Customized Bus Stop Location Model Based on Dual Population Adaptive Immune Algorithm

Abstract

Selecting optimal locations for customized bus stops is crucial for enhancing the adoption rate of customized bus services, reducing operational costs, and, consequently, mitigating traffic congestion. This study leverages ride-hailing data to analyze the distance between passengers and bus stops, as well as the operational costs associated with establishing these stops, to construct a customized bus stop location model. To address the limited local search capability of conventional immune algorithms, we propose a Dual Population Adaptive Immunity Algorithm (DPAIA) to solve the bus stop location problem. Finally, we conduct simulation experiments using passenger travel data from a ride-hailing company in Chengdu to evaluate the proposed customized bus stop location model. Through simulations with Chengdu ride-hailing data, the DPAIA algorithm minimized the weighted cost to CNY 28.95 ten thousand, outperforming all counterparts. Although proposing 9–11 more stops than competitors, this increase slightly impacts costs while markedly reducing passenger walking distances. Optimizing station placement to meet demand and road networks, our model endorses 50 strategic bus stops, enhancing service accessibility and potentially easing urban congestion while boosting operator profits.