Age and Group-driven Pedestrian Behaviour: from Observations to Simulations

Gorrini, Andrea; Vizzari, Giuseppe; Bandini, Stefania

doi:10.17815/cd.2016.3

Cited by 47 publications

(35 citation statements)

References 20 publications

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“…The influential parameters were selected based on analysis of existing studies among those that resulted important when it comes to research of pedestrian behavior. Many studies have shown that children's behavior in traffic is influenced by their age, gender [21,23,24] control of inhibitors, parental expectations and beliefs [25], parental supervision [26], the complexity of the situation [19], the distraction of attention [27,28], and various development and other parameters that are not measurable in real traffic conditions.…”

Section: Influential Parameters and Field-testing Methodologymentioning

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: Influential Parameters and Field-testing Methodologymentioning

confidence: 99%

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

et al. 2021

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“…Broken groups can regroup to keep the unity [9,28]. When individuals meet groups, singles are statistically walking faster and are more likely to adjust their routes [6,10]. Stationary groups act as obstacles [48,49].…”

Section: Introductionmentioning

confidence: 99%

SR-LSTM: State Refinement for LSTM Towards Pedestrian Trajectory Prediction

Zhang

Ouyang

Zhang

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

474

282

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In crowd scenarios, reliable trajectory prediction of pedestrians requires insightful understanding of their social behaviors. These behaviors have been well investigated by plenty of studies, while it is hard to be fully expressed by hand-craft rules. Recent studies based on LSTM networks have shown great ability to learn social behaviors. However, many of these methods rely on previous neighboring hidden states but ignore the important current intention of the neighbors. In order to address this issue, we propose a data-driven state refinement module for LSTM network (SR-LSTM), which activates the utilization of the current intention of neighbors, and jointly and iteratively refines the current states of all participants in the crowd through a message passing mechanism. To effectively extract the social effect of neighbors, we further introduce a social-aware information selection mechanism consisting of an element-wise motion gate and a pedestrian-wise attention to select useful message from neighboring pedestrians. Experimental results on two public datasets, i.e. ETH and UCY, demonstrate the effectiveness of our proposed SR-LSTM and we achieves state-of-the-art results.

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“…The presence of social groups can influence the dynamics of pedestrian crowds. Social groups often move slower and reduce the walking speed of the pedestrians [3,8,9,10]. However, Manenti et al [11] observed that group members can walk faster than individuals in high density scenarios.…”

Section: Introductionmentioning

confidence: 99%

Empirical study on social groups in pedestrian evacuation dynamics

Krüchten

Schadschneider

2017

Physica A: Statistical Mechanics and its Applications

141

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Pedestrian crowds often include social groups, i.e. pedestrians that walk together because of social relationships. They show characteristic configurations and influence the dynamics of the entire crowd. In order to investigate the impact of social groups on evacuations we performed an empirical study with pupils. Several evacuation runs with groups of different sizes and different interactions were performed. New group parameters are introduced which allow to describe the dynamics of the groups and the configuration of the group members quantitatively. The analysis shows a possible decrease of evacuation times for large groups due to self-ordering effects. Social groups can be approximated as ellipses that orientate along their direction of motion. Furthermore, explicitly cooperative behaviour among group members leads to a stronger aggregation of group members and an intermittent way of evacuation.

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Age and Group-driven Pedestrian Behaviour: from Observations to Simulations

Cited by 47 publications

References 20 publications

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

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

SR-LSTM: State Refinement for LSTM Towards Pedestrian Trajectory Prediction

Empirical study on social groups in pedestrian evacuation dynamics

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