Evaluating perceived quality of traffic information system using structural equation modeling

Abstract

Megacities worldwide have been employing some form of traffic information systems to manage their road network and to mitigate congestion. However, the full benefits of such systems can only be achieved if drivers perceive the system to be of adequate quality for them to actually use the disseminated information in their travel choices. One way to evaluate the underlying quality of the system is to conduct questionnaire survey, gather direct driver feedback and perform statistical analyses to derive attributes or indicators such as driver awareness, utilization of traffic information and perceived effectiveness which are useful to agencies for their planning and management activities. However many past works have treated the attributes to be independent and did not offer a complete picture on the perceived quality of the traffic information system. This paper therefore presents the development of a holistic structural equation model that can consider all potential attributes affecting perceived quality in a single framework. Attributes considered in the paper include: driver awareness, driver utilization, perceived effectiveness, expectation, and perceived necessity of the system. Using the traffic information system in Klang Valley, Malaysia as a case study, it is demonstrated that there are causal and cross-loading relationships between the attributes contributing to perceived system quality and the attributes must be considered in a comprehensive manner. In addition, the model offers a useful interpretation on the factors affecting road user perception on the traffic information system, thereby allowing agencies to undertake appropriate measures to further improve the level of user satisfaction.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 10 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.