Clustering Passenger Trip Data for the Potential Passenger Investigation and Line Design of Customized Commuter Bus

Qiu, Guo Yu; Song, Rui; He, Shiwei; Xu, Wangtu; Jiang, Min

doi:10.1109/tits.2018.2875466

Cited by 49 publications

(25 citation statements)

References 26 publications

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“…The differences between the minimized costs denoted as Z G and Z H with GA and HGA, respectively, against that with LINGO denoted as Z L , are represented by "Δ GA " and "Δ HGA ", formulated as Eqs. (24) and (25). Thus,…”

Section: B Results Analysismentioning

confidence: 94%

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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: B Results Analysismentioning

confidence: 94%

“…With a density-based clustering algorithm, Qiu et al [25] estimated the origin and destination demand for a CB network. Ma et al [26] optimized the CB route considering operation cost and social benefit.…”

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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show abstract

“…Based on a summary of the challenges faced by the bus ridesharing problem, in Table 4, we present the core of the modelling and solution approaches of the bus ridesharing models; the details are explained below. However, in practical use, the state-of-the-art model in [6]- [12] has several shortcomings, which are as follows.…”

Section: A Motivationmentioning

confidence: 99%

“…(i) It is difficult to gather enough passengers from one location to another in a short time. Therefore, if the origin/destination of a number of ride requests is far from the ride request pickup/delivery point, the request will be pruned by the minimum loadable capacity of the shared vehicle, as applied in [7], [10], [12]. This action pushes down the ridesharing success rate.…”

Section: A Motivationmentioning

confidence: 99%

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

Liu

2020

IEEE Access

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“…Some researchers used this algorithm to evaluate, identify, or optimize collective transportation systems, such as bus or train lines [21][22][23]. Qiu et al proposed a spatial clustering−based algorithm (P−DN), based on OD demand, which makes it possible to obtain the desired cluster lines so that the main bus corridors can be identified [24]. The key elements for bus-corridor-location identification are the urban key nodes and their links [15].…”

Section: Literature Reviewmentioning

confidence: 99%

Identification Method for Optimal Urban Bus Corridor Location

Yin

Zhang

2020

Sustainability

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Locating urban bus corridors based on corridor characteristics can increase the transportation capacity, improve transportation efficiency, and increase the attractiveness and commercial value of urban bus corridors. In this paper, we describe the comprehensive optimization of the urban bus corridor location and setting of bus lanes, while considering the aggregation effect of the corridor. First, we use a K-shortest path algorithm to generate a candidate set of bus corridors. Then, we analyze the influencing factors of the bus corridor. Following this, we take the minimum generalized cost and the maximum aggregation utility along the path as the objective function and design a bus corridor location identification optimization model, considering arc capacity, plot ratio, corridor development, and time constraints. Finally, we examine the real-world example of the Beijing city and identify the location of the bus corridors in the morning and evening peak hours. The one-way traffic of most of the roads identified as bus corridors was found to be greater than 6671 people/h. Thus, the location of the bus corridor and setting of bus lanes in the corridor are closely related to passenger flow, and the method can provide scientific guidance for transportation and urban planning departments and facilitate collaboration between these departments.

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Clustering Passenger Trip Data for the Potential Passenger Investigation and Line Design of Customized Commuter Bus

Cited by 49 publications

References 26 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

Identification Method for Optimal Urban Bus Corridor Location

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