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Electrifying the ride-hailing services has the potential to significantly reduce greenhouse gas emissions in the shared mobility sector. However, these emission reduction benefits depend on the utilization of EVs to serve trip requests, especially during the fleet electrification process. In this paper, we evaluated the performance and emission impacts of ride-hailing service with three dispatching policies and various EV penetration levels in the ride-hailing fleet. A large-scale simulation platform was developed for the city of San Francisco in SUMO to enable the application of ride-hailing, electric vehicle charging, and idle vehicle repositioning. Simulation results indicate that with a 60% EVs in the simulated fleet, the off-peak EV priority policy and off-peak EV only policy can reduce CO2 emissions by 32% - 40% while preserving the mobility performance in terms of deadheading, total travel distance, and average rider pick-up time. It is important for ride-hailing platforms to increase the zero-emission rides and encourage ride pooling to comply with California’s Clean Miles Standard.
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Curb Allocation and Pick-Up Drop-Off Aggregation for a Shared Autonomous Vehicle Fleet
Advances in information technologies and vehicle automation have birthed new transportation services, including shared autonomous vehicles (SAVs). Shared autonomous vehicles are on-demand self-driving taxis, with flexible routes and schedules, able to replace personal vehicles for many trips in the near future. The siting and density of pick-up and drop-off (PUDO) points for SAVs, much like bus stops, can be key in planning SAV fleet operations, since PUDOs impact SAV demand, route choices, passenger wait times, and network congestion. Unlike traditional human-driven taxis and ride-hailing vehicles like Lyft and Uber, SAVs are unlikely to engage in quasi-legal procedures, like double parking or fire hydrant pick-ups. In congested settings, like central business districts (CBD) or airport curbs, SAVs and others will not be allowed to pick up and drop off passengers wherever they like. This paper uses an agent-based simulation to model the impact of different PUDO locations and densities in the Austin, Texas CBD, where land values are highest and curb spaces are coveted. In this paper 18 scenarios were tested, varying PUDO density, fleet size and fare price. The results show that for a given fare price and fleet size, PUDO spacing (e.g., one block vs. three blocks) has significant impact on ridership, vehicle-miles travelled, vehicle occupancy, and revenue. A good fleet size to serve the region’s 80 core square miles is 4000 SAVs, charging a $1 fare per mile of travel distance, and with PUDOs spaced three blocks of distance apart from each other in the CBD.
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- PAR ID:
- 10494392
- Publisher / Repository:
- Sage Publications
- Date Published:
- Journal Name:
- International Regional Science Review
- Volume:
- 47
- Issue:
- 2
- ISSN:
- 0160-0176
- Page Range / eLocation ID:
- 131 to 158
- Subject(s) / Keyword(s):
- pickup and drop-off locations shared autonomous vehicles autonomous taxis ridesharing stations dynamic ride-sharing curb management
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1177/01600176231160498
