Development of a cumulative prospect theory-based departure time choice model for dynamic traffic microsimulation

This study presents a comprehensive framework of dynamic traffic microsimulation modeling system that considers travelers’ departure time (DT) choices in response to sudden risk events in the transport network. The novelty of the model is that it captures the nonlinear responses of travelers to sudden risk events during DT choice-making by utilizing a Cumulative Prospect Theory (CPT)-based approach. For model testing, the study considers a case of transportation systems’ critical infrastructure (CI) renewal in Halifax, Canada that poses considerable uncertainty for travelers in the morning rush hours during a construction period. Two models were evaluated: (1) a model without the DT component (Model 1) and (2) a model with the DT component (Model 2). Model 2 offers methodological promises in studying traveler behavior under uncertainty. The proposed CPT-based DT model is advantageous to capture nonlinearity in quantifying travelers’ perception of transportation choice utility. The results of Model 2 significantly differ from the results of the traditional model without the DT component in terms of network performance. For instance, if the DT choice is considered, total traffic delays significantly increase in the early rush hours due to construction-related sudden bridge closure. In Model 2, queue increases at local intersections for initial hours if drivers’ DT adjustment is explicitly modeled within the traffic microsimulation modeling framework. Results of this study provide insights into developing emergency transportation management strategies in the case of sudden disruptions to daily travel activities and traffic operations in the network.