Crowding in public transport systems: Effects on users, operation and implications for the estimation of demand

Tirachini, Alejandro; Hensher, David A.; Rose, John M.

doi:10.1016/j.tra.2013.06.005

Cited by 217 publications

(170 citation statements)

References 55 publications

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“…These data are mainly used in transportation planning and management [74], [52], [51], crowd density and crowd event estimation [51], traveler trajectory and mobility pattern esti- [2], [75], point of interest identification [96], etc. Also, automatic vehicle location data can be used for transportation planning and management [97], [98].…”

Section: B Crowd Data-sources Generation and Applicationsmentioning

confidence: 99%

“…This approach identified the individual's mobility pattern and estimated the most visited destinations [75]. Tirachini et al examined different aspects of passenger crowding in transport management and operation [52]. They considered waiting and in-vehicle time saving, route and bus choice, travel time reliability, passengers' wellbeing, vehicle size, frequency, and fare.…”

Section: Transport Datamentioning

confidence: 99%

“…The main focus of these articles were the methods that describe detection of crowd dynamics provided by crowd data. These dynamics generally consist of crowd count [6], [7], [11], [13], [49], [50], [17], [51], [52], crowd trajectories/mobility [32], [53], [9], [14], [54], crowd sentiment [21], and crowd behavior [18], [20], [55], [56], [57], [58], [51], [59], [60]. These articles clearly showed an increasing trend in the crowd event detection research for UA (see Figure 1C).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Advances in Crowd Analysis for Urban Applications Through Urban Event Detection

Kaiser

Lwin

Mahmud

et al. 2018

IEEE Trans. Intell. Transport. Syst.

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Abstract-The recent expansion of pervasive computing technology has contributed with novel means to pursue human activities in urban space. The urban dynamics unveiled by these means generate an enormous amount of data. These data are mainly endowed by portable and radio-frequency devices, transportation systems, video surveillance, satellites, unmanned aerial vehicles, and social networking services. This has opened a new avenue of opportunities, to understand and predict urban dynamics in detail, and plan various real-time services and applications in response to that. Over the last decade, certain aspects of the crowd, e.g. mobility, sentimental, size estimation and behavioral, have been analyzed in detail and the outcomes have been reported. This article mainly conducted an extensive survey on various data sources used for different urban applications, the state-of-the-art on urban data generation techniques and associated processing methods in order to demonstrate their merits and capabilities. Then, a possible crowd event detection framework is discussed which fuses data from all the available pervasive technology sources. In addition, available open-access crowd datasets for urban event detection are provided along with relevant Application Programming Interfaces, and finally, some open challenges and promising research directions are outlined.Index Terms-Urban sensing, pervasive technology, crowd mobility and management, information fusion, decision support system, benchmark datasets.

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Section: B Crowd Data-sources Generation and Applicationsmentioning

confidence: 99%

Section: Transport Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Advances in Crowd Analysis for Urban Applications Through Urban Event Detection

Kaiser

Lwin

Mahmud

et al. 2018

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

show abstract

“…On the other hand, methods based on a micro-simulation approach analyse pedestrian behaviour on platforms explicitly, especially in crowded conditions [11,12], and relate it to delays and to other aspects of rail service performance.…”

Section: Introductionmentioning

confidence: 99%

Methodology for Determining Dwell Times Consistent with Passenger Flows in the Case of Metro Services

et al. 2017

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The importance of a mobility system based on railway technology as the backbone of public transport is now widely acknowledged. Indeed, rail systems are green, high performing, smart and able to ensure a high degree of safety. Therefore, modal split should be steered towards rail transport by increasing the attractiveness of this transport mode. In this context, a key element is represented by the timetabling design phase, which must aim to guarantee an appropriate degree of robustness of rail operations in order to ensure a high degree of system reliability and increase service quality. A crucial factor in the task of timetabling entails evaluating dwell times at stations. The innovative feature of this paper is the analytical definition of dwell times as flow dependent. Our proposal is based on estimating dwell times according to the crowding level at platforms and related interaction between passengers and the rail service in terms of user behaviour when a train arrives. An application in the case of a real metro system is provided in order to show the feasibility of the proposed approach.

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“…Jiang et al [11] pointed out that the crowded metro system will reduce the comfort of passengers and increase the risks of safety and fire. The study by Tirachini et al [12] shows that passengers in crowded public transport systems such as metro systems will suffer from travelling. Cox et al [13] found that crowdedness in a metro system presents a threat both to the development of the rail industry and the health of passengers.…”

Section: Introductionmentioning

confidence: 99%

Measuring Crowdedness between Adjacent Stations in an Urban Metro System: a Chinese Case Study

et al. 2017

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Abstract:The urban metro system has been widely appreciated as the most important component in urban infrastructures. It plays a critical role in promoting urban social and economic development, and particularly in reducing the urban traffic congestion. However, there are various inherent problems with operating metro systems, which typically involve the crowdedness both at stations and inside vehicles. Both policymakers and academic researchers in China have paid little attention to the crowdedness between metro stations. In order to solve the problem of crowdedness, it is necessary to develop a method to evaluate the level of crowdedness. This work establishes a model to measure the crowdedness between adjacent stations in a metro system based on the load factor principle, passenger standing density, and other factors such as the metro operation schedule and estimations of passenger flows. The Chongqing Metro Line 3 in China is used as a case study to demonstrate the application of the evaluation model. The case study reveals that the model introduced in this study can assist with assessing the crowdedness level between adjacent stations in a metro line. The model is an effective tool for helping the metro management and administration understand the level of crowdedness, apply proper methods to mitigate the crowdedness, and thus improve the quality of the service for those utilizing the metro system.

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Crowding in public transport systems: Effects on users, operation and implications for the estimation of demand

Cited by 217 publications

References 55 publications

Advances in Crowd Analysis for Urban Applications Through Urban Event Detection

Advances in Crowd Analysis for Urban Applications Through Urban Event Detection

Methodology for Determining Dwell Times Consistent with Passenger Flows in the Case of Metro Services

Measuring Crowdedness between Adjacent Stations in an Urban Metro System: a Chinese Case Study

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