Authors:
Jiyao Li
and
Vicki H. Allan
Affiliation:
Department of Computer Science, Utah State University, Logan, Utah, U.S.A.
Keyword(s):
Ridesharing Service, Task Assignment, Vehicle Repositioning, Q-learning.
Abstract:
In this paper, we propose a unified mechanism known as T-Balance for scheduling taxis across a city. Balancing the supplies and demands in a city scale is a challenging problem in the field of the ride-sharing service.
To tackle the problem, we design a unified mechanism considering two important processes in ride-sharing
service: ride-matching and vacant taxi repositioning. For rider-matching, the Scoring Ride-matching with
Lottery Selection (SRLS) is proposed. With the help of Lottery Selection (LS) and smoothed popularity score,
the Scoring Ride-matching with Lottery Selection (SRLS) can balance supplies and demands well, both in the
local neighborhood areas and hot places across the city. In terms of vacant taxi repositioning, we propose Qlearning Idle Movement (QIM) to direct vacant taxis to the most needed places in the city, adapting to dynamic
change environments. The experimental results verify that the unified mechanism is effective and flexible.