Zizhan Jiang , Jiying Wang , Siyuan Huang , Hua Daniel Xu
{"title":"A solution to the Single-School school bus routing problem considering accessibility and economy","authors":"Zizhan Jiang , Jiying Wang , Siyuan Huang , Hua Daniel Xu","doi":"10.1016/j.trip.2025.101506","DOIUrl":null,"url":null,"abstract":"<div><div>The school bus routing problem (SBRP) involves the optimal placement of bus stops and design of bus routes. A well-designed SBRP strategy can reduce the operating costs of the school bus system, improve accessibility, and ensure timely arrival for teachers and students. This paper integrates the heuristic Dijkstra algorithm with Geographic Information Systems (GIS) to propose a three-stage heuristic GIS optimization method (THGO) for solving the Single-School Multi-Route School Bus Routing Problem (SSMR-SBRP).</div><div>In the first stage, we use a clustering method based on the minimum covering circle to set bus stops that passengers can reach on foot. In the second stage, we establish a mixed-integer linear programming (MILP) model for SSMR-SBRP, aiming to minimize the total travel distance. Heuristic algorithms, such as Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Floyd-Warshall, are employed to determine the initial solution, which includes the optimal number of buses and their preliminary routes. In the third stage, Dijkstra’s algorithm is combined with GIS to optimize the bus routes, with a focus on accessibility and economy.</div><div>Experimental results demonstrate that the THGO method can efficiently find the global optimal solution while satisfying constraints such as time windows, bus capacity, and road directionality. The optimized routes reduce driving distance by 23.13%, save 30.41% of driving time, and cut operating costs by 26.02% compared to the current school bus system.</div><div>This paper validates the feasibility and advantages of the THGO method using the SSMR-SBRP case study. Moreover, by introducing resource-sharing and constraint mechanisms into the model, the THGO framework can be extended to multi-school, heterogeneous school bus scenarios, offering a practical solution to the Multi-School Mixed School Bus Routing Problem.</div></div>","PeriodicalId":36621,"journal":{"name":"Transportation Research Interdisciplinary Perspectives","volume":"32 ","pages":"Article 101506"},"PeriodicalIF":3.8000,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transportation Research Interdisciplinary Perspectives","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S259019822500185X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"TRANSPORTATION","Score":null,"Total":0}
引用次数: 0
Abstract
The school bus routing problem (SBRP) involves the optimal placement of bus stops and design of bus routes. A well-designed SBRP strategy can reduce the operating costs of the school bus system, improve accessibility, and ensure timely arrival for teachers and students. This paper integrates the heuristic Dijkstra algorithm with Geographic Information Systems (GIS) to propose a three-stage heuristic GIS optimization method (THGO) for solving the Single-School Multi-Route School Bus Routing Problem (SSMR-SBRP).
In the first stage, we use a clustering method based on the minimum covering circle to set bus stops that passengers can reach on foot. In the second stage, we establish a mixed-integer linear programming (MILP) model for SSMR-SBRP, aiming to minimize the total travel distance. Heuristic algorithms, such as Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Floyd-Warshall, are employed to determine the initial solution, which includes the optimal number of buses and their preliminary routes. In the third stage, Dijkstra’s algorithm is combined with GIS to optimize the bus routes, with a focus on accessibility and economy.
Experimental results demonstrate that the THGO method can efficiently find the global optimal solution while satisfying constraints such as time windows, bus capacity, and road directionality. The optimized routes reduce driving distance by 23.13%, save 30.41% of driving time, and cut operating costs by 26.02% compared to the current school bus system.
This paper validates the feasibility and advantages of the THGO method using the SSMR-SBRP case study. Moreover, by introducing resource-sharing and constraint mechanisms into the model, the THGO framework can be extended to multi-school, heterogeneous school bus scenarios, offering a practical solution to the Multi-School Mixed School Bus Routing Problem.