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Team: Siyu Chen, Yifei Xie, Youssef Aboutableb, Simon Oh, Jimi Oke, Arun Akinepally, Ravi Seshadri, Carlos Azevedo, Kakali Basak, Moshe Ben-Akiva

Develop an agent-based simulation framework to model automated mobility-on-demand (AMOD) services including demand and supply, and integrate this framework within SimMobility

Analyze the impacts of AMOD from the standpoint of users, fleet operators, regulators and society as a whole using extensive scenario simulations in prototypical north American cities and other cities worldwide

The advent of autonomous vehicle technologies and the emergence of new ride-sourcing business models has spurred interest in Automated Mobility-on-Demand (AMOD) as a prospective solution to meet the challenges of urbanization. AMOD has the potential of providing a convenient, reliable and affordable mobility service through more competitive cost structures enabled by autonomy (relative to existing services) and more efficient centralized fleet operations. However, the short and medium-term impacts of AMOD are as yet uncertain. On the one hand, it has the potential to alleviate congestion through increased ride-sharing and reduced car-ownership, and by complementing mass-transit. Conversely, AMOD may in fact worsen congestion due to induced demand, the cannibalization of public transit shares, and an increase in Vehicle-Kilometers Traveled (VKT) because of rebalancing and empty trips. This stream of research attempts to systematically examine the impacts of AMOD on transportation in Singapore through agent-based simulation, modeling demand, supply and their interactions explicitly. On the demand side, we utilize an activity-based model system, and on the supply side, we model the operations of the AMOD fleet (including the assignment of requests to vehicles and rebalancing), which are integrated within a multimodal mesoscopic traffic simulator.


1. Oke, J. B., Akkinepally, A. P., Chen, S., Xie, Y., Aboutaleb, Y. M., Azevedo, C. L., ... & Ben-Akiva, M. (2020). Evaluating the systemic effects of automated mobility-on-demand services via large-scale agent-based simulation of auto-dependent prototype cities. Transportation Research Part A: Policy and Practice, 140, 98-126.

2. Oh, S., Seshadri, R., Azevedo, C. L., Kumar, N., Basak, K., & Ben-Akiva, M. (2020). Assessing the impacts of automated mobility-on-demand through agent-based simulation: A study of Singapore. Transportation Research Part A: Policy and Practice, 138, 367-388.

3. Oh, S., Seshadri, R., Le, D. T., Zegras, P. C., & Ben-Akiva, M. E. (2020). Evaluating Automated Demand Responsive Transit Using Microsimulation. IEEE Access, 8, 82551-82561.

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