Planning guidelines for metro–bus interchanges by means of a pedestrian microsimulation model

Seriani, Sebastián; Fernández, Rodrigo

doi:10.1080/03081060.2015.1039235

Cited by 27 publications

(23 citation statements)

References 3 publications

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“…The method used in this research was based on real-scale laboratory experiments at PAMELA. The main variables used in these methods were selected according to the classification proposed by [1]. The authors used three types of variables: physical (i.e., vertical and horizontal gap, width of doors, and width and length of platforms), spatial (i.e., number of seats and setback), and operational (i.e., density of passengers, BAT, and time for each passenger to board and alight).…”

Section: Geometrical Layout and Variablesmentioning

confidence: 99%

“…The platform train interface (PTI) is a very complex space where most interactions occur between passengers boarding and alighting [1]. The way (e.g., movement) that passengers go from the platform to the train (boarding) or from the train to the platform (alighting) is a very important issue that affects the efficiency and safety of metro systems.…”

Section: Introductionmentioning

confidence: 99%

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Exploring the Effect of Boarding and Alighting Ratio on Passengers’ Behaviour at Metro Stations by Laboratory Experiments

Seriani

Fernández

Luangboriboon

et al. 2019

Journal of Advanced Transportation

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The objective of this work was to study the effect of the ratio between passengers boarding and alighting on the passengers’ behaviour at metro stations. A mock-up of a vehicle and the relevant portion of the platform was built to run a series of simulation experiments at University College London’s Pedestrian Accessibility and Movement Environment Laboratory (PAMELA). Different scenarios were tested based on the next generation London Underground trains. The scenarios were classified according to different load conditions. Four types of behaviour are described. In most cases boarding is first, and passengers compete for space to enter the train. In the case of alighting, first passengers are faster than the rest of alighters due to the space available on the platform as boarding passengers give way to those who are getting off the train. In addition, alighters form lanes of flow depending on the number of passengers waiting to board the train on the platform. With respect to the train, if the density inside the train is higher than 4 passengers per square metre, then the flow at the doors starts to decrease. More experiments are needed to study the relationship between platform density and boarding and alighting time.

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Section: Geometrical Layout and Variablesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Exploring the Effect of Boarding and Alighting Ratio on Passengers’ Behaviour at Metro Stations by Laboratory Experiments

Seriani

Fernández

Luangboriboon

et al. 2019

Journal of Advanced Transportation

View full text Add to dashboard Cite

show abstract

“…Hence, it is cost-effective for commuters to change between the metro and ground-level bus system and it can reduce car usage and traffic congestion in urban areas. However, inefficient transfer design can reduce the attractiveness of public transportation [4]. Nowadays, it is very significant to find and improve poorly connected transfer stations by using the huge transaction data collected by automatic fare collection systems (AFCSs) and automatic vehicle location systems (AVLSs), which can increase the commuters' willingness and mitigate prevalent traffic congestion.…”

Section: Introductionmentioning

confidence: 99%

Citywide Metro-to-Bus Transfer Behavior Identification Based on Combined Data from Smart Cards and GPS

Huang

Lin

et al. 2019

Applied Sciences

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The aim of this study is to develop a fast data fusion method for recognizing metro-to-bus transfer trips based on combined data from smart cards and a GPS system. The method is intended to establish station- and time-specific elapsed time thresholds for overcoming the limitations of one-size-fits-all criterion which is not sufficiently convincing for different transfer pairs and personal characteristics. Firstly, a data fusion method with bus smart card data and GPS data is proposed to supplement absent bus boarding information in the smart card data. Then, a model for identifying metro-to-bus interchange trips is derived based on two rules about maximal allowable transfer distance and elapsed transfer time threshold. Finally, in tests that used half-monthly field smart card data and GPS data from Shenzhen, China, the results recognized by the proposed method were more consistent with the actual surveyed group transfer time with a P value of 0.17 determined by Mann–Whitney U test. The comparison analysis showed that the proposed method can be widely applied to successfully identify and interpret metro-to-bus interchange behavior beyond a static transfer time threshold of 30 min.

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“…The urban railway station consists of various spaces: a platform, passage, concourse, complementary, and urban spaces [1]. Rather than being spaces used solely for the public transportation, these spaces are transformed into complex living spaces with various functionalities, including spaces of work, commerce, and leisure.…”

Section: Introductionmentioning

confidence: 99%

Identifying the Indoor Space Characteristics of an Urban Railway Station Based on Pedestrian Trajectory Data

Jeong

You

Liu

et al. 2019

Journal of Advanced Transportation

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Stations are being converted into various living spaces that can be used for public transportation, work, commerce, and leisure. To satisfy the various requirements and expectations for functional extension, it is necessary to investigate and understand the phenomena caused by users. A methodology to cluster the characteristics of pedestrian space of a railway station through the pedestrian trajectory data collected from an actual operating station is proposed in this paper. Then the spatial usability of the movement and stay of pedestrians were defined through the results of the clustering. The procedure to cluster the indoor space characteristics of an urban railway station in this study consists of four steps: data collection, feature vector extraction, K-means clustering, and cluster characteristics analysis. A case study was conducted for the Samseong station. The results of the proposed spatial clustering analysis showed that there are several types of spaces depending on the space occupancy characteristics of pedestrians. The proposed methodology could be applied to indoor space diagnosis from the perspective of station monitoring and management. In addition, the station operator could respond flexibly to unexpected events by monitoring the indoor spaces according to whether the flow is normal or suggestive of an emergency.

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Planning guidelines for metro–bus interchanges by means of a pedestrian microsimulation model

Cited by 27 publications

References 3 publications

Exploring the Effect of Boarding and Alighting Ratio on Passengers’ Behaviour at Metro Stations by Laboratory Experiments

Exploring the Effect of Boarding and Alighting Ratio on Passengers’ Behaviour at Metro Stations by Laboratory Experiments

Citywide Metro-to-Bus Transfer Behavior Identification Based on Combined Data from Smart Cards and GPS

Identifying the Indoor Space Characteristics of an Urban Railway Station Based on Pedestrian Trajectory Data

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