Efficient Route Planning for Real-Time Demand-Responsive Transit

Demand Responsive Transit (DRT) responds to the dynamic users’ requests without any fixed routes and timetablesand determines the stop and the start according to the demands. This study explores the optimization of dynamicvehicle scheduling and real-time route planning in urban public transportation systems, with a focus on busservices. It addresses the limitations of current shared mobility routing algorithms, which are primarily designedfor simpler, single origin/destination scenarios, and do not meet the complex demands of bus transit systems. Theresearch introduces an route planning algorithm designed to dynamically accommodate passenger travel needsand enable real-time route modifications. Unlike traditional methods, this algorithm leverages a queue-based,multi-objective heuristic A∗ approach, offering a solution to the inflexibility and limited coverage of suburbanbus routes. Also, this study conducts a comparative analysis of the proposed algorithm with solutions based onGenetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO), focusing on calculation time, routelength, passenger waiting time, boarding time, and detour rate. The findings demonstrate that the proposedalgorithmsignificantly enhances route planning speed, achieving an 80–100-fold increase in efficiency over existingmodels, thereby supporting the real-time demands of Demand-Responsive Transportation (DRT) systems. Thestudy concludes that this algorithm not only optimizes route planning in bus transit but also presents a scalablesolution for improving urban mobility.

A novel GIS-based decision-making framework for the school bus routing problem

The school bus routing problem(SBRP)is a central issue in transportation planning and optimization systems.SBRP seeks to plan an efficient schedule for a fleet of school buses where each bus picks up students from various bus stops and delivers them to their designated schools while satisfying various constraints such as the maximum capacity of a bus,and the time window of a school.Due to its inherent complexity,many heuristics have been proposed to solve this combinatorial problem in an effective way.In this paper,a novel geographic information systems(GIS)-based decisionmaking framework that combines GIS,clustering techniques,network cutting techniques,and a hybrid ant colony optimization metaheuristic with the iterated Lin–Kernighan local improvement heuristic is proposed for solving the SBRP as a split delivery vehicle routing problem(SDVRP).Experiments were conducted for evaluating the proposed framework by comparing the results for solving 11 routing problems using both the proposed decision-making framework and Arc-GIS 9.2 Network Analyst which uses the greedy Dijkstra’s algorithm.The reported results of the proposed framework generally outperform that of the ArcGIS Network Analyst.In addition,the proposed decision-making framework was applied to solve a real life SBRP to demonstrate its application.