Modeling pedestrians’ interest in locations: A concept to improve simulations of pedestrian destination choice

Kielar, Peter M.; Borrmann, André

doi:10.1016/j.simpat.2015.11.003

Cited by 37 publications

(20 citation statements)

References 56 publications

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“…When considering pedestrian movements, route selection and pedestrian behavior at different types of intersections are connected and should be observed in the context of the entire pedestrian movement [5]. The route selection must be observed in the spatial and semantic context and influential factors related to the motivation, content of the location and psychology are numerous [6], as well as the ones related to the dynamics of changing preferences of the users [7], which should be analyzed in generating timebased origin-destination matrices. In order to analyze pedestrian behavior at a specific type of intersection, the most commonly applied is modeling at microsimulation level.…”

Section: Introductionmentioning

confidence: 99%

Development of Models for Children—Pedestrian Crossing Speed at Signalized Crosswalks

et al. 2021

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Modeling the behavior of pedestrians is an important tool in the analysis of their behavior and consequently ensuring the safety of pedestrian traffic. Children pedestrians show specific traffic behavior which is related to cognitive development, and the parameters that affect their traffic behavior are very different. The aim of this paper is to develop a model of the children-pedestrian’s speed at a signalized pedestrian crosswalk. For the same set of data collected in the city of Osijek—Croatia, two models were developed based on neural network and multiple linear regression. In both cases the models are based on 300 data of measured children speed at signalized pedestrian crosswalks on primary city roads located near a primary school. As parameters, both models include the selected traffic infrastructure features and children’s characteristics and their movements. The models are validated on data collected on the same type of pedestrian crosswalks, using the same methodology in two other urban environments—the city of Rijeka, Croatia and Enna in Italy. It was shown that the neural network model, developed for Osijek, can be applied with sufficient reliability to the other two cities, while the multiple linear regression model is applicable with relatively satisfactory reliability only in Rijeka. A comparative analysis of the statistical indicators of reliability of these two models showed that better results are achieved by the neural network model.

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Section: Introductionmentioning

confidence: 99%

Development of Models for Children—Pedestrian Crossing Speed at Signalized Crosswalks

et al. 2021

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“…[4]). Computer modellers have researched the factors influencing pedestrian movement in order to create models which accurately predict movement in a variety of crowd scenarios, from evacuations [5,6], to pedestrian flow in crowded spaces [7][8][9]. Biologists have shown that we can gain insight to human crowd movement by looking to the behavioural patterns of social insects, fish and other non-human animals [10,11].…”

Section: Introductionmentioning

confidence: 99%

Walking together: behavioural signatures of psychological crowds

2018

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Research in crowd psychology has demonstrated key differences between the behaviour of physical crowds where members are in the same place at the same time, and the collective behaviour of psychological crowds where the entire crowd perceive themselves to be part of the same group through a shared social identity. As yet, no research has investigated the behavioural effects that a shared social identity has on crowd movement at a pedestrian level. To investigate the direction and extent to which social identity influences the movement of crowds, 280 trajectories were tracked as participants walked in one of two conditions: (1) a psychological crowd primed to share a social identity; (2) a naturally occurring physical crowd. Behaviour was compared both within and between the conditions. In comparison to the physical crowd, members of the psychological crowd (i) walked slower, (ii) walked further, and (iii) maintained closer proximity. In addition, pedestrians who had to manoeuvre around the psychological crowd behaved differently to pedestrians who had to manoeuvre past the naturally occurring crowd. We conclude that the behavioural differences between physical and psychological crowds must be taken into account when considering crowd behaviour in event safety management and computer models of crowds.

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“…( 5) can be further improved and extended by adding additional behavioral features. For instance, explicit representation of group behavior [52] and an interest function [68] can be added to the joining behavior model. A natural progression of this work is to analyze the numerical simulation results from the perspective of capacity estimation.…”

Section: Discussionmentioning

confidence: 99%

Jamming transitions induced by an attraction in pedestrian flow

Kwak

Luttinen

et al. 2017

Phys. Rev. E

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We numerically study jamming transitions in pedestrian flow interacting with an attraction, mostly based on the social force model for pedestrians who can join the attraction. We formulate the joining probability as a function of social influence from others, reflecting that individual choice behavior is likely influenced by others. By controlling pedestrian influx and the social influence parameter, we identify various pedestrian flow patterns. For the bidirectional flow scenario, we observe a transition from the free flow phase to the freezing phase, in which oppositely walking pedestrians reach a complete stop and block each other. On the other hand, a different transition behavior appears in the unidirectional flow scenario, i.e., from the free flow phase to the localized jam phase and then to the extended jam phase. It is also observed that the extended jam phase can end up in freezing phenomena with a certain probability when pedestrian flux is high with strong social influence. This study highlights that attractive interactions between pedestrians and an attraction can trigger jamming transitions by increasing the number of conflicts among pedestrians near the attraction. In order to avoid excessive pedestrian jams, we suggest suppressing the number of conflicts under a certain level by moderating pedestrian influx especially when the social influence is strong.

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Modeling pedestrians’ interest in locations: A concept to improve simulations of pedestrian destination choice

Cited by 37 publications

References 56 publications

Development of Models for Children—Pedestrian Crossing Speed at Signalized Crosswalks

Development of Models for Children—Pedestrian Crossing Speed at Signalized Crosswalks

Walking together: behavioural signatures of psychological crowds

Jamming transitions induced by an attraction in pedestrian flow

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