Personalised and Coordinated Demand-Responsive Feeder Transit Service Design: A Genetic Algorithms Approach

Sun, Bo; Wei, Ming; Yang, Chunfeng; Xu, Zhihuo; Wang, Han

doi:10.3390/fi10070061

Cited by 11 publications

(11 citation statements)

References 33 publications

(48 reference statements)

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“…The results show that the fixed-route service was more effective. Sun et al [12] established a mixed-integer linear programming model aiming at minimizing the total travel time of all passengers. A distributed genetic algorithm was designed, and its effectiveness was verified.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimization for Feeder Bus Route Model Design with Station Transfer

Cao¹,

Jiang²,

Wang³

2022

Sustainability

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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.

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Section: Literature Reviewmentioning

confidence: 99%

Optimization for Feeder Bus Route Model Design with Station Transfer

Cao¹,

Jiang²,

Wang³

2022

Sustainability

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“…In the route-building method, a set of feasible routes is firstly generated and then used to search and fine-tune initial solutions according to the proposed constraints at a reasonable computational cost. In meta-heuristics method, simulated annealing [25][26][27], tabu search [18,23,28], genetic algorithms [29][30][31][32], and colony algorithm approach [33,34] are widely used to contend with DRTs.…”

Section: Literaturementioning

confidence: 99%

“…Since they are an extension of VRPs, their solution algorithms can be used with each other. However, an integration of DRTs and RS is much more complicated than DRTs and RS alone [7][8][9]31]. 2The basic assumption of traditional DRTs is all of the demand points must be visited by vehicles.…”

Section: Literaturementioning

confidence: 99%

An Optimization Model for Demand-Responsive Feeder Transit Services Based on Ride-Sharing Car

Sun

Wei

2019

Information

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Ride-sharing (RS) plays an important role in saving energy and alleviating traffic pressure. The vehicles in the demand-responsive feeder transit services (DRT) are generally not ride-sharing cars. Therefore, we proposed an optimal DRT model based on the ride-sharing car, which aimed at assigning a set of vehicles, starting at origin locations and ending at destination locations with their service time windows, to transport passengers of all demand points to the transportation hub (i.e., railway, metro, airport, etc.). The proposed model offered an integrated operation of pedestrian guidance (from unvisited demand points to visited ones) and transit routing (from visited ones to the transportation hub). The objective was to simultaneously minimize weighted passenger walking and riding time. A two-stage heuristic algorithm based on a genetic algorithm (GA) was adopted to solve the problem. The methodology was tested with a case study in Chongqing City, China. The results showed that the model could select optimal pick-up locations and also determine the best pedestrian and route plan. Validation and analysis were also carried out to assess the effect of maximum walking distance and the number of share cars on the model performance, and the difference in quality between the heuristic and optimal solution was also compared.

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“…For example, most researches ignored the time window constraints at the transit hub where good transfers can ensure passengers to proceed their journeys as expected. In [40], passengers' personalized subway schedules were taken as input to realize good transfers between DRC and rail transit. However, successful transfers could not be guaranteed for the soft constraint of passengers' arrival time [40].…”

Section: Introductionmentioning

confidence: 99%

“…In [40], passengers' personalized subway schedules were taken as input to realize good transfers between DRC and rail transit. However, successful transfers could not be guaranteed for the soft constraint of passengers' arrival time [40]. Besides, to authors' knowledge, no research has discussed the elasticity of DRC operation plan, where unforeseen delay often occurs because of some stochastic factors.…”

Section: Introductionmentioning

confidence: 99%

Compatibility-Based Approach for Routing and Scheduling the Demand Responsive Connector

2020

IEEE Access

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This study concentrates on the routing and scheduling problem of Demand Responsive Connector to build feeder plans for people traveling from and to transit hub. An in-depth analysis on the characteristics of feeder services was implemented to inspire the compatibility-based algorithm design. With the goal of reducing operating cost and passenger inconvenience, the proposed algorithm took several factors critical to the real-word operation into consideration, such as double time window assurance (the time constraints at the beginning and end of passenger travels), the flexibility of feeder plans, and the number of vehicles. Our method was validated on numerical instances of 400, 600, 800 and 1000 passengers. Simulation results show that the compatibility-based algorithm can effectively reduce the number of vehicles with acceptable increase of passengers' inconvenience, and can improve the algorithm efficiency considerably. In addition, the setting of flexible time window of shutter plan can hold some elasticity for feeder services. Sensitivity analysis was conducted to help service providers evaluate the trade-off between the operation cost and level of service. INDEX TERMS Compatibility-based approach, demand responsive connector, routing and scheduling.

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Personalised and Coordinated Demand-Responsive Feeder Transit Service Design: A Genetic Algorithms Approach

Cited by 11 publications

References 33 publications

Optimization for Feeder Bus Route Model Design with Station Transfer

Optimization for Feeder Bus Route Model Design with Station Transfer

An Optimization Model for Demand-Responsive Feeder Transit Services Based on Ride-Sharing Car

Compatibility-Based Approach for Routing and Scheduling the Demand Responsive Connector

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