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A platform for research: civil engineering, architecture and urbanism
Ride-pooling Electric Autonomous Mobility-on-Demand:Joint optimization of operations and fleet and infrastructure design
This paper presents a modeling and design optimization framework for an Electric Autonomous Mobility-on-Demand system that allows for ride-pooling, i.e., multiple users can be transported at the same time towards a similar direction to decrease vehicle hours traveled by the fleet at the cost of additional waiting time and delays caused by detours. In particular, we first devise a multi-layer time-invariant network flow model that jointly captures the position and state of charge of the vehicles. Second, we frame the time-optimal operational problem of the fleet, including charging and ride-pooling decisions as a mixed-integer linear program, whereby we jointly optimize the placement of the charging infrastructure. Finally, we perform a case-study using Manhattan taxi-data. Our results indicate that jointly optimizing the charging infrastructure placement allows to decrease overall energy consumption of the fleet and vehicle hours traveled by approximately 1% compared to a heuristic placement. Most significantly, ride-pooling can decrease such costs considerably more, and up to 45%. Finally, we investigate the impact of the vehicle choice on the energy consumption of the fleet, comparing a lightweight two-seater with a heavier four-seater, whereby our results show that the former and latter designs are most convenient for low- and high-demand areas, respectively.
Ride-pooling Electric Autonomous Mobility-on-Demand:Joint optimization of operations and fleet and infrastructure design
This paper presents a modeling and design optimization framework for an Electric Autonomous Mobility-on-Demand system that allows for ride-pooling, i.e., multiple users can be transported at the same time towards a similar direction to decrease vehicle hours traveled by the fleet at the cost of additional waiting time and delays caused by detours. In particular, we first devise a multi-layer time-invariant network flow model that jointly captures the position and state of charge of the vehicles. Second, we frame the time-optimal operational problem of the fleet, including charging and ride-pooling decisions as a mixed-integer linear program, whereby we jointly optimize the placement of the charging infrastructure. Finally, we perform a case-study using Manhattan taxi-data. Our results indicate that jointly optimizing the charging infrastructure placement allows to decrease overall energy consumption of the fleet and vehicle hours traveled by approximately 1% compared to a heuristic placement. Most significantly, ride-pooling can decrease such costs considerably more, and up to 45%. Finally, we investigate the impact of the vehicle choice on the energy consumption of the fleet, comparing a lightweight two-seater with a heavier four-seater, whereby our results show that the former and latter designs are most convenient for low- and high-demand areas, respectively.
Ride-pooling Electric Autonomous Mobility-on-Demand:Joint optimization of operations and fleet and infrastructure design
Paparella, Fabio (author) / Chauhan, Karni (author) / Koenders, Luc (author) / Hofman, Theo (author) / Salazar, Mauro (author)
2025-01-01
Paparella , F , Chauhan , K , Koenders , L , Hofman , T & Salazar , M 2025 , ' Ride-pooling Electric Autonomous Mobility-on-Demand : Joint optimization of operations and fleet and infrastructure design ' , Control Engineering Practice , vol. 154 , 106169 . https://doi.org/10.1016/j.conengprac.2024.106169
Article (Journal)
Electronic Resource
English
Road Infrastructure Design towards Passenger Ride Comfort for Autonomous Public Transport
| UB Braunschweig | 2020