Capacity-oriented passenger flow control under uncertain demand: Algorithm development and real-world case study

Xu, Xinyue; Liu, Jun; Li, Haiying; Jiang, Man

doi:10.1016/j.tre.2016.01.004

Cited by 112 publications

(39 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In addition, the identification result would be helpful to control the key station for improving the performance. Especially, which and how many passengers of stations should be determined during the morning peak hours, which is the hot topic of these days in the field of urban rail transit [3], [19][20].…”

Section: Resultsmentioning

confidence: 99%

A Key Station Identification Method for Urban Rail Transit: A Case Study of Beijing Subway

Sun

2017

PROMET

View full text Add to dashboard Cite

INTRODUCTIONWith the rapid development of urban rail transit network operation technique, the increase of passenger volume leads to congestion in stations [1], causing even safety risk in the network [2]. Besides, the topology of urban rail transit network can also aggravate or relief the congestion situation of subway networks. Therefore, an effective method identified the key station is an imperative to manage the overcrowded urban rail transit.For the factors of the station performance, many of the current approaches focus on the relationship between passenger flow and facility capacity. Xu et al.[3] combined queuing theory with facility utilization evaluation to conduct the station capacity evaluation, according to the gathering and scattering process of a station. Kepaptsoglou and Karlaftis [4] proposed a model to evaluate passenger evolution on subway station platforms, which usually gathered most passengers. Wang et al.[5] also used the passenger evacuation capacity to evaluate the station based on the facility utilization. If the service level of the facility is too low, there are big problems in the station.Besides, passengers' subjective feelings and other factors are also used for evaluation. Matulin et al.[6] proposed a two-level evaluation method to analyze public transport performances from micro and macro levels, using performance indicators and quality of service indicators. Kesten and Öğüt [7] developed a measurement framework of passenger-oriented performance to evaluate rail transportation, using satisfaction and importance ratings obtained from passenger surveys.Meanwhile, there are many approaches employed in the station evaluation. Some typical methods such as Data Envelopment Analysis (DEA) have played their great role in the station assessment. Sancha et al. [8] used the method to estimate the technical, social and environmental efficiency of transit transfer stations. Pjevčević et al. [9] used DEA method to measure and analyze the efficiencies of ports on the Danube River, which can be borrowed for station evaluation. In addition, simulation is also another method, where relevant simulation or operation data are provided. Zhang et al. [10] proposed a cellular automata-based model to simulate passengers' alighting and boarding behaviours in metro stations, aiming to analyze and evaluate the operation performance of stations. However, most works above do not consider the influence of other stations in the subway system, which results in local rather than global conclusions.Fortunately, in recent years, a complex network theory has been introduced to the station evaluation from a global viewpoint. Indexes such as degree centrality, betweenness centrality, and closeness centrality have an outstanding ability to identify key nodes from the network [11][12][13][14]. Sheikhahmadi et al. [12] proposed an improved high degree centrality index to identify the Traffic Management Original Scientific Paper

show abstract

Section: Resultsmentioning

confidence: 99%

A Key Station Identification Method for Urban Rail Transit: A Case Study of Beijing Subway

Sun

2017

PROMET

View full text Add to dashboard Cite

show abstract

“…This allows for readjusting optimization objectives and quantifying the disutility of factors such as transfers and waiting times. Related studies have so far referred to urban railway networks, due to the availability of both entry and exit point AFC transactions in them and involved route choice modeling [48][49][50] and the identification of flows in network transfer points [51][52][53][54].…”

Section: Origin-destination and Transfer Inferencementioning

confidence: 99%

“…Collectively, researchers have agreed upon increased computational costs associated with (a) processing ITS data and (b) specifying optimization models across all planning stages [29,42,43,78,94]. Optimization formulations accounting for variability in input data, either through statistics or simulation-based evaluation of objectives reasonably entail the execution of additional processes [31,51,54,55,85,91]. Especially agent-based simulation models require significant efforts for calibration and validation [18,20].…”

Section: Computational Effortmentioning

confidence: 99%

Combining ITS and optimization in public transportation planning: state of the art and future research paths

Iliopoulou

Κεπαπτσόγλου

2019

Eur. Transp. Res. Rev.

View full text Add to dashboard Cite

Intelligent Transportation Systems (ITS) applications in public transportation have allowed for automated data collection, which is particularly useful for planning and operations. While technological advancement of ITS has so far been extensive, their usage for developing relevant planning and operational tools is rather limited. Research on planning and operations of public transportation systems has not widely investigated the potential of combining optimization models with data originating from ITS. Such applications, which could benefit from such an approach include route planning, scheduling and resource allocation in real time. In this context, this paper investigates and critically discusses potential models and methodologies in public transport planning and operations, which can benefit from ITS data, highlights their potential and identifies possible research paths on that area. The overview of literature collectively points to a series of common challenges faced by transportation professionals and underlines the need for better decision support tools for ITS data.

show abstract

“…Thus, the hybrid PSO has been proposed to solve the train timetable rescheduling problem [30,31]. Moreover, genetic algorithm (GA) adopts the mutation operation with certain probability to avoid the local optima and is widely employed to solve real-world problems [32,33]. The GA-PSO algorithm is also applied to solve nonlinear constrained optimization problems [34,35].…”

Section: Introductionmentioning

confidence: 99%

Genetic Algorithm-Based Particle Swarm Optimization Approach to Reschedule High-Speed Railway Timetables: A Case Study in China

Wang

et al. 2019

Journal of Advanced Transportation

View full text Add to dashboard Cite

In this study, a mixed integer programming model is proposed to address timetable rescheduling problem under primary delays. The model considers timetable rescheduling strategies such as retiming, reordering, and adjusting stop pattern. A genetic algorithm-based particle swarm optimization algorithm is developed where position vector and genetic evolution operators are reconstructed based on departure and arrival time of each train at stations. Finally, a numerical experiment of Beijing-Shanghai high-speed railway corridor is implemented to test the proposed model and algorithm. The results show that the objective value of proposed method is decreased by 15.6%, 48.8%, and 25.7% compared with the first-come-first-service strategy, the first-schedule-first-service strategy, and the particle swarm optimization, respectively. The gap between the best solution obtained by the proposed method and the optimum solution computed by CPLEX solver is around 19.6%. All delay cases are addressed within acceptable time (within 1.5 min). Moreover, the case study gives insight into the correlation between delay propagation and headway. The primary delays occur in high-density period (scheduled headway closes to the minimum headway), which results in a great delay propagation.

show abstract

Capacity-oriented passenger flow control under uncertain demand: Algorithm development and real-world case study

Cited by 112 publications

References 46 publications

A Key Station Identification Method for Urban Rail Transit: A Case Study of Beijing Subway

A Key Station Identification Method for Urban Rail Transit: A Case Study of Beijing Subway

Combining ITS and optimization in public transportation planning: state of the art and future research paths

Genetic Algorithm-Based Particle Swarm Optimization Approach to Reschedule High-Speed Railway Timetables: A Case Study in China

Contact Info

Product

Resources

About