By Philip Lu, 2020 Undergraduate Student Fellow
Driverless, autonomous vehicles (AVs) pose sweeping implications for the future of our cities and roads, from congestion to the design and usage of public spaces. In Ontario, the Ministry of Transportation has allowed AVs to be tested on public roads under certain conditions. Though it may be years still before fully autonomous vehicles roam our streets, we are at a critical window of opportunity to anticipate and plan for their impacts on our cities and citizens. In this project, I simulated different operating scenarios of autonomous, mobility-on-demand (AMoD) vehicle fleets in the City of Toronto. I developed a simulation pipeline that includes generating a synthetic trips dataset and using an open-source software library, AMoDeus, to create, run, and view agent-based models of AV fleets. After running the simulation, I also explored how metrics such as passenger wait times and fleet travel distances vary across different operating policies and income and age groups.
Results show that the choice of AV fleet policy strongly influences how wait times are distributed across income groups. In particular, passengers from lower income households tend to experience longer wait times, especially when a fleet rebalancing algorithm is used. This suggests that AV fleets optimized for efficiency may potentially exacerbate mobility inequities between low and high-income passengers.
The simulation and analysis pipeline are meant to serve as a starting point for further research and exploration on the impacts of autonomous vehicles on the city.
To learn more about the project and to access the data and open-source code, please visit: https://github.com/philipqlu/amod-toronto