Publication | Closed Access
Operations of Shared Autonomous Vehicle Fleet for Austin, Texas, Market
306
Citations
3
References
2015
Year
Automated VehiclesEngineeringTransportation SystemConnected CarAutomationBusinessLogisticsSystems EngineeringFleet ManagementVehicle Routing ProblemTransportation ResearchMobility ServiceDriverless TaxiTransportation EngineeringCommercial SalesTraffic ManagementOn-demand TransportOperations Research
Automated vehicles promise transformative transportation, and the emerging shared autonomous vehicle (SAV) combines on‑demand rentals with driverless taxis. This study evaluates the impact of a low‑penetration SAV fleet (1.3 % of regional trips) in Austin, Texas. Using a 32,272‑link network and MATSIM dynamic traffic assignment, the authors simulated 5‑minute departure windows for SAVs based on a planning‑model trip sample across traffic analysis zones. The simulation indicates each SAV could replace roughly nine conventional vehicles while keeping average wait times near one minute, but may increase vehicle‑miles traveled by about 8 % due to empty repositioning.
The emergence of automated vehicles holds great promise for the future of transportation. Although commercial sales of fully self-driving vehicles will not commence for several more years, once these sales are possible a new transportation mode for personal travel promises to arrive. This new mode is the shared autonomous (or fully automated) vehicle (SAV), combining features of short-term, on-demand rentals with self-driving capabilities: in essence, a driverless taxi. This investigation examined the potential implications of the SAV at a low level of market penetration (1.3% of regional trips) by simulating a feet of SAVs serving travelers in the 12-mi by 24-mi regional core of Austin, Texas. The simulation used a sample of trips from the region's planning model to generate demand across traffic analysis zones and a 32,272-link network. Trips called on the vehicles in 5-min departure time windows, with link-level travel times varying by hour of day based on MATSIM's dynamic traffic assignment simulation software. Results showed that each SAV could replace about nine conventional vehicles within the 24-mi by 12-mi area while still maintaining a reasonable level of service (as proxied by user wait times, which averaged just 1 min). Additionally, approximately 8% more vehicle miles traveled (VMT) may be generated because of SAV's ability to journey unoccupied to the next traveler or relocate to a more favorable position in anticipation of its next period demand.
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