Future Mobility Systems

Future Mobility Systems
Investigation of large-scale coordination algorithms for the optimization of future mobility systems, with an emphasis on autonomous mobility on demand (AMoD) – a transformative and rapidly developing mode of transportation wherein fleets of self-driving vehicles transport passengers on demand within a city. Emphasis is placed on accounting for the couplings with other modes of transportation (in the context of an intermodal transportation system) and with other infrastructure (e.g., the power network). This line of research involves collaborations with a number of industry partners, from conceptual studies all the way to field deployments.

Keywords: Autonomous Mobility on Demand, Optimization, Routing

Students: Ramon Iglesias, Matt Tsao

Related Work

Journal Articles

  1. F. Rossi, R. Zhang, Y. Hindy, and M. Pavone, “Routing Autonomous Vehicles in Congested Transportation Networks: Structural Properties and Coordination Algorithms,” Autonomous Robots, vol. 42, no. 7, pp. 1427–1442, 2018.

    Abstract: This paper considers the problem of routing and rebalancing a shared fleet of autonomous (i.e., self-driving) vehicles providing on-demand mobility within a capacitated transportation network, where congestion might disrupt throughput. We model the problem within a network flow framework and show that under relatively mild assumptions the rebalancing vehicles, if properly coordinated, do not lead to an increase in congestion (in stark contrast to common belief). From an algorithmic standpoint, such theoretical insight suggests that the problem of routing customers and rebalancing vehicles can be decoupled, which leads to a computationally-efficient routing and rebalancing algorithm for the autonomous vehicles. Numerical experiments and case studies corroborate our theoretical insights and show that the proposed algorithm outperforms state-of-the-art point-to-point methods by avoiding excess congestion on the road. Collectively, this paper provides a rigorous approach to the problem of congestion-aware, system-wide coordination of autonomously driving vehicles, and to the characterization of the sustainability of such robotic systems.

    @article{RossiZhangEtAl2017,
      author = {Rossi, F. and Zhang, R. and Hindy, Y. and Pavone, M.},
      title = {Routing Autonomous Vehicles in Congested Transportation Networks: Structural Properties and Coordination Algorithms},
      journal = {{Autonomous Robots}},
      volume = {42},
      number = {7},
      pages = {1427--1442},
      year = {2018},
      doi = {10.1007/s10514-018-9750-5},
      url = {../wp-content/papercite-data/pdf/Rossi.Zhang.Hindy.Pavone.AURO17.pdf},
      owner = {frossi2},
      timestamp = {2018-08-07}
    }
    

Conference Articles

  1. M. Tsao, D. Milojevic, C. Ruch, M. Salazar, E. Frazzoli, and M. Pavone, “Model Predictive Control of Ride-sharing Autonomous Mobility on Demand Systems,” in Proc. IEEE Conf. on Robotics and Automation, Montreal, Canada, 2019. (Submitted)

    Abstract: This paper presents a model predictive control (MPC) approach to optimize routes for Ride-sharing Autonomous Mobility-on-Demand (RAMoD) systems, whereby self-driving vehicles provide coordinated on-demand mobility, possibly allowing multiple customers to share a ride. Specifically, we first devise a time-expanded network flow model for RAMoD. Second, leveraging this model, we design a real-time MPC algorithm to optimize the routes of both empty and customer-carrying vehicles, with the goal of optimizing social welfare, namely, a weighted combination of customers’ travel time and vehicles’ mileage. Finally, we present a real-world case study for the city of San Francisco, CA, by using the microscopic traffic simulator MATSim. The simulation results show that a RAMoD system can significantly improve social welfare with respect to a single-occupancy AMoD system, and that the predictive structure of the proposed MPC controller allows it to outperform existing reactive ride-sharing coordination algorithms for RAMoD.

    @inproceedings{TsaoMilojevicEtAl2019,
      author = {Tsao, M. and Milojevic, D. and Ruch, C. and Salazar, M. and Frazzoli, E. and Pavone, M.},
      title = {Model Predictive Control of Ride-sharing Autonomous Mobility on Demand Systems},
      booktitle = {{Proc. IEEE Conf. on Robotics and Automation}},
      year = {2019},
      note = {Submitted},
      address = {Montreal, Canada},
      month = may,
      url = {../wp-content/papercite-data/pdf/Tsao.ea.ICRA19.pdf},
      keywords = {sub},
      owner = {samauro},
      timestamp = {2018-09-16}
    }