Optimization for Feeder Bus Route Model Design with Station Transfer

Abstract

To fully take the advantages of conventional bus and subway, and to maximize the overall feeder efficiency of the public transport system, the topic of feeder bus route optimization is studied in this paper. Considering the origin destination demand of passenger flow between subway stations and bus stations and transfer characteristics, the objective function is established with the minimum sum of bus operation cost and passenger travel cost. Taking into account the integrity of the feeder bus route, the rationality of the route, the route capacity and the station transfer factors, the constraints of the optimization model are constructed. Based on the idea of the genetic algorithm, the solution algorithm of the optimization model is developed. The genetic algorithm and enumeration algorithm are used to solve the optimization of the feeder bus route in this case, and the accuracy and efficiency of the solution are analyzed. The influence of the number of feeder bus routes on the system in the case network is compared and discussed. We compare and analyze the differences between the original bus network and the feeder bus network in terms of bus operation cost, passenger flow demand and total passenger travel cost. The research shows that the model and algorithm can find the approximate optimal solution of the feeder bus network scheme related to the subway through fewer iterations. The number of routes in the model has little impact on the whole feeder system, and the optimization scheme using five routes is effective and reasonable in this paper. Compared with the existing bus network, the optimization scheme has obvious advantages in improving the passenger-carrying rate, reducing the per capita travel cost and improving the overall operation efficiency of the system.

Optimizing the Three-Dimensional Multi-Objective of Feeder Bus Routes Considering the Timetable

“…To align feeder bus routes with prevailing conditions, Wei et al [8] utilized temporal and spatial data to construct a dual-layer planning model, employing an ant colony optimization algorithm to reduce total bus travel time for passengers significantly. Cao et al [9] considered passenger demand and transfer characteristics to refine feeder bus route optimization, devising a genetic algorithm to minimize operational and passenger travel expenses and achieve optimal outcomes. Cipriani et al [10] developed an algorithm to tailor route plans and frequencies to concentrated customer demands at a singular destination, while subsequent research [11] adapted this approach to cater to diverse origins and destinations, reflecting the unique demand patterns of feeder bus routes.…”

Collaborative Optimization Model for the Design and Operation of Feeder Bus Routes Based on Urban Metro

“…Traveling through metro mass transit is 20% cheaper [4]. However, compared with the urban metro, although the conventional bus has a small capacity and slow speed, it has the advantages of low cost, vital accessibility, and comprehensive coverage [5]. Terefore, studying the design and operation optimization of the feeder bus routes can help build a perfect feeder bus service system, give full play to their advantages, expand cooperation with the urban metro, and reduce competition.…”

The Multi-Type Demands Oriented Framework for Flex-Route Transit Design

“…Though the fixed-route service offered by traditional transit may benefit stable demands, it could hardly adapt to the temporal and spatial variations in unstable demands [2][3][4][5]. As a result, the popularity of traditional transit is decreasing, while the usage of private transit is increasing [6][7][8][9][10][11], which leads to heavy traffic congestion and serious air pollution [12][13][14][15][16].…”

“…If the trip j accepts the real-time demand n * (z j,n * = 1), it must meet the requirement of station sequence, as constraints (2) to (7) show, maintain the service for reserved demands, as constraints ( 8) to (10) show, and build the temporal and spatial relationship with the real-time demand n * , as constraints (18) to (21) show.…”