Customized bus routing problem with time window restrictions: model and case study

Guo, Rongge; Guan, Wei; Zhang, Wenyi; Meng, Fanchao; Zhang, Zixian

doi:10.1080/23249935.2019.1644566

Cited by 60 publications

(39 citation statements)

References 38 publications

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“…Ma et al [29] optimized locations of stops, routes and timetables that minimized total cost including operation, environmental and congestion cost. Guo et al [30] optimized stop locations, bus routes, and trip assignment that minimized total cost, and then extended that work by considering service time window [31]. Huang et al [34] designed a two-phase model to optimize trip assignment, routes, timetables, which maximized total profit.…”

Section: Customized Bus (Cb) Transitmentioning

confidence: 99%

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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%

“…is a combinatorial problem. The solution is difficult to be optimized using the exact algorithm, especial for large-scale networks [31]. In order to efficiently search for the solution, a hybrid genetic algorithm (HGA) that integrated the features of GA and SA is proposed.…”

Section: Solution Algorithmmentioning

confidence: 99%

Optimizing Multi-Terminal Customized Bus Service With Mixed Fleet

Sun

Chien

et al. 2020

IEEE Access

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“…(ii) The theoretic models reside in L 1 -norm space, L 2norm space and L p -norm space and are difficult to generalize in a practical setting by exploiting a real city road network. Euclidean distance and a density threshold are applied in [6], [8], [9], [11], [12], as the dissimilarity measure of travel demands is inaccurate in a practical setting. For example, in many existing studies, the OD pair is regarded as an object to be clustered in many existing studies.…”

Section: A Motivationmentioning

confidence: 99%

“…In addition, it was determined that the discovered hot locations of potential ride requests could be regarded as the candidate locations of CB stops and could be used to set candidate CB lines. [11] examined the CB routing problem with time-window restrictions and developed a mixed integer programming model to determine routes and passenger-to-vehicle assignment simultaneously, combined with passengers' time-window satisfaction. [22] aimed realtime CB route optimization and divided the process into the two stages: the first stage dealt with static ride requests to obtain the initial CB route optimization scheme, and the second stage dealt with dynamic ride requests to update the CB route in real-time.…”

Section: Related Workmentioning

confidence: 99%

Optimization Approach to Improve the Ridesharing Success Rate in the Bus Ridesharing Service

Liu

2020

IEEE Access

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“…Focusing on the customized bus service network design, [32] jointly optimized the passengerto-vehicle assignment problem and vehicle routing problem, and the passenger convenience was considered in constraints instead of the objective function in their model. Presented with full spatial-temporal constraints, [33] optimized vehicle routes and passenger assignment procedure to cut down the operation cost. Yu et al tailored the DRC service for people traveling from a fixed rail station to their final work destinations, and a bi-level nonlinear mixed integer programming model is constructed to tackle the feeder bus network design problem (FBNDP) [34].…”

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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Customized bus routing problem with time window restrictions: model and case study

Cited by 60 publications

References 38 publications

Optimizing Multi-Terminal Customized Bus Service With Mixed Fleet

Optimizing Multi-Terminal Customized Bus Service With Mixed Fleet

Optimization Approach to Improve the Ridesharing Success Rate in the Bus Ridesharing Service

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

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