Value of Time for Users of King Fahd Causeway Using Conventional and Artificial Intelligence Methodologies

Value-of-time (VOT) measures are valuable in a wide range of transport planning and policy implementation. Within the transport literature, econometric approach has widely been used to obtain this measure. Mostly, it is derived by estimating models from the logit family for mode choice and route choice behavior, where travel time and travel cost variables are part of the utility function, and the ratio of their estimated parameter is termed as VOT. This paper presents a detailed account on VOT estimation for different user classes. Furthermore, it introduces artificial intelligence (AI) and provides a framework through which VOT can be measured through this technique. The dataset used for the estimation of VOT is cross-sectional data collected from more than 500 individuals, who were users of the causeway and used different modes of transport for their journeys. VOT obtained from the logit approach is only based on the pooled dataset; however, AI technique in the form of artificial neural networks provides results for the different user classes in plausible terms and the values estimated are in line with the value obtained from the logit approach.