Methodology of mixed load customized bus lines and adjustment based on time windows

Li, Zhijie; Song, Rui; He, Shiwei; Bi, Mingkai

doi:10.1371/journal.pone.0189763

Cited by 15 publications

(9 citation statements)

References 14 publications

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“…Ma et al [26] optimized the CB route considering operation cost and social benefit. Li et al [27] optimized bus routing which minimized the operating cost of CB service providers. Cao and Wang [28] optimized trip assignment that minimized total cost.…”

Section: Customized Bus (Cb) Transitmentioning

confidence: 99%

Optimizing Multi-Terminal Customized Bus Service With Mixed Fleet

Sun

Chien

et al. 2020

IEEE Access

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The customized bus (CB) transit is recognized as an effective transportation mode offering more flexible and demand-responsive service than traditional bus transit with fixed route and schedule, especially during the peak hours. The novelty of this study is the development of a mixed integer non-linear model for optimizing multi-terminal CB service in an urban setting. According to the estimated spatiotemporal passenger demand, the objective total cost, consisting of supplier's and users' costs, is minimized subject to capacity and time window constraints. A mixed bus fleet with various bus sizes is employed to accommodate passenger demand, which increases vehicle utilization and reduces supplier's cost. The inconvenience of passengers caused by early arrival at the destination is treated as penalty and considered in users' cost. The study optimization problem is combinatorial with many decision variables including trip assignment, bus routing and associated timetables, and fleet size. A hybrid genetic algorithm (HGA) which integrates the features of genetic algorithm (GA) and simulated annealing (SA) is developed to effectively search for the optimal solution. A real-world CB network is employed to demonstrate the applicability of the developed model and explore the relation between the model parameters and optimized results. It was found that the total cost can be reduced by 16.5% after employing multiple terminals and a mixed bus fleet.

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Section: Customized Bus (Cb) Transitmentioning

confidence: 99%

Optimizing Multi-Terminal Customized Bus Service With Mixed Fleet

Sun

Chien

et al. 2020

IEEE Access

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“…The design of the newly developed CB system also attracted the attention in recent studies. These studies mainly focused on the basic design of CB system [12], including bus line routing and station location design, bus timetable scheduling, crew and vehicle dispatching, and bus real-time control.…”

Section: Related Workmentioning

confidence: 99%

Mobile Phone Data in Urban Customized Bus: A Network-based Hierarchical Location Selection Method with an Application to System Layout Design in the Urban Agglomeration

Liu

Qiu

et al. 2020

Sustainability

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Employment is one of the essential socioeconomic connections in urban agglomeration. However, both the demand and the supply of transjurisdictional public transport service are unevenly distributed in such area. Customized bus has a high potential of serving transjurisdictional and long-distance commuting demand. This study proposes a network-based layout design method to generate hierarchical service scopes and stations for customized bus system. First, the home and the workplace of residents are identified using mobile phone data, to construct a jobs-housing relationship network. Then, an iterative algorithm based on network community detection and density-based spatial clustering is applied to the jobs-housing relationship network to hierarchically segment the urban agglomeration area into communities. Three methods are proposed for location selection of customized bus stations. A case study is conducted using the mobile phone dataset from nine cities in the Yangtze River Urban Agglomeration in China. A four-level hierarchical customized bus system layout is generated and both the spatial properties and network properties of service scopes are analyzed. The proposed three methods of customized bus station location selection are compared based on the average travel distance and the rationality of the resulted customized bus station location.

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“…Accordingly, the optimal vehicle scheduling scheme was formulated, and the optimal solution of the model was obtained by combining a greedy algorithm with a genetic algorithm. Li [3] established a mixed-load custom bus routing model that satisfies the requirements of the time window and used the Cplex software to solve the model. Carol Tong [4] established an optimization model that satisfies the time window and passenger needs, to optimize vehicle paths and allocate vehicles.…”

Section: Introductionmentioning

confidence: 99%

Research on Optimization of Customized Bus Routes Based on Uncertainty Theory

Sang

Zhang

Xue

et al. 2021

Journal of Advanced Transportation

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In the optimization process of the routes of customized buses, there are numerous uncertainties in the route planning and setting. In this study, the uncertainty theory is introduced into the optimization problem of a customized bus route, and an uncertain customized bus route optimization model is established, which aims at the minimizing the total mileage of vehicle operation. An improved genetic algorithm is used to solve the model, whose feasibility is verified by a case study. The results show that the optimization model based on the uncertainty theory can yield a reasonable customized bus route optimization scheme, and the total mileage reduced from 35.6 kilometers to 32.2 kilometers. This research provides the theoretical support for the optimization of customized bus routes.

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Methodology of mixed load customized bus lines and adjustment based on time windows

Cited by 15 publications

References 14 publications

Optimizing Multi-Terminal Customized Bus Service With Mixed Fleet

Optimizing Multi-Terminal Customized Bus Service With Mixed Fleet

Mobile Phone Data in Urban Customized Bus: A Network-based Hierarchical Location Selection Method with an Application to System Layout Design in the Urban Agglomeration

Research on Optimization of Customized Bus Routes Based on Uncertainty Theory

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