Integrated simulation-based dynamic traffic and transit assignment model for large-scale network

• Published in: Canadian journal of civil engineering Vol. 47; no. 8; pp. 898 - 907
• Main Authors: Kamel, Islam, Shalaby, Amer, Abdulhai, Baher
• Format: Journal Article
• Language: English
• Published: 1840 Woodward Drive, Suite 1, Ottawa, ON K2C 0P7 NRC Research Press 01.08.2020; Canadian Science Publishing NRC Research Press
• Subjects: affectation dynamique des services de transport en commun; affectation dynamique du trafic; Computer simulation; congestion sur les routes; Dynamic models; dynamic traffic assignment; dynamic transit assignment; engorgement dans les véhicules de transport en commun; in-vehicle congestion; large-scale network; modèle de transport multimodal; multimodal transportation model; out-of-vehicle congestion; réseau à grande échelle; Schedules; Simulation; Strategic planning (Business); Timetables; Traffic assignment; Traffic congestion; Traffic models; Transport; Transportation models; Travel time; Ontario
• ISSN: 0315-1468; 1208-6029
• DOI: 10.1139/cjce-2018-0706

Although the traffic and transit assignment processes are intertwined, the interactions between them are usually ignored in practice, especially for large-scale networks. In this paper, we build a simulation-based traffic and transit assignment model that preserves the interactions between the two assignment processes for the large-scale network of the Greater Toronto Area during the morning peak. This traffic assignment model is dynamic, user-equilibrium seeking, and includes surface transit routes. It utilizes the congested travel times, determined by the dynamic traffic assignment, rather than using predefined timetables. Unlike the static transit assignment models, the proposed transit model distinguishes between different intervals within the morning peak by using the accurate demand, transit schedule, and time-based road level-of-service. The traffic and transit assignment models are calibrated against actual field observations. The resulting dynamic model is suitable for testing different demand management strategies that impose dynamic changes on multiple modes simultaneously.