Next-Generation Decision-Support Tool for Urban Transportation Planning: Accommodating on-Demand Mobility and Automated/Connected Automated Vehicle Systems

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The upcoming transport revolution-driven by internet/smartphone-based on-demand services and automated vehicle (AV) technologies-will completely change the way we plan, maintain and operate urban transportation infrastructures. Yet, we have neither a comprehensive understanding of the impacts of these technologies on urban transportation systems nor an appropriate decision-support tool to help develop the public policies necessary for sustainable systems. AV systems will affect travel behaviour, presenting new choices while possibly eliminating old ones. The demand for private cars may increase, particularly among those incapable of driving, e.g. children, seniors. This can improve mobility and social inclusion but at the cost of greater reliance on the automobile and, perhaps, the demise of current forms of urban transit. However, today's best transportation demand models cannot make reliable predictions. They use household travel surveys for data. Such surveys collect data that are of current/existing contexts of urban form and transportation technologies, and so the mathematical specifications of these models are based on the demand-supply interactions of conventional transportation systems. Current contexts of urban form, transportation systems and demand-supply interactions may not remain valid for the future with widespread applications of AV, on-demand mobility services and our changed demands for transportation. So, there is a dire need of updating the current state-of-the art of travel demand models (a decision-support system for urban transportation) and this is envisioned in this project. The next-generation decision-support system to be developed in this project will be based on consumer behaviour-based travel choice models and on accurate travel demand data collected through designed experiments (of impacts of AV and on-demand mobility). These models can predict latent/suppressed demands for using urban transportation systems as the latter evolve due to increasing automation and a coordinated/connected approach to operations.  I have extensive experience in travel survey methods, data fusion and travel demand modelling as well as in training students, who are in high demand at public, non-profit and for-profit organizations in urban transportation. The project will train 18 students on transportation planning, travel demand modelling, travel survey methods, statistical analysis and software development. The tool (decision-support system) will break new ground on evidence-based transportation planning and policy development while recognizing future uncertainty. It will help urban planners, transportation planners, traffic engineer, and public policy-makers develop transportation and land-use policies for the impending era with accurate data on the urban economy, environmental sustainability and social equity.