Microsimulation Modelling of Urban Goods Movement

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Freight transportation is at the core of all industry sectors of the Canadian economy. The transportation and warehousing sector in Canada represents 3.8% of the gross domestic product, and commercial transport industry employs about 5% of the Canadian population (Transport Canada, 2020). Freight transportation is undergoing rapid change, with major recent increases in e-commerce, pandemic related disruptions to supply chains, pressure to reduce greenhouse gas (GHG) emissions, new available electrification and automation technologies, labour shortages and lack of diversity. Last mile deliveries usually occur in urban centres and are the most expensive and impactful part of the supply chain. Advances are needed in urban freight transportation analytics, modelling and observational studies to improve our capability to assess impacts of new technology and business innovations, economic and regulatory policies, initiatives to improve labour force diversity, and investments in transportation and infrastructure. Advanced urban freight models are needed to assess mitigation of urban freight impacts on congestion, road safety, air quality, GHG emissions, and noise. Opportunities exist to improve the state-of-the-art in urban freight modelling, resulting from developments in data availability (especially truck telematics and satellite imagery) and data science techniques. There is also a need for freight pilot studies to transfer of knowledge from urban freight models into demonstration projects, which push forward the development of supportive regulation and policy and can provide insights that inform realistic model assumptions and scenarios. The long-term objective of Prof. Roorda's research program is to develop a data-driven urban freight microsimulation modelling system for the Toronto Area that can be used in practice to forecast impacts of freight-related technology, regulation, policy, and infrastructure on private sector operations, freight flows, and their social and environmental impacts. The modelling research will improve state-of-the-art methods for integrating logistics, optimization and data science techniques into public sector urban freight transportation models and will optimize use of combined telematics, sensor, industry and survey data. Scenario analysis, modelling and pilot studies will, in combination, lead to evidence-based public policy in freight and improved private sector freight operations. The short-term objectives are to: 1) Create an integrated freight data platform using data science techniques 2) Develop models of firm behaviour, supply chain and parking for application in a microsimulation model 3) Develop a computation platform for agent-based microsimulation of goods movement and firm behaviour 4) Demonstrate effectiveness of novel city logistics delivery methods by conducting pilot studies 5) Provide private and public sector policy support by applying microsimulation model scenarios