We are observing a disruption in the urban transportation worldwide. The number of cities offering shared-use on-demand mobility services is increasing rapidly. They promise sustainable and affordable personal mobility without a burden of owning a vehicle. Despite growing popularity, on-demand services still have a hard time dealing with heterogeneous demand patterns. It’s the case of rebalancing of one-way carsharing and Uber/Lyft like services. In the past few years we have been studying Autonomous Mobility on Demand (AMOD) solutions: an on-demand self-driving electric vehicle service. In a study with ARES group at MIT and SMART, we built upon our simulation platform, SimMobility, to study the operation of AMOD systems and its impact on travel decision making. We compare the performance of different AMOD fleet sizes, parking lot locations and operational (rebalancing) algorithms and uncovered changes in the overall multi-modal transportation system and in individual mobility patterns, specifically in regard to modal shares, routes, and destinations.
 “Simulation Framework for Rebalancing of Autonomous Mobility on-Demand Systems”
Marczuk, M., Soh, H., Lima Azevedo, C., Lee, D.H., Frazzoli, E. 5th International Conference on Transportation and Traffic Engineering (ICTTE 2016), Lucerne, Switzerland, July 6-10, 2016
 “Microsimulation of Demand and Supply of Autonomous Mobility On-Demand”
Lima Azevedo, C., Marczuk, K., Raveau, S., Soh, H., Adnan, M., Basak, K., Loganathan, H., Deshmunkh, N., Lee, D. H., Frazzoli, E., Ben-Akiva, M. E. Transportation Research Record: Journal of the Transportation Research Board, No. 2564., pp. 21-30.
(A day of AMOD operation in Singapore’s extended CBD network; pink are vehicles in service – from station to pick-up, occupied, from drop-off to station; blue are vehicles being rebalanced; Credits to Kasia and Harold)