Autonomous Vehicle Public Transportation System: Scheduling and Admission Control

TL;DR

This paper proposes a novel autonomous vehicle-based public transportation system that optimizes scheduling and admission control to maximize efficiency and profit, using advanced optimization techniques validated with real-world data.

Contribution

It introduces a new AV public transportation framework with integrated scheduling and admission control, employing a genetic algorithm for optimization.

Findings

The proposed system effectively manages AV fleets for point-to-point ride sharing.

The genetic algorithm approach improves admission control decisions.

Validation with real-world data demonstrates system efficiency and profitability.

Abstract

Technology of autonomous vehicles (AVs) is getting mature and many AVs will appear on the roads in the near future. AVs become connected with the support of various vehicular communication technologies and they possess high degree of control to respond to instantaneous situations cooperatively with high efficiency and flexibility. In this paper, we propose a new public transportation system based on AVs. It manages a fleet of AVs to accommodate transportation requests, offering point-to-point services with ride sharing. We focus on the two major problems of the system: scheduling and admission control. The former is to configure the most economical schedules and routes for the AVs to satisfy the admissible requests while the latter is to determine the set of admissible requests among all requests to produce maximum profit. The scheduling problem is formulated as a mixed-integer linear program and the admission control problem is cast as a bilevel optimization, which embeds the scheduling problem as the major constraint. By utilizing the analytical properties of the problem, we develop an effective genetic-algorithm-based method to tackle the admission control problem. We validate the performance of the algorithm with real-world transportation service data.