Modelling Pedestrian Travel Time and the Design of Facilities: A Queuing Approach

Rahman, Khalidur; Ghani, Noraida Abdul; Kamil, Anton Abdulbasah; Mustafa, Adli; Chowdhury, Md. Ahmed Kabir

doi:10.1371/journal.pone.0063503

Cited by 33 publications

(12 citation statements)

References 17 publications

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“…In the pedestrian modeling context, queuing theory describes the facilities as graphs, where each node represents a region. A queue appears in a node when a positive difference exists between the service demanded and the service provided by the node [Rahman et al 2013]. Different probabilistic models can be used to describe the arrival intensity to the queue and the service mechanism.…”

Section: Stochastic Modelsmentioning

confidence: 99%

“…The theory tries to set up a model for the dynamics of the queues that represent a pedestrian flow in a lane. Following the work by Rahman et al [2013]: "The basic entities which characterize a queueing model are: i) the arrival date, ii) the service mechanism iii) the queue strategy (e.g. first come first served) and iv) the number of service nodes".…”

Section: Stochastic Modelsmentioning

confidence: 99%

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Modeling, Evaluation, and Scale on Artificial Pedestrians

et al. 2017

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Modeling pedestrian dynamics and their implementation in a computer are challenging and important issues in the knowledge areas of transportation and computer simulation. The aim of this paper is to provide a bibliographic outlook so that the reader could have a quick access to the most relevant works related with this problem. We have used three main axes to organise the paper contents: pedestrian models, validation techniques and multiscale approaches. The backbone of the paper is the classification of existing pedestrian models; we have organised the works in the literature under five categories, according to the techniques used for implementing the operational level in each pedestrian model. Then, the main existing validation methods, oriented to evaluate the behavioural quality of the simulation systems, are reviewed. Furthermore, we review the key issues that arise when facing multiscale pedestrian modeling, where we firstly focus on the behavioural scale (combinations of micro and macro pedestrian models) and secondly, on the scale size (from individuals to crowds). The paper begins introducing the main characteristics of walking dynamics and its analysis tools and concludes with a discussion about the contributions that different knowledge fields can do in a near future to this exciting area.

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Section: Stochastic Modelsmentioning

confidence: 99%

Section: Stochastic Modelsmentioning

confidence: 99%

Modeling, Evaluation, and Scale on Artificial Pedestrians

et al. 2017

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“…It can be employed for motion analysis [1], where it can be used to identify crowd behavior attributes. Another important area of application is image-based surveillance or visual features extractions for person identification [2,3]. Other applications include video analytics for business intelligence, or searching a criminal database for suspects using the identified visual attributes.…”

Section: Introductionmentioning

confidence: 99%

Pedestrian attribute recognition using two-branch trainable Gabor wavelets network

Junejo

2021

PLoS ONE

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Keeping an eye on pedestrians as they navigate through a scene, surveillance cameras are everywhere. With this context, our paper addresses the problem of pedestrian attribute recognition (PAR). This problem entails recognizing attributes such as age-group, clothing style, accessories, footwear style etc. This multi-label problem is extremely challenging even for human observers and has rightly garnered attention from the computer vision community. Towards a solution to this problem, in this paper, we adopt trainable Gabor wavelets (TGW) layers and cascade them with a convolution neural network (CNN). Whereas other researchers are using fixed Gabor filters with the CNN, the proposed layers are learnable and adapt to the dataset for a better recognition. We propose a two-branch neural network where mixed layers, a combination of the TGW and convolutional layers, make up the building block of our deep neural network. We test our method on twoo challenging publicly available datasets and compare our results with state of the art.

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“…However, to the best of our knowledge, it is inappropriate for the studies in subway stations with multi-directional flow, and various or irregular geometry. Most queuing models [23] mainly focus on modeling of individual facilities in stations, by regarding facilities and passengers as service desks and customers, respectively, and it perform well in describing the queuing process. Ref.…”

Section: Introductionmentioning

confidence: 99%

Applying System Dynamics to Simulate the Passenger Flow in Subway Stations

Gao

Jia

Guo

2019

Discrete Dynamics in Nature and Society

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A macroscopic passenger flow simulation model based on system dynamics is proposed in this paper. It considers the key factors influencing the dynamics of passenger flow from a holistic perspective of the stations and then models the dynamic change in the number of passengers. Firstly, the transmission of passenger flow for a general many-to-many relation between nodes are presented. When the sum of sending capacities heading for a downstream node is less than the receiving capacity of this node, the aggregation of stranded passengers will form the queuing part of the node. The results coming from a passenger flow simulation of a subway station in Beijing show that the proposed model performs well by comparing it with the real data. It can be applied to describe the dynamic change in the number of passengers and the level of service for the facilities.

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Modelling Pedestrian Travel Time and the Design of Facilities: A Queuing Approach

Cited by 33 publications

References 17 publications

Modeling, Evaluation, and Scale on Artificial Pedestrians

Modeling, Evaluation, and Scale on Artificial Pedestrians

Pedestrian attribute recognition using two-branch trainable Gabor wavelets network

Applying System Dynamics to Simulate the Passenger Flow in Subway Stations

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