Circle Line Optimization of Shuttle Bus in Central Business District without Transit Hub

Yao, Baozhen; Cao, Qingda; Jin, Lu; Zhang, Mingheng; Zhao, Yibing

doi:10.7307/ptt.v29i1.2015

Cited by 9 publications

(9 citation statements)

References 34 publications

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“…In the traffic jams, travelers would like to choose public transit which has been proved effective in many literatures. 3,4 These principles are also verified in the early study, such as maximum demand cover and adjacent highquality public transit service. 5 Aros-Vera studied P&R facility's location problems using logit model, where travelers could either select transfer (P&R) facilities or choose a car travel.…”

Section: Literature Reviewmentioning

confidence: 83%

Optimization of park-and-ride system: A case study of Shunyi in Beijing

Shen

Chen

et al. 2017

Advances in Mechanical Engineering

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Nowadays, rapid increase in automobile ownership results in traffic congestions especially in large cities all over the world. Park-and-ride mode could ease traffic congestion in urban areas without abandoning traffic demands. The optimization of park-and-ride facility location and charging rules are of importance for travelers, governments, and environment. Therefore, a multi-objective model considering investments and pollution is proposed and a bi-level genetic algorithm is designed. The case study of Shunyi in Beijing is conducted and the results show that the optimization model is feasible, which could provide suggestion for charging policies and park-and-ride locations.

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Section: Literature Reviewmentioning

confidence: 83%

Optimization of park-and-ride system: A case study of Shunyi in Beijing

Shen

Chen

et al. 2017

Advances in Mechanical Engineering

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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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“…The bi-level programming model can be solved by heuristic algorithms, [39][40][41][42][43] the k-nearest neighbors algorithm, 44 the artificial neural network algorithm 45 and so on. In this paper, the SCE-UA is introduced to solve the proposed bilevel programming model.…”

Section: Shuffled Complex Evolution Algorithm For the Distribution Cementioning

confidence: 99%

Optimization of a regional distribution center location for parts of end-of-life vehicles

et al. 2017

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The high transportation cost of the parts of end-of-life vehicles (ELVs) has received a great deal of attention lately, and different approaches for solving the problem have been taken. In this paper, a bi-level programming model is developed to decompose the distribution center location problem for the parts of ELVs in order to obtain a trade-off between the location cost of the distribution center and the transportation cost. The upper level determines the sites of distribution centers and the lower level represents the delivery routes under any location pattern of candidate sites of distribution centers. The proposed model is illustrated by real data and the results demonstrate that it can be an effective method to determinate a distribution center for ELVs.

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Circle Line Optimization of Shuttle Bus in Central Business District without Transit Hub

Abstract: ABSTRACT

Cited by 9 publications

References 34 publications

Optimization of park-and-ride system: A case study of Shunyi in Beijing

Optimization of park-and-ride system: A case study of Shunyi in Beijing

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

Optimization of a regional distribution center location for parts of end-of-life vehicles

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