01659929

Component

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, the authors build a simulation-based traffic and transit assignment network model in Aimsun that preserves the interactions between the two assignment processes for the large-scale network of the Greater Toronto Area (GTA) during the AM peak. The traffic assignment model is dynamic, user equilibrium seeking, and it includes surface transit routes from two major transit agencies. The transit assignment utilizes the congested travel times, determined by the dynamic traffic assignment model, rather than assuming free-flow conditions or using predefined timetables. In addition to the surface transit systems, the transit assignment model includes rapid rail systems as well of two different agencies (Government of Ontario (GO) Transit commuter rail, and Toronto Transit Commission Subway). To enhance over the static transit assignment that is typically used in Aimsun, the proposed transit model distinguishes between the different intervals within the AM peak model by using the accurate demand, transit schedule, and, more importantly, road level-of-service that are appropriate for each interval. Moreover, the traffic and transit assignment models are calibrated using actual field observations such that the errors in the simulated road flows and speeds, transit route loads, and passenger counts are minimized. The traffic assignment calibration was done manually, while a parallel genetic algorithm (GA) engine was used in the transit assignment calibration process. The resulting dynamic model that captures the interactions between the road and transit networks is suitable for testing different intelligent transportation systems (ITS) and travel demand management (TDM) strategies that impose dynamic changes on multiple modes simultaneously.

This paper was sponsored by TRB committee AHB45 Standing Committee on Traffic Flow Theory and Characteristics.

18-01797

English

23p

2018

Transportation Research Board 97th Annual Meeting

Digital/other

Figures; References; Tables

Dynamic traffic assignment; Highway traffic; Intelligent transportation systems; Railroad commuter service; Simulation; Subways; Traffic simulation; Travel demand management

Greater Toronto and Hamilton Area (GTHA)

Highways; Operations and Traffic Management; Public Transportation

Transportation Research Board Annual Meeting 2018 Paper #18-01797

TRIS, TRB, ATRI

Jan 8 2018 10:27AM