Data fusion for agent-based freight demand and traffic simulation

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The proposed research will develop an agent-based model of freight demand that is integrated with a traffic simulation model. The agent-based demand model will predict behavioural elements of urban goods movement, including truck tour formation, shipment location, stop sequence, stop duration, and route choice. Such models have extensive data requirements, which will be addressed through the development of data fusion methods that will integrate private sector truck GPS data, survey data from manufacturers and retail establishments, truck parking inventory and usage data, and information from a variety of other sources. The proposed research will integrate the agent-based freight demand model with a vehicle-by-vehicle simulation of combined truck and passenger vehicle traffic on the urban road network. The study area will initially be a small test study area in Toronto. This study area will be expanded to represent regional goods movement within the Greater Toronto and Hamilton Area, making use of parallel computing resources. The integrated agent-based modelling approach will be valuable for evaluating a variety of transportation policies and infrastructure improvements that are under consideration by various levels of government in Canada. Such policies include truck parking policy, truck route plans, road pricing, truck restrictions, and freight related bylaws related to industrial land-uses in urban areas. Impacts that can be assessed include congestion, emissions, noise, reliability and economic effects.