A Multi-Objective Optimization and Hybrid Heuristic Approach for Urban Bus Route Network Design

Abstract

This study proposed a hybrid optimization model for urban bus transit route network design problem (TRNDP). Although several mathematical methods had been developed to make the problem tractable, the methods relied on excessive assumptions, which resulted in oversimplification or idealization of the problems. In light of these considerations, a multi-level and multi-mode network design method was introduced in this study. A multi-level network consisted of three levels: skeleton network, arterial network, and feeder network. The different levels of network were associated with different modes of public transportation (such as subway, light rail transit, trolleybus, BRT, normal bus, and community branch) based on the features of bus routes, city sizes, etc. In addition, according to the respective features of urban transit route network structures, we developed different optimization models for different levels. Finally, the proposed methodology was applied to case studies of the city of Zhaoyuan in China and the transit network of Mandl benchmark. The results showed that the total travel time for the proposed method was significantly lower than that of the competing method, with a 21.51% reduction. In addition, the proposed method provided 85.23% direct travelers, 14.65% travelers with one transfer, 0.12% travelers with two transfers, and no unsatisfied demand, which were better than the results from the compared method.

On the Role of Multi-Objective Optimization to the Transit Network Design Problem

“…Arbex et al [1] introduced an Alternating Objective Genetic Algorithm (AOGA) to boost efficiency, in which the objectives are cyclically alternated along the generations. Wang et al [22] formulates the TNDP as a multi-level task described by the skeleton network, arterial network, and feeder network, proposing an hybrid heuristic approach. Despite the relevance of multi-optimization stances to assess alternative solutions, existing works generally focus on road developments and reforms [4], including street constructions, lane additions, allocations and reorientations [15].…”

Evolutionary Algorithm for Multiobjective Optimization Based on Density Estimation Ranking

“…The DER simultaneously includes the convergence and distribution information of individuals to enhance MOEA performance (2) The basic approach of DER is simple, considering the contribution of individuals to population convergence in sparse area, and the DER shifts poor convergence individuals to density crowded area. In this way, these individuals with poor convergence performance will be given a high density value, which is easier to be eliminated in the evolution process, so improving the efficiency of the algorithm.…”

“…Multiobjective optimization problems (MOPs) are common in the real-life, e.g., robotics [1], urban bus transit route network design problem [2], smart grids [3], and electricity selling market [4]. These problems aim to optimize more than two conflicting objectives at the same time, which can be mathematically formulated clearly depict as follows.…”

A Combined Modal Split and Traffic Assignment Model With Capacity Constraints for Siting Remote Park-and-Ride Facilities

“…The proposed bi-level model denotes an NP-hard problem, including the NL model and multimodal subnetwork, which further increases the complexity and introduces challenges to solving the problem. The bi-level model represents a mixed-integer nonlinear program that has been used for the transport network design [39], [40]. In order to reduce the complexity and difficulty of solving this NP-hard problem, this paper transforms the mixed-integer nonlinear program from a continuous problem into a discrete problem.…”