Sk. Md. Mashrur & Brenden Lavoie present ‘Development of a multimodal network microsimulation model for a regional agent-based travel demand modelling for the Greater Toronto and Hamilton Area (GTHA)’

Innovative mobility solutions are constantly emerging with the advent of information and communication technologies, offering travellers multidimensional choices including, but not limited to, modes, routes, and locations. Without a fully agent-based and dynamic travel demand microsimulation modelling system, it is impossible to evaluate such emerging mobility solutions/options realistically. The network modelling component is the most critical roadblock of such a modelling system. Available regional travel demand models in the GTHA use aggregate, static, and deterministic user equilibrium-based network models (e.g., the GTA and GGH models). There is no operational network model in an agent-based microsimulation framework available in the region, which is of the entire GTHA multimodal transportation network that can capture the network dynamics.

The project aims to develop a network model for the entire GTHA, using a multi-agent-based transport simulation (MATSim) framework named GTHA-Sim. MATSIM uses a co-evolutionary algorithm with dynamic queue-based traffic and schedule-based transit assignment procedures, making it a fast and feasible network model for regional transportation planning applications. The GTHA-Sim microsimulates network flows for all modes, including all types of transits. GTHA-Sim’s traffic network is developed based on OpenStreetMap data, and its transit network is based on Google Transit Feed Specification. The network is calibrated using the 2016 Transportation Tomorrow Survey data, which is of 5% residents of the GTHA. The GTHA-Sim uses an embedded model calibration system of MATSim called CADYTS. The CADYST based calibration is specified by using peak hour traffic volume at numerous count stations in major highways and regional streets. The GTHA-Sim is developed to further integrate with an activity-based modelling framework.

Once integrated, the framework will work as a testbed for evaluating policy analysis for various emerging mobility alternatives (i.e., ridesharing, on-demand mobility needs, autonomous vehicles) and even unprecedented situations like the COVID-19 pandemic. The model deliverables will equip the authorities to make conclusive decisions considering users’ level of behavioural adaptation with intricate policies in megacities like the Greater Toronto Area.

Sk. Md. Mashrur is a PhD student in Civil Engineering at the University of Toronto. He earned his master’s and bachelor’s degrees from the Bangladesh University of Engineering and Technology. His research interest is transit demand modelling. He is currently working on integrated activity-based demand and dynamic transit assignment framework for his PhD under the supervision of Professor Khandker Nurul Habib.

Brenden Lavoie is a Civil Engineering MASc candidate, working under the supervision of Professor Khandker Nurul Habib. He graduated with his undergraduate engineering degree from the University of Toronto in June 2021, and is now back as a graduate student researching impacts of autonomous vehicles.

Presented by University of Toronto ITE Student Chapter, UT-ITE.

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