For this month's Faculty Partner Highlights we'd like to feature Professor Amer Shalaby. Professor Amer Shalaby focuses his research on crowds and congestion, both at the local and global levels, with particular emphasis on disruption management. This work intends to help transportation authorities to respond more effectively to unexpected impediments or crises within a network, and permit the rerouting of passengers without seriously affecting migration patterns. As this work continues to evolve it will have ramifications for predicting crowd behaviours on micro levels, such as subways and other public transit applications, where large crowd congestions occur.
The project he is working on is titled "Analytical Framework for Planning On-Demand Transit Services and a Case Study of MiWay”. The project is in collaboration with MiWay, the public transit service of the City of Mississauga. This work was done with his master's student Shahrin Islam and Postdoc Reza Mahmoudi.
The research aims to optimize the use of limited transit resources in Mississauga. While some routes are highly utilized, others remain underperforming. The project aims to explore replacing underutilized fixed routes with on-demand transit services, which are more flexible and can better meet the needs of passengers in areas with lower demand. On-demand transit, similar to services like UberPool, allows passengers to request rides via an app, with routes dynamically adjusted based on demand.
The research team extensively reviewed the literature and industry practices related to on-demand transit in Canada. Additionally, they analyzed nine months' worth of data from MiWay, including AVL (Automatic Vehicle Location) data, APC (Automatic Passenger Count) data, and Presto card data. This analysis helped evaluate the performance of the current fixed-route system, identifying underperforming routes and areas where on-demand transit could be beneficial. Based on these findings, the project's next phase will involve developing models to assess the effectiveness of introducing on-demand transit in these identified areas.
The potential impacts of this project are both practical and methodological. Practically, the research will directly inform the implementation of an on-demand transit pilot in Mississauga, helping the city make data-driven decisions on where and how to integrate on-demand services into the existing transit network. Methodologically, the project advances new techniques for evaluating the performance of transit systems, which could be applied to other cities looking to optimize their transit services.