Heterogeneous Decision Rule Model of Mode Choice Incorporating Utility Maximization and Disutility Minimization

With the advent of more flexible discrete choice models, the analysis of heterogeneity at the observed and unobserved levels is receiving increasing attention. However, heterogeneity in decision rules has hardly been investigated in the mode choice context. This study proposes a heterogeneous decision rule model of mode choice incorporating utility maximization and disutility minimization using empirical data from Chennai City, India. The two decision rules may yield different estimates of mode choice probabilities if the error structure is not symmetric. Therefore, a heterogeneous decision rule model is estimated by postulating separate choice behaviors for each decision segment. Because the decision rule remains latent, individuals are probabilistically assigned to the two segments. The membership propensity of belonging to each class is modeled by using a binary logit form. The performance of the proposed heterogeneous decision rule (HDR) model is compared with the pure utility maximization, pure disutility minimization, and heterogeneous latent class models. The results reveal that the HDR model outperforms these alternative specifications. Further, significant differences are observed across the decision segments for aggregate modal shares, intrinsic preference for different modes, sensitivity to modal attributes, role of subjective factors, and the effect of activity patterns and accessibility. Factors influencing the decision-segment membership propensity are also identified. These findings have important behavioral and practical implications for analysis and evaluation of travel demand management measures aimed at sustainable urban transportation systems, congestion mitigation, and transit improvement.