Estimating Wait Time and Passenger Load in a Saturated Metro Network: A Data-Driven Approach

Qu, Hezhou; Xiaoyue, Xu; Chien, Steven

doi:10.1155/2020/4271871

Cited by 10 publications

(6 citation statements)

References 33 publications

(42 reference statements)

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“…In addition, overcrowding discouraged passengers boarding the train, and this situation may result in fail-to-board, and extra wait/transfer time shall be expected (Qu et al 2020), which in turn leads to additional costs. Therefore, our paper involves the impact of the additional costs caused by standing in a vehicle and failure to board.…”

Section: Literature Reviewmentioning

confidence: 99%

A Mass Transit Equilibrium Problem with Bottleneck Congestion and Capacity Constraints

Shang

Tian

Chu

et al. 2023

Preprint

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Seat capacity setting greatly affects vehicle capacity. Increasing the number of seats allows more passengers to benefit from comfortable travel conditions but reduces the available space for standing passengers and the total capacity of the vehicle. By reducing the number of seats, the operator increases the capacity of the vehicle but most passengers do not find an available seat. Without changing the size of the bus, the operator chooses the desirable seat capacity when purchasing the bus, which can maximize vehicle capacity and allow passengers to have more opportunities to get a seat. Considering capacity constraints, this paper establishes multiple origins and a single destination equilibrium model to study the travel behaviors during peak-period. A bi-level programming model is considered to meet the equilibrium cost. The result shows that we succeed in finding an optimal seat capacity to make the total travel cost be lowest.

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Section: Literature Reviewmentioning

confidence: 99%

A Mass Transit Equilibrium Problem with Bottleneck Congestion and Capacity Constraints

Shang

Tian

Chu

et al. 2023

Preprint

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“…It is noted that transfer passengers stated here represents those passenger transfers on the same line among the different train services, not including passengers transferring from other lines. The demand of transfer passengers from other lines is estimated by a previously developed method ( 33 ).…”

Section: Model Formulationmentioning

confidence: 99%

Optimizing Integrated All-Stop, Express, and Short-Turn Services on a Metro Line with Capacity Constraint Considering Passenger Spatio-Temporal Path Choice

Wang

Liu

2022

Transportation Research Record: Journal of the Transportation R

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Optimization of integrated train service is quite challenging under the situation of express trains overtaking local trains, because passenger path choice becomes more complicated and is largely unknown. To fill this gap, this study proposes a passenger spatio-temporal path choice model to replicate passenger behaviors given the real-time operation information in the metro system. Based on which, a non-linear programming model is developed to optimize integrated train services (i.e., all-stop, express, and short-turn) and the associated train timetables to minimize the total cost for a congested metro line, subject to a set of constraints (i.e., capacity, headway, service pattern, conditions of overtaking, etc.). The genetic algorithm with floating-point number coding is used to perform the optimization. It is applied to a real-world metro line in Chengdu, China, to assess its feasibility and effectiveness. The experimental results indicate that the integrated service is substantially improved in relation to total cost. The sensitivity analysis is conducted to explore the impacts of model parameters on various operation strategies. The proposed modeling approach is beneficial for transit agencies to improve their service planning.

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“…Zhu et al (2018); Ma et al (2019) developed data-driven methods to estimate the number of left-behind passengers in metro systems. Qu et al (2020) also studied the waiting time of left-behind passengers; they found passengers' waiting time in peak hours is much longer than the metro headway. Mo et al (2020) proposed a performance monitoring framework that incorporates the number of left-behind passengers.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In terms of the economic evaluation of public transport services, waiting time is also a critical component in assessing the social benefit/cost of different planning and operation strategies. In the literature, many methods have been developed to estimate the in-station waiting time or transfer time of metro systems using individual-based smart card transactions Sun and Xu, 2012;Sun et al, 2015b;Zhu et al, 2018;Qu et al, 2020). However, despite that out-of-station waiting may cover a substantial proportion of overall travel time and the experience is much more unpleasant than waiting inside the train station (e.g., under bad weather (Zhang et al, 2021)), it has received little attention in the research.…”

Section: Introductionmentioning

confidence: 99%

Quantifying out-of-station waiting time in oversaturated urban metro systems

Zhu¹,

Cheng²,

Wu³

et al. 2021

Preprint

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Metro systems in megacities such as Beijing, Shenzhen and Guangzhou are under great passenger demand pressure. During peak hours, it is common to see oversaturated conditions (i.e., passenger demand exceeds network capacity), which bring significant operational risks and safety issues. A popular control intervention is to restrict the entering rate during peak hours by setting up out-of-station queueing with crowd control barriers. The out-of-station waiting can make up a substantial proportion of total travel time but is not well-studied in the literature. Accurate quantification of out-of-station waiting time is important to evaluating the social benefit and cost of service scheduling/optimization plans; however, out-of-station waiting time is difficult to estimate because it is not a part of smart card transactions. In this study, we propose an innovative method to estimate the out-of-station waiting time by leveraging the information from a small group of transfer passengers-those who transfer from nearby bus routes to the metro station. Based on the estimated transfer time for this small group, we first infer the outof-station waiting time for all passengers by developing a Gaussian Process regression with a Student-t likelihood and then use the estimated out-of-station waiting time to build queueing diagrams. We apply our method to the Tiantongyuan North station of Beijing metro as a case study; our results show that the maximum out-of-station waiting time can reach 15 minutes, and the maximum queue length can be over 3000 passengers. Our results suggest that out-of-station waiting can cause significant travel costs and thus should be considered in analyzing transit performance, mode choice, and social benefits. To the best of our knowledge, this paper is the first quantitative study for out-of-station waiting time.

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Estimating Wait Time and Passenger Load in a Saturated Metro Network: A Data-Driven Approach

Cited by 10 publications

References 33 publications

A Mass Transit Equilibrium Problem with Bottleneck Congestion and Capacity Constraints

A Mass Transit Equilibrium Problem with Bottleneck Congestion and Capacity Constraints

Optimizing Integrated All-Stop, Express, and Short-Turn Services on a Metro Line with Capacity Constraint Considering Passenger Spatio-Temporal Path Choice

Quantifying out-of-station waiting time in oversaturated urban metro systems

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