A multi-objective meta-heuristic approach for transit network design and frequency setting problem in a bus transit system

Jha, Shashi Bhushan; Jha, J.K.; Tiwari, Manoj Kumar

doi:10.1016/j.cie.2019.02.025

Cited by 73 publications

(34 citation statements)

References 53 publications

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“…Random connection method of adjacent nodes. In this method, the routes are generated by the connection between adjacent nodes, such as the probability-based IRSG procedure proposed by Jha et al [26]. e method usually has high calculation e ciency, but cannot guarantee that the travel time between OD pairs will not be too long along the routes.…”

Section: Generation Of Initial Populationsmentioning

confidence: 99%

“…e term (25) indicates that the passenger ow corresponding to the OD pair only has the in ow at the starting point and the out ow at the ending point , and the in ow is equal to the out ow at the intermediate stations of the path . e term (26) indicates that the passenger ow entering the line is equal to that exiting the line . e term (27) indicates that re ow is not allowed in the path.…”

Section: Generation Of Initial Populationsmentioning

confidence: 99%

“…If the lines are selected from a given line pool to form the network, the rationality of these lines will largely depend on the quality of the line pool [20]. At present, the metaheuristic methods, such as genetic algorithm [8,21,22], simulated annealing algorithm [13], tabu search algorithm [23], swarm intelligence algorithm [24] and other metaheuristics [25,26] are usually applied in the transit network design problem.…”

Section: Introductionmentioning

confidence: 99%

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An Improved NSGA-II Algorithm for Transit Network Design and Frequency Setting Problem

Chai

Qing-huai

2020

Journal of Advanced Transportation

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The transit network design and frequency setting problem is related to the generation of transit routes with corresponding frequency schedule. Considering not only the influence of transfers but also the delay caused by congestion on passengers’ travel time, a multi-objective transit network design model is developed. The model aims to minimize the travel time of passengers and minimize the number of vehicles used in the network. To solve the model belongs to a NP-Hard problem and is intractable due to the high complexity and strict constraints. In order to obtain the better network schemes, a multi-population genetic algorithm is proposed based on NSGA-II framework. With the algorithm, network generation, mode choice, demand assignment, and frequency setting are all integrated to be solved. The effectiveness of the algorithm which includes the high global convergence and the applicability for the problem is verified by comparison with previous works and calculation of a real-size case. The model and algorithm can be used to provide candidates for the sustainable policy formulation of urban transit network scheme.

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Section: Generation Of Initial Populationsmentioning

confidence: 99%

Section: Generation Of Initial Populationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Improved NSGA-II Algorithm for Transit Network Design and Frequency Setting Problem

Chai

Qing-huai

2020

Journal of Advanced Transportation

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“…Multi-objective evolutionary algorithms optimize multiple objectives simultaneously and they are widely used in the transportation field. Multi-objective evolutionary algorithms include several such as Non-dominated Sorting Genetic Algorithm (NSGA-II) [24], Multi-Objective Simulated Annealing (MOSA) [25], Multi-Objective Tabu Search Algorithm (MOTS) [26], and Multi-Objective Particle Swarm Optimization (MOPSO) [27]. However, NSGA-II performs better in terms of finding a diverse set of solutions and in converging to near the true pareto-optimal set compared with others.…”

Section: Solution Algorithmmentioning

confidence: 99%

Optimization of Bus Bridging Service under Unexpected Metro Disruptions with Dynamic Passenger Flows

Wang

Yuan

Yin

2019

Journal of Advanced Transportation

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A metro disruption is a situation where metro service is suspended for some time due to unexpected events such as equipment failure and extreme weather. Metro disruptions reduce the level of service of metro systems and leave numerous passengers stranded at disrupted stations. As a means of disruption management, bus bridging has been widely used to evacuate stranded passengers. This paper focuses on the bus bridging problem under operational disruptions on a single metro line. Unlike previous studies, we consider dynamic passenger flows during the disruption. A multi-objective optimization model is established with objectives to minimize total waiting time, the number of stranded passengers and dispatched vehicles with constraints such as fleet size and vehicle capacity. The NSGA-II algorithm is used for the solution. Finally, we apply the proposed model to Shanghai Metro to access the effectiveness of our approaches in comparison with the current bridging strategy. Sensitivity analysis of the bus fleet size involved in the bus bridging problem was conducted.

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“…e proposed approach is applied to a real-life BRTS of the city of Pereira, Columbia. Most recently, Jha et al [25] studied the transit network design and frequency-setting problem for public buses. e authors used a multiobjective PSO with a number of search strategies to tackle the problem.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Differential Evolution‐Particle Swarm Optimization Algorithm for Multiobjective Urban Transit Network Design Problem with Homogeneous Buses

Buba

Lee

2019

Mathematical Problems in Engineering

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This paper considers an urban transit network design problem (UTNDP) that deals with construction of an efficient set of transit routes and associated service frequencies on an existing road network. The UTNDP is an NP-hard problem, characterized by a huge search space, multiobjective nature, and multiple constraints in which the evaluation of candidate route sets can be both time consuming and challenging. This paper proposes a hybrid differential evolution with particle swarm optimization (DE-PSO) algorithm to solve the UTNDP, aiming to simultaneously optimize route configuration and service frequency with specific objectives in minimizing both the passengers’ and operators’ costs. Computational experiments are conducted based on the well-known benchmark data of Mandl’s Swiss network and a large dataset of the public transport system of Rivera City, Northern Uruguay. The computational results of the proposed hybrid algorithm improve over the benchmark obtained in most of the previous studies. From the perspective of multiobjective optimization, the proposed hybrid algorithm is able to produce a diverse set of nondominated solutions, given the passengers’ and operators’ costs are conflicting objectives.

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A multi-objective meta-heuristic approach for transit network design and frequency setting problem in a bus transit system

Cited by 73 publications

References 53 publications

An Improved NSGA-II Algorithm for Transit Network Design and Frequency Setting Problem

An Improved NSGA-II Algorithm for Transit Network Design and Frequency Setting Problem

Optimization of Bus Bridging Service under Unexpected Metro Disruptions with Dynamic Passenger Flows

Hybrid Differential Evolution‐Particle Swarm Optimization Algorithm for Multiobjective Urban Transit Network Design Problem with Homogeneous Buses

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