The whole day path planning problem incorporating mode chains modeling in the era of mobility as a service. (November 2021)

• Record Type: Journal Article
• Authors: Song, Yuchen; Li, Dawei; Cao, Qi; Yang, Min; Ren, Gang
• Is Part Of: Transportation research. Volume 132(2021)
• Publication Date: 2021-11
• Subjects: Whole day multimodal path planning -- Dynamic discrete choice model -- Unobservable heterogeneity -- Feasible mode chains -- User-constrained hyperpath algorithm; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2021.103360
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: A systematic approach is developed for the whole day multimodal shortest hyperpath problem. The dynamic discrete choice model generates a set of feasible mode chains. The conditional choice probability estimator and EM algorithm are used together. A user-constrained shortest hyperpath algorithm formulates the travel schemes. Potential use value of the recommendation system on the MaaS platform is presented. Abstract: The growing popularity of Mobility as a Service (MaaS) has led researchers to realize that it can be used for the optimal management of the transportation system of a city. This study is to solve the whole day multimodal path planning problem considering user-specific modal preference, which can be used on the recommended system of the MaaS platform. The mathematical formulations of the whole day multimodal shortest hyperpath problem are proposed while accounting for individual constraints, such as the time window, multicriteria, and park-and-ride demands, to develop customized travel schemes. The primary obstacle involves consideration of the travelers' modal preferences to develop an optimal whole day journey. The dynamic discrete choice model (DDCM) that accounts for unobservable heterogeneity is proposed to characterize dynamic mode choice behavior and generate a set of feasible mode chains. The conditional choice probability estimator and expectation–maximization (EM) algorithm are used in conjunction to estimate the dynamic model. In this way, the posterior probability can be adapted to the conditional choice probability estimation. The label correcting concept is used to develop a user-constrained shortest hyperpath algorithm that can solve the multimodal shortest hyperpath problem and calculate the Pareto set of the whole day travel schemes according to the feasible mode chains and different constraints. The proposed techniques are verified by utilizing household travel survey data and multimodal network data in Nanjing (China). Two unobserved states of travelers, namely time- and cost-sensitive, are identified based on the BIC value. The adjusted rho-squared value, accuracies of predicted mode chains, along with the aggregate validation results, confirm the model's effectiveness. The top two resulting hyperpaths are generated for a randomly chosen traveler on the basis of his or her preferences. This study is an important step toward promoting MaaS and improving the sales of MaaS bundles.