A new dynamic relationship of drivers' travel choice and their perceived congestion levels is deduced. This relationship explains the type of travel plan changes the drivers are more likely to make under different traffic conditions, endorsing benefits in travel demand modeling as opposed to a single value outcome provided by most of the existing studies. The model reveals that drivers perceived only 3 levels of congestion, i.e., low, medium, and high congestion. They tend to change their departure time choice at medium perceived congestion level while the likeliness to change route increases when congestion level increases. Drivers cancel trips when they perceive heavy congestion but less likely to perform mode choice changes.