Train boarding and alighting rates at high passenger loads

Harris, Nigel G

doi:10.1002/atr.5670400302

Cited by 60 publications

(73 citation statements)

References 2 publications

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“…With the mock-up car, the boarding and alighting time of the participants who play the role of passengers can be measured [5,6,16]. With this experimental simulation, the relations between the time and the influencing factors can be found.…”

Section: Methods Of Data Acquisitionmentioning

confidence: 99%

“…Even though these benchmarking groups contain data from various continents, they do not have the Seoul metro data. Although Kim et al [17] found coefficients for the fractional model [16] suitable to the measured data in Korea, it is necessary to find more suitable model to the boarding and alighting time in Seoul.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The Weston model is based on the fact that the boarding or alighting time per a person is decreased because the passengers can board or alight successively in a line if the number of boarding and alighting passengers is sufficiently large. Harris [16] disclosed that the coefficient of crossing passengers in the Weston model was designated excessively and proposed a revised fractional square model after analysing data acquired from the South West Train, United Kingdom, and proposed the new fractional square model. However, it was shown that the Weston model can be applied internationally if some of the coefficients are properly adapted [7].…”

Section: Literature Reviewmentioning

confidence: 99%

“…All the evaluated models show relatively high greater than 0.9. The fractional square model [16] presents the highest , but exponential model [4] presents the lowest. This is obvious because exponential model cannot be applied to the case that the numbers of boarding or alighting passengers exceed 25.…”

Section: Fidelity Investigation Of the Previous Modelsmentioning

confidence: 99%

See 3 more Smart Citations

Development of a boarding and alighting time model for the urban rail transit in a megacity

Kim¹,

Kim²,

Hong³

et al. 2015

WIT Transactions on the Built Environment

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Dwell time is the time a loaded rail transit spends in a station, which consists of operation time, standby time, and passenger flow time. Among these, passenger flow time, the time required by passengers to board and alight (i.e., boarding and alighting time), is the key factor that determines the dwell time. Wide fluctuations in boarding and alighting time are found in crowded urban areas, especially in a megacity such as Seoul. Accordingly, it is very important to accurately assess the boarding and alighting time for the efficient scheduling of an urban rail transit. Numerous studies have been conducted to develop the prediction model in many countries. However, most of the suggested models were developed based on the light rail transit, so it may not be appropriate to apply those models to the heavy rail transit that is mainly running in Seoul. Furthermore, the passenger behaviour, which is one of the key influencing factors, varies depending upon behavioural devices such as bleepers, indicating that doors are closing or platform screen door, etc. Hence, the existing models developed under different circumstances cannot be applied blindly to the assessment, and the limitation of those models should be evaluated. In this study, existing models are evaluated by the regression analysis of data measured in Seoul metro green-line. Then, a novel boarding and alighting time model is proposed. The superior performance of the proposed model is demonstrated by comparing the adjusted coefficient of determination. This model can be employed to schedule train operation as well as to assess time consumption if the boarding and alighting passengers are given.

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Section: Methods Of Data Acquisitionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Fidelity Investigation Of the Previous Modelsmentioning

confidence: 99%

See 2 more Smart Citations

Development of a boarding and alighting time model for the urban rail transit in a megacity

Kim¹,

Kim²,

Hong³

et al. 2015

WIT Transactions on the Built Environment

View full text Add to dashboard Cite

show abstract

“…Rolling stock plays a role in the problem of dwell time estimation due to many factors: the control system, number and width of doors [24], kind of service performed [25], horizontal and vertical gaps between the train and platform [26,27] and interior layout of the convoy seen as number and position of seats [28] or as passenger distance to exit doors and potential free space that the passenger is inclined to occupy [29]. As shown by [30], also fare collection methods may influence dwell times at stations.…”

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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Estimation of train dwell time at short stops based on track occupation event data: A study at a Dutch railway station

Daamen

Goverde

2016

J of Advced Transportation

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SUMMARYTrain dwell time is one of the most unpredictable components of railway operations, mainly because of the varying volumes of alighting and boarding passengers. However, for reliable estimations of train running times and route conflicts on main lines, it is necessary to obtain accurate estimations of dwell times at the intermediate stops on the main line, the so-called short stops. This is a great challenge for a more reliable, efficient and robust train operation. Previous research has shown that the dwell time is highly dependent on the number of boarding and alighting passengers. However, these numbers are usually not available in real time. This paper discusses the possibility of a dwell time estimation model at short stops without passenger demand information by means of a statistical analysis of track occupation data from the Netherlands. The analysis showed that the dwell times are best estimated for peak and off-peak hours separately. The peakhour dwell times are estimated using a linear regression model of train length, dwell times at previous stops and dwell times of the preceding trains. The off-peak-hour dwell times are estimated using a non-parametric regression model, in particular, the k-nearest neighbor model. There are two major advantages of the proposed estimation models. First, the models do not need passenger flow data, which is usually impossible to obtain in real time in practice. Second, detailed parameters of rolling stock configuration and platform layout are not required, which makes the model more generic and eases implementation. A case study at Dutch railway stations shows that the estimation accuracy is 85.8%-88.5% during peak hours and 80.1% during off-peak hours, which is relatively high. We conclude that the estimation of dwell times at short stop stations without passenger data is possible.

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Train boarding and alighting rates at high passenger loads

Cited by 60 publications

References 2 publications

Development of a boarding and alighting time model for the urban rail transit in a megacity

Development of a boarding and alighting time model for the urban rail transit in a megacity

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

Estimation of train dwell time at short stops based on track occupation event data: A study at a Dutch railway station

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