Agent-based Approach to Integrated Transit-Land Use Planning, with emphasis on Small Cities and Rural Communities

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Public transit in small communities can help support local businesses, boost local economy, and provide access to jobs and mobility to residents. However, the characteristics of small communities (e.g. low population density, dispersed land use, high auto ownership, limited financial capacity, etc.) present substantial challenges to the development of decent transit services. In addition, classical tools of transit planning are plagued with many problems and more suitable for regional planning than neighbourhood planning. This research program proposes an agent-based tool for integrated transit-land use planning that will put transit at the centre of communities through the development of customer-focused transit network design and supportive land use planning at the neighbourhood level. The proposed tool will bridge the gap between network design and service evaluation given its design and evaluation components. The design component will generate network designs that maximize demand attraction. The evaluation component will assess the impacts of the generated designs on demand satisfaction. Mode/route shift to transit will be modelled as an adaptive learning process (i.e. learning by interaction with the surrounding system) using Reinforcement Learning (RL) concepts and Random Utility Maximization (RUM) principles. RL concepts will be employed to account for the long-term accumulation of rewards. RUM principles will be used to measure passengers’ satisfaction in the short-term. Unlike classical transit planning tools, passengers will be modelled as goal-directed agents that interact with the transit/land use system (and among each other) and apply an optimal policy to choose the best option. At each simulation episode, agents will perceive the state of the system and choose a mode/route of travel accordingly while considering their past experiences. Based on earning positive or negative rewards, agents will adjust their behavior (i.e. mode/route choices) while seeking to maximize the total return received in the long-term. Modal shift barriers such as habit formation, awareness limitation, and partial information provision will be captured in the model by specifying a threshold or inertia against switching modes. The feedback between network design and service evaluation, using learning and adaptation, is unique to the proposed framework. Given its microscopic representation of individual passengers (demand) and the transit/land use system (supply), this approach is suitable for the analysis of various transit/land use initiatives where design details and behavioural aspects combine together to drive passengers’ choices. The final product will be an operationally feasible decision-support tool that best suits the context of small communities and considers municipalities’ existing and future land use patterns, mobility requirements, and financial capacity.