Compared to Conventional Trip Sharing Solutions that Spend more than Half of Trips Serving Only a Single Passenger, Models of Trip Sharing Augmented with Autonomous Vehicle Technology Indicate Ridership with Three to Four Travelers can be Maintained.
An optimization model examines trip sharing solutions augmented with autonomous vehicle technology.
Made Public Date
02/25/2021
Identifier
2021-B01535
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The Benefits of Autonomous Vehicles for Community-Based Trip Sharing

Summary Information

Community-based trip sharing is a model that has the potential to reduce congestion and parking space utilization by leveraging the structure of commuting patterns and urban communities. A recent paper sought to quantify the benefits of autonomous vehicles for community-based trip sharing, and to compare it to a conventional carpooling platform. 

In order to evaluate the potential benefits of such a model, researchers applied it to a dataset tracking daily commuting information from 15 parking structures in downtown Ann Arbor, MI, collected in April 2017. The paper evaluated two potential depot configurations for the autonomous vehicle trip sharing service: one in which all neighborhoods were served by vehicles from a single, centralized depot, and one in which neighborhoods are served by smaller, local depots. Additionally, the model compared a lexicographic approach that minimized the number of vehicles, and a single objective approach that only minimized the overall travel distance.

  • The introduction of autonomous vehicles to the "commute trip sharing problem" (CTSP) was found to serve, on average, an order of magnitude more trips than conventional vehicles. While implementing the CTSP with conventional vehicles was found to reduce the total necessary vehicle count for travelers by 45-56 percent, implementing the CTSP with AVs reduced vehicle count by up to 96 percent. 
  • With the centralized depot configuration, the study projected a reduction in total Vehicle Miles Traveled (VMT) of 15 to 19 percent. However, this reduction was actually less than the reduction from conventional trip sharing, which reduced VMT by 38 to 40 percent, in part because of the much higher volume of trips served. 
  • While conventional trip sharing rides spend more than 50 percent of the trip serving only a single passenger, autonomous vehicle enabled trip sharing configurations spend relatively more time traveling with three or four travelers, indicating increased efficiency. 
  • Researchers concluded that a centralized, lexicographic model was the most efficient of the types analyzed, given that it had the greatest vehicle reduction potential, and the strongest logistical benefits. 
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