Day-to-Day and Within-Day Traffic Assignment Model of Heterogeneous Travelers Within the MaaS Framework

Abstract

With the continuous advancement of Mobility as a Service (MaaS), a hybrid traffic flow comprising MaaS-based and conventional trips has emerged within transportation networks, leading to diverse behaviors among heterogeneous travelers. Given the coexistence of heterogeneous travelers during the promotion of MaaS, this paper investigates two distinct groups: travelers using MaaS subscription services (defined as “subscribed users”) and traditional travelers who rely on personal experience (defined as “decentralized users”). Accordingly, we propose a day-to-day and within-day bi-level dynamic traffic assignment model for heterogeneous travelers under the MaaS framework. By optimizing subscribed users’ travel decisions, this model assists urban planners in predicting the evolution of mixed traffic flows, enabling improved road resource allocation and subscription service mechanisms. For the day-to-day component, the model explicitly incorporates mode-switching behaviors among heterogeneous travelers. In the within-day context, departure time and route choices are considered, along with travel time costs and additional costs arising from early or late arrivals. Consequently, we propose a within-day, time-dependent traffic assignment model specifically tailored for heterogeneous users. For modeling subscribed users’ traffic assignment, we develop a system-optimal (SO) bi-level programming model aiming at minimizing the total travel cost. Furthermore, by integrating an improved Genetic Algorithm with the Method of Successive Averages (MSA), we introduce an enhanced IGA-MSA hybrid algorithm to solve the proposed model. Finally, numerical experiments based on the Nguyen–Dupuis network are conducted to evaluate the performance of the proposed model and algorithm. The results indicate that the network with heterogeneous MaaS users can reach a steady state effectively, significantly reducing overall travel costs. Notably, decentralized users rapidly shift towards becoming subscribed users, highlighting the attractiveness of MaaS platforms in terms of cost reduction and enhanced travel experience. Additionally, the IGA-MSA hybrid algorithm effectively decreases overall travel costs in the early evolution stages and achieves a more balanced temporal distribution of trips across the system, effectively managing congestion during peak periods.

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