A critical assessment of pedestrian behaviour models

Papadimitriou, Eleonora; Yannis, George; Golias, John

doi:10.1016/j.trf.2008.12.004

Cited by 254 publications

(117 citation statements)

References 50 publications

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“…Nevertheless, pedestrians naturally adapt to their surroundings [16]. The mean accepted gap for pedestrian to make a cross is estimated to be 8 seconds [17].…”

Section: Literature Overviewmentioning

confidence: 99%

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Crossing Behaviour of Pedestrians Along Urban Streets in Malaysia

et al. 2017

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Abstract. Road crossings are considered as an unavoidable part of walking in which the desirable route of pedestrians interacts with vehicles. These interactions may expose the pedestrians to risks or delays. In Malaysia, road accident statistics show that pedestrian casualties are fairly high. Inappropriate gap acceptance when pedestrians cross roads is a main contributing element to this situation. In this context, the purpose of this study was to develop realistic models for pedestrian road crossing behaviour using the regression technique for mid-block street crossing. A choice model was produced to capture the decision making process of pedestrians whereas rejected or accepted vehicular gaps was based on the discrete choice theory. Gap acceptance data under real mix traffic conditions was collected using video camera on a typical unsignalised two lane one way urban street section in the city center of Kuala Lumpur, Malaysia. The lognormal regression model developed for the crossing behaviour model shows that traffic speed, pedestrian waiting time, gender, crossing distance, age group, frequency of attempts and pedestrian number are the significant factors which are able to predict 77.0% of variance or changes in accepted gap size at 0.05 significance level. Higher traffic speed, lower waiting time, being a male, wider crossing distance, older age group, lower frequency of attempts and higher number of pedestrian were found to influence pedestrians to accept a bigger gap size. The binary logistic regression developed for the crossing choice model was found to be influenced by traffic speed, driver yield, pedestrian number and age group. Furthermore, lower traffic speed, willingness of drivers to slow down, more pedestrian crossings at the same time and a younger age group lead to a higher chance or probability of crossing roads. The model was validated again using 100 isolated samples and an accuracy of 98% was obtained compared to the calibrated model which yielded an accuracy of 98.9%.

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“…Nevertheless, pedestrians naturally adapt to their surroundings [16]. The mean accepted gap for pedestrian to make a cross is estimated to be 8 seconds [17].…”

Section: Literature Overviewmentioning

confidence: 99%

“…These gaps are often described by estimating the means of liner regression modeling or by probability distributions. Indicatively, it can be declared that the minimum accepted gap has been estimated to be 2 seconds while the mean accepted gap has been estimated to be 8 seconds [17].…”

Section: Introductionmentioning

confidence: 99%

Crossing Behaviour of Pedestrians Along Urban Streets in Malaysia

et al. 2017

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“…However, most models focus specifically on crowd or evacuation dynamics and not so much on modelling entire trips of pedestrians in regular traffic in public spaces. Despite advances in modelling techniques towards sophisticated ABMs, they still have challenges in reproducing real world behaviours reliably in all situations (Castle and Crooks, 2006;Crooks et al, 2008;Papadimitriou et al, 2009). A main reason for that collectively mentioned in the literature is that there exist few empirical studies and verified standard HSD datasets from recordings of real life pedestrian and bicycle traffic to calibrate the models against.…”

Section: Mapping and Modelling Human Movement And Behaviour In Publicmentioning

confidence: 99%

Thermal imaging systems for real-time applications in smart cities

Gade

Moeslund

Nielsen

et al. 2016

IJCAT

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Abstract:In the modern world, cities need to keep up with the demand for mobility, efficient infrastructure and environmental sustainability. The future smart cities use intelligent information and communication technologies to raise the quality of life. This includes computer vision as one of the main technologies. It can observe and analyse human activities from a distance in a noninvasive manner. Traditional computer vision utilises RGB cameras, but problems with this sensor include its light dependency, and the privacy issues that can be raised by people being observed. In this paper, we propose the use of thermal imaging in real-time smart city applications. Thermal cameras operate independently of light and measure the radiated infrared waves representing the temperature of the scene. In order to showcase the possibilities, we present five different applications which use thermal imaging only. These include both indoor and outdoor scenarios with the purposes of people detection, counting and tracking, as well as one application for traffic safety evaluation.

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“…In transportation science, quantitative assessment was done to analyze the effects of physical settings on transportation routes and time (Braess et al, 2005;Jin et al, 2008;Papadimitriou et al, 2009;Stepanov and Smith, 2009). Service has also been a research topic for quantitative evaluation, but previous analyses have been limited to the concept of service process and its productivity (Gronroos and Ojasalo, 2004).…”

Section: Quantitative Evaluations On Service Environmentsmentioning

confidence: 99%

Development of Evaluation Metrics for Pedestrian Flow Optimization in a Complex Service Environment Based on Behavior Observation Method

Bahn¹,

Lee²,

Kwon³

et al. 2010

Journal of the Ergonomics Society of Korea

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In a service environment, the spatial layout is an important factor that has a great impact on customers' behavioral characteristics including wayfinding and purchasing. Previous studies have shown a gap between marketing, focusing solely on profitability and satisfaction, and architecture, looking only into efficiency of pedestrian flow. To balance such disparity, this study suggests an integrated approach for assessing behavioral patterns in complex service environments. With the objective that complex service environments should aim to increase its profitability and efficiency while guaranteeing customer satisfaction, quantitative metrics was developed for evaluation. The metrics was defined to use data from behavior observation including path tracking, population counting, and gaze analysis, while previous studies have relied on abstract survey methods that were prone to sampling errors and loss of data. For validation of the metrics in a real world setting, a case study was conducted at 4 train stations in Korea. In the case study, experiments were conducted to gather the required data in all 4 train stations, while their physical layouts were also analyzed. With the results from the case study, comparative evaluation of the 4 train stations in terms of behavioral efficiency was possible, together with a discussion on the effect of their physical settings.

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A critical assessment of pedestrian behaviour models

Cited by 254 publications

References 50 publications

Crossing Behaviour of Pedestrians Along Urban Streets in Malaysia

Crossing Behaviour of Pedestrians Along Urban Streets in Malaysia

Thermal imaging systems for real-time applications in smart cities

Development of Evaluation Metrics for Pedestrian Flow Optimization in a Complex Service Environment Based on Behavior Observation Method

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