Random Parameter Nested Logit Model for Combined Departure Time and Route Choice

Abstract

A driver’s daily commute involves making three types of decisions: choice of departure time, route choice and mode choice. The objective of this paper was to investigate potential impacts of implementing variable congestion charging on driver's daily commute choice. Additionally, this study focused on the departure time and route choice process and investigated Random Parameter Nested Logit model (RPNL) for the combined departure time and route choice. Software R was used for stated choice experiment design, while software BIOGEME was used for model estimation based on the collected data. Compared with the basic Multinomial Logit model (MNL), the estimation results indicated that Random Parameter Nested Logit model (RPNL) improved the model performance, due to considering inter-alternative correlations as well as commuters’ heterogeneity. The combined choice models were utilized to present commuters’ trade-offs among commute time, monetary cost and schedule delay. The results showed that under the situation without flexible work patterns, the disutility of late arrival and early departure were high, which resulted in less attractiveness for commuters to switch their departure time and further weaken the performance of implementing congestion charging. Therefore, in order to enhance the performance of a congestion charging policy for relieving traffic congestion in a community, elastic work patterns as well as other complimentary measures should be introduced in parallel.