An autonomous mobility-on-demand dispatching system can reduce average passenger wait time by 30 percent and increase trip dispatch success rate by 8 percent.
A University of California-Irvine study modeled the potential benefits of dispatch system automation.
Made Public Date
06/26/2019

302

New York City
New York
United States
Identifier
2019-01375
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Managing Autonomous Mobility on Demand Systems for Better Passenger Experience

Summary Information

Autonomous mobility on demand (AMOD) systems, though still in their infancy, have very promising prospects in providing urban population with sustainable and safe personal mobility in the near future. Although numerous existing research addresses taxi dispatching problems using various methodologies including techniques in multi-agent systems, no real research has been performed that addresses methodologies on dispatching for a better user experience. Unlike conventional taxi dispatching systems where it is often difficult to coordinate because of human factors (e.g., the drivers may not follow the dispatcher’s instructions carefully) AMOD systems make it possible to implement a much higher level of autonomy through delicate, system-wide coordination. Moreover, it is challenging to achieve near instant availability using conventional approaches.

Researchers at the University of California-Irvine tried to bridge this gap by introducing a new algorithm identified as the Expand and Target (EAT) algorithm. This algorithm enables managing authorities of AMOD systems to automatically and effectively dispatch autonomous vehicles while updating adjacency schedules for better passenger experience. First, it increases the possibility of finding global optimal solutions; second, it reduces computation time by avoiding looping all the possible vehicles; third, it connects isolated areas with other dispatching areas and makes updates to the adjacency schedule automatically, which increases the dispatch success rate.

Methodology

An agent-based simulation platform was implemented that integrated the EAT algorithm with six different scheduling strategies for dispatching autonomous vehicles. An empirical analysis was performed that compared both the average passenger waiting time and the trip success rate of the AMOD systems using the six different dispatching approaches with 2013 New York City Taxi Data.

Findings

Experimental results demonstrated that the algorithm significantly improved passengers’ experience by reducing the average passenger waiting time by up to 29.82 percent and increasing the trip success rate by up to 7.65 percent.

Managing Autonomous Mobility on Demand Systems for Better Passenger Experience

Managing Autonomous Mobility on Demand Systems for Better Passenger Experience
Publication Sort Date
07/01/2015
Author
Shen, Wen; and Cristina Lopes
Publisher
University of California, Irvine

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