Demand-Responsive Transportation Vehicle Routing Optimization Based on Two-Stage Method  

作　　者：Jingfa Ma Hu Liu Lingxiao Chen 

机构地区：[1]School of Railway Transportation,Shanghai Institute of Technology,Shanghai,201418,China

出　　处：《Computers, Materials & Continua》2024年第10期443-469,共27页计算机、材料和连续体（英文）

摘　　要：Demand-responsive transportation(DRT)is a flexible passenger service designed to enhance road efficiency,reduce peak-hour traffic,and boost passenger satisfaction.However,existing optimization methods for initial passenger requests fall short in addressing real-time passenger needs.Consequently,there is a need to develop realtime DRT route optimization methods that integrate both initial and real-time requests.This paper presents a twostage,multi-objective optimization model for DRT vehicle scheduling.The first stage involves an initial scheduling model aimed at minimizing vehicle configuration,and operational,and CO_(2)emission costs while ensuring passenger satisfaction.The second stage develops a real-time scheduling model to minimize additional operational costs,penalties for time window violations,and costs due to rejected passengers,thereby addressing real-time demands.Additionally,an enhanced genetic algorithm based on Non-dominated Sorting Genetic Algorithm-II(NSGA-II)is designed,incorporating multiple crossover points to accelerate convergence and improve solution efficiency.The proposed scheduling model is validated using a real network in Shanghai.Results indicate that realtime scheduling can serve more passengers,and improve vehicle utilization and occupancy rates,with only a minor increase in total operational costs.Compared to the traditional NSGA-II algorithm,the improved version enhances convergence speed by 31.7%and solution speed by 4.8%.The proposed model and algorithm offer both theoretical and practical guidance for real-world DRT scheduling.

关 键 词：Demand responsive transit genetic algorithm muti-objective optimization artificial intelligence applications 

分 类 号：U495[交通运输工程—交通运输规划与管理]