An integrated assignment, routing, and speed model for roadway mobility and transportation with environmental, efficiency, and service goals

TL;DR

This paper develops a comprehensive multi-objective model for autonomous vehicle assignment, routing, and speed control in smart cities, balancing environmental impact, efficiency, and service quality.

Contribution

It introduces a novel trilevel multi-objective formulation architecture and an integrated algorithm for urban vehicle routing and speed optimization considering traffic congestion.

Findings

Effective approximation of Pareto fronts for conflicting objectives

Successful integration of assignment, routing, and speed decisions

Algorithm demonstrates computational efficiency on urban networks

Abstract

Managing all the mobility and transportation services with autonomous vehicles for users of a smart city requires determining the assignment of the vehicles to the users and their routing in conjunction with their speed. Such decisions must ensure low emission, efficiency, and high service quality by also considering the impact on traffic congestion caused by other vehicles in the transportation network. In this paper, we first propose an abstract trilevel multi-objective formulation architecture to model all vehicle routing problems with assignment, routing, and speed decision variables and conflicting objective functions. Such an architecture guides the development of subproblems, relaxations, and solution methods. We also propose a way of integrating the various urban transportation services by introducing a constraint on the speed variables that takes into account the traffic volume caused across the different services. Based on the formulation architecture, we introduce a (bilevel) problem where assignment and routing are at the upper level and speed is at the lower level. To address the challenge of dealing with routing problems on urban road networks, we develop an algorithm that alternates between the assignment-routing problem on an auxiliary complete graph and the speed optimization problem on the original non-complete graph. The computational experiments show the effectiveness of the proposed approach in determining approximate Pareto fronts among the conflicting objectives.