The design of future mobility solutions such as autonomous vehicles and micromobility, and the design of the mobility systems they enable are closely coupled. Indeed, knowledge about the intended service of novel mobility solutions would impact their design and deployment process, while insights about their technological development could significantly affect transportation management policies. This requires tools to study such a coupling and co-design future mobility systems in terms of different objectives. Our work tries to address such co-design problems. In particular, we leverage the recently developed mathematical theory of co-design to frame and solve the problem of designing and deploying an intermodal mobility system, whereby autonomous vehicles service travel demands jointly with micromobility solutions such as shared bikes and e-scooters, and public transit, in terms of fleets sizing, vehicle characteristics, and public transit service frequency. The key ingredients of our framework are modularity and compositionality, which allow one to describe the design problem as the interconnection of its individual components and to tackle it from a system-level perspective. Our work suggests that it is possible to create user-friendly optimization tools to systematically assess the costs and benefits of interventions, and that such analytical techniques might inform policy-making in the future.
The design of future mobility solutions (autonomous vehicles, micromobility solutions, etc.) and the design of the mobility systems they enable
are closely coupled. Indeed, knowledge about the intended service of novel mobility solutions would impact their design and deployment process,
whilst insights about their technological development could significantly affect transportation management policies. This requires tools to study
such a coupling and co-design future mobility systems in terms of different objectives. This paper presents a framework to address such co-design
problems. In particular, we leverage the recently developed mathematical theory of co-design to frame and solve the problem of designing and
deploying an intermodal mobility system, whereby autonomous vehicles service travel demands jointly with micromobility solutions such as shared
bikes and e- scooters, and public transit, in terms of fleets sizing, vehicle char- acteristics, and public transit service frequency.
Our framework is modular and compositional, allowing one to describe the design problem as the interconnection of its individual components and
to tackle it from a system-level perspective. Moreover, it only requires very general monotonicity assumptions and it naturally handles multiple
objectives, delivering the rational solutions on the Pareto front and thus enabling policy makers to select a policy. To showcase our methodology,
we present a real- world case study for Washington D.C., USA. Our work suggests that it is possible to create user-friendly optimization tools
to systematically assess the costs and benefits of interventions, and that such analytical techniques might inform policy-making in the future.
@article{zardini2020camod,
title={Co-Design to Enable User-Friendly Tools to Assess the Impact of Future Mobility Solutions},
author={Zardini, Gioele and Lanzetti, Nicolas and Censi, Andrea and Frazzoli, Emilio and Pavone, Marco},
journal={arXiv preprint arXiv:2008.08975},
year={2020}
}
G. Zardini, N. Lanzetti, M. Salazar, A. Censi, E. Frazzoli, M. Pavone On the Co-Design of AV-Enabled Mobility Systems
IEEE 23rd International Conference on Intelligent Transportation Systems Conference (ITSC), 2020, Rhodes (Greece)
The design of autonomous vehicles (AVs) and the design of AV-enabled mobility systems are closely coupled. Indeed, knowledge
about the intended service of AVs would impact their design and deployment process, whilst insights about their technological
development could significantly affect transportation management decisions. This calls for tools to study such a coupling and
co-design AVs and AV-enabled mobility systems in terms of different objectives. In this paper, we instantiate a framework to
address such co-design problems. In particular, we leverage the recently developed theory of co-design to frame and solve the
problem of designing and deploying an intermodal Autonomous Mobility-on-Demand system, whereby AVs service travel demands
jointly with public transit, in terms of fleet sizing, vehicle autonomy, and public transit service frequency.
Our framework is modular and compositional, allowing one to describe the design problem as the interconnection of its
individual components and to tackle it from a system-level perspective. To showcase our methodology, we present a
real-world case study for Washington D.C., USA. Our work suggests that it is possible to create user-friendly optimization
tools to systematically assess costs and benefits of interventions, and that such analytical techniques might gain a momentous
role in policy-making in the future.
@inproceedings{ZardiniEtAl2020,
author = {Zardini, G. and Lanzetti, N. and Salazar, M. and Censi, A. and Frazzoli, E. and Pavone, M.},
title = {On the Co-Design of AV-Enabled Mobility Systems},
booktitle={2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)},
year = {2020},
address = {Rhodes, Greece},
month = sep
doi={10.1109/ITSC45102.2020.9294499}
}
The design of Autonomous Vehicles (AVs) and the design of AVs-enabled mobility systems are closely coupled.
Indeed, knowledge about the intended service of AVs would impact their design and deployment process, whilst insights
about their technological development could significantly affect transportation management decisions. This calls for tools
to study such a coupling and co-design AVs and AVs-enabled mobility systems in terms of different objectives. In this paper,
we instantiate a framework to address such co-design problems. In particular, we leverage the recently developed theory of
co-design to frame and solve the problem of designing and deploying an intermodal Autonomous Mobility-on-Demand system, whereby
AVs service travel demands jointly with public transit, in terms of fleet sizing, vehicle autonomy, and public transit service
frequency. Our framework is modular and compositional, allowing to describe the design problem as the interconnection of its
individual components and to tackle it from a system-level perspective. Moreover, it only requires very general monotonicity
assumptions and it naturally handles multiple objectives, delivering the rational solutions on the Pareto front and thus enabling
policy makers to select a solution through “political” criteria. To showcase our methodology, we present a real- world case study
for Washington D.C., USA. Our work suggests that it is possible to create user- friendly optimization tools to systematically
assess the costs and benefits of interventions, and that such analytical techniques might gain a momentous role in policy-making
in the future.
@inproceedings{zardini2020towards,
author = {Zardini, G. and Lanzetti, N. and Salazar, M. and Censi, A. and Frazzoli, E. and Pavone, M.},
title = {Towards a Co-Design Framework for Future Mobility Systems},
booktitle = {99th Annual Meeting of the Transportation Research Board},
year = {2020},
address = {Washington D.C., United States},
month = jan
}
Related Talks
Co-Design to Enable User-Friendly Tools to Assess the Impact of Future Mobility Solutions
IEEE International Conference on Intelligent Transportation Systems, September 2020
Stanford University, Autonomous Systems Lab, September 2019
