Crowding in Public Transport: A Review of Objective and Subjective Measures

Li, Zheng; Hensher, David A.

doi:10.5038/2375-0901.16.2.6

Cited by 111 publications

(82 citation statements)

References 20 publications

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“…Finally, an integration of the studies on psychology and choice modelling is a promising venue of further research, in order to comprehend how the psychological aspects that influence the perception of crowding are linked to the perception of time, and to the willingness to pay to reduce the time spent in crowded environments in general, and in crowded public transport stations and vehicles in particular. Li and Hensher (2013) provide a review of this broader literature.…”

Section: Discussionmentioning

confidence: 99%

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

Tirachini

Hensher

Rose

2013

Transportation Research Part A: Policy and Practice

227

153

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The effects of high passenger density at bus stops, at rail stations, inside buses and trains are diverse. . This paper examines the multiple dimensions of passenger crowding related to public transport demand, supply and operations, including effects on operating speed, waiting time, travel time reliability, passengers' wellbeing, valuation of waiting and invehicle time savings, route and bus choice, and optimal levels of frequency, vehicle size and fare. Secondly, crowding externalities are estimated for rail and bus services in Sydney, in order to show the impact of crowding on the estimated value of in-vehicle time savings and demand prediction. Using Multinomial Logit (MNL) and Error Components models, we show that alternative assumptions concerning the threshold load factor that triggers a crowding externality effect do have an influence on the value of travel time (VTTS) for low occupancy levels (all passengers sitting); however, for high occupancy levels, alternative crowding models estimate similar VTTS. Importantly, if demand for a public transport service is estimated without explicit consideration of crowding as a source of disutility for passengers, demand will be overestimated if the service is designed to have a number of standees beyond a threshold, as analytically shown using a MNL choice model. 2

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Section: Discussionmentioning

confidence: 99%

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

Tirachini

Hensher

Rose

2013

Transportation Research Part A: Policy and Practice

227

153

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“…The agency defines acceptable levels of crowding, which can vary widely from agency to agency (Li and Hensher 2013). Our platform allows users to analyze crowding levels using the number of passengers as a percentage of seated capacity.…”

Section: Understanding Transit System Performance Using Avl-apc Data:mentioning

confidence: 99%

Understanding Transit System Performance Using AVL-APC Data: An Analytics Platform with Case Studies for the Pittsburgh Region

Pi¹,

Egge²,

Whitmore³

et al. 2018

JPT

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“…Rd (s−s) in this study is considered from two main aspects: the designed carrying capacity (or the transportation volume) of the metro vehicle, and the metro operation schedule. According to Li and Hensher [38], many United States (US) transit authorities adopted the load factor (passengers/seat) to evaluate the in-vehicle crowdedness of the rail system. In line with this, this study chooses the Sustainability 2017, 9, 2325 4 of 14 load factor to represent the designed carrying capacity of a metro vehicle.…”

Section: Development Of Measurement Model Of Crowdednessmentioning

confidence: 99%

“…According to Li and Hensher [38], many United States (US) transit authorities adopted the load factor (passengers/seat) to evaluate the in-vehicle crowdedness of the rail system. In line with this, this study chooses the load factor to represent the designed carrying capacity of a metro vehicle.…”

Section: Development Of Measurement Model Of Crowdednessmentioning

confidence: 99%

“…However, when the produced value of s s θ − is interpreted to the level of crowdedness, there is a need to establish a threshold of crowdedness level. The study by Li and Hensher [38] provides a typical reference for establishing the thresholds. In their study, six levels of crowdedness for the rail line are identified, as shown in Table 1.…”

Section: Development Of the Thresholds Of Crowdedness Levelmentioning

confidence: 99%

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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: A Review of Objective and Subjective Measures

Abstract: Crowding in public transport is becoming a growing concern as demand grows at a rate that is outstripping available capacity.

Cited by 111 publications

References 20 publications

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

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

Understanding Transit System Performance Using AVL-APC Data: An Analytics Platform with Case Studies for the Pittsburgh Region

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

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