Open Access

ARTICLE

Efficient Route Planning for Real-Time Demand-Responsive Transit

Hongle Li¹, SeongKi Kim^2,*

1 Development Department, Changseol Build System, Daegu, 42601, South Korea
2 Division of Software Convergence, Sangmyung University, Seoul, 03016, South Korea

* Corresponding Author: SeongKi Kim. Email:

(This article belongs to the Special Issue: Intelligent Computing Techniques and Their Real Life Applications)

Computers, Materials & Continua 2024, 79(1), 473-492. https://doi.org/10.32604/cmc.2024.048402

Received 06 December 2023; Accepted 11 February 2024; Issue published 25 April 2024

Abstract

Demand Responsive Transit (DRT) responds to the dynamic users’ requests without any fixed routes and timetables and determines the stop and the start according to the demands. This study explores the optimization of dynamic vehicle scheduling and real-time route planning in urban public transportation systems, with a focus on bus services. It addresses the limitations of current shared mobility routing algorithms, which are primarily designed for simpler, single origin/destination scenarios, and do not meet the complex demands of bus transit systems. The research introduces an route planning algorithm designed to dynamically accommodate passenger travel needs and 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 suburban bus routes. Also, this study conducts a comparative analysis of the proposed algorithm with solutions based on Genetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO), focusing on calculation time, route length, passenger waiting time, boarding time, and detour rate. The findings demonstrate that the proposed algorithm significantly enhances route planning speed, achieving an 80–100-fold increase in efficiency over existing models, thereby supporting the real-time demands of Demand-Responsive Transportation (DRT) systems. The study concludes that this algorithm not only optimizes route planning in bus transit but also presents a scalable solution for improving urban mobility.

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Keywords

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