Journal of Advanced Transportation

Empirical Research on Pedestrians’ Behavior and Crowd Dynamics


Publishing date
01 Aug 2019
Status
Published
Submission deadline
05 Apr 2019

Lead Editor

1University of Sydney, Sydney, Australia

2University of Bristol, Bristol, UK

3Forschungszentrum Jülich, Jülich, Germany

4Eindhoven University of Technology, Eindhoven, Netherlands

5National Research Council of Italy, Rome, Italy


Empirical Research on Pedestrians’ Behavior and Crowd Dynamics

Description

The increasing urban population around the world has made the control and management of crowded public facilities a challenge to urban planners and safety authorities. Transportation hubs and large-scale buildings host an increasing number of users and occupants; and mass gatherings are more frequent than ever before. This motivated the development of dedicated models and more accurate knowledge of pedestrian movement to improve safety and comfort in crowded public facilities, prevent overcrowding, and plan for cases of an evacuation.

Within this area of research, a major gap is the fact that empirical research is disproportionately underrepresented compared to the body of theoretical and purely-computational studies. The lack of adequate empirical studies has left many theoretical models of crowd dynamics unverified, with their reliability and prediction outcomes often being subject to skepticism.

This special issue aims at reducing the gap between the theoretical and empirical body of knowledge in the domain of pedestrian dynamics by encouraging data-driven studies. The issue covers studies ranging from normal-condition movements to evacuation scenarios. The main criterion of inclusion is the demonstration of substantial and robust links to empirical data (whether from experimental or field sources). We also strongly encourage studies related to the calibration and validation of pedestrian models. These models can encompass those of pedestrian motions and pedestrian dynamics to behavioral models of wayfinding, decision-making, and beyond. Purely experimental studies that make significant contributions to fundamental understanding of pedestrians’ behavior are also welcome.

Although the primary focus of this special issue is on empirical and data-driven studies, theoretical and methodological work that offer significant contributions to the field may also be considered. Also, simulation-based or computational studies that draw informative parallels with the existing empirical data (e.g., comparing simulated versus observational fundamental diagrams) and studies that report on simulation-based behavior modifications (e.g., identifying optimum behavior) may also be considered.

Potential topics include but are not limited to the following:

  • Innovative methods of data collection, measurement, and visualization in pedestrian research and crowd control (including machine- and deep-learning techniques)
  • External validity, generalizability, and replicability of pedestrian experiments
  • Applications of virtual-reality and augmented reality experiments (e.g., in the domain of emergency evacuations)
  • Evacuation drill experiments and studies that make systemic use of field data in actual emergencies like fires, earthquakes, and so on
  • Pedestrian safety in crowded walkways and intersections
  • New methodologies for simulation-based replication of well-known crowd phenomena or simulation-based reexamination of controversial crowd phenomena
  • Innovative collection and analyses of field pedestrian data in transportation hubs
  • Calibration and validation of modeling approaches for simulating complex transportation nodes

Articles

  • Special Issue
  • - Volume 2019
  • - Article ID 3457370
  • - Editorial

Empirical Research on Pedestrians’ Behavior and Crowd Dynamics

Milad Haghani | Nikolai W. F. Bode | ... | Emiliano Cristiani
  • Special Issue
  • - Volume 2019
  • - Article ID 2380348
  • - Research Article

‘Rationality’ in Collective Escape Behaviour: Identifying Reference Points of Measurement at Micro and Macro Levels

Milad Haghani | Majid Sarvi
  • Special Issue
  • - Volume 2019
  • - Article ID 9267643
  • - Review Article

Panic, Irrationality, and Herding: Three Ambiguous Terms in Crowd Dynamics Research

Milad Haghani | Emiliano Cristiani | ... | Alessandro Corbetta
  • Special Issue
  • - Volume 2019
  • - Article ID 7602792
  • - Research Article

A Modified Inverse Distance Weighting Method for Interpolation in Open Public Places Based on Wi-Fi Probe Data

Da-wei Wang | Lu-ning Li | ... | Pin-wen Huang
  • Special Issue
  • - Volume 2019
  • - Article ID 5874085
  • - Research Article

Multiobjective Calibration Framework for Pedestrian Simulation Models: A study on the Effect of Movement Base Cases, Metrics, and Density Levels

Martijn Sparnaaij | Dorine C. Duives | ... | Serge P. Hoogendoorn
  • Special Issue
  • - Volume 2019
  • - Article ID 2015671
  • - Research Article

Understanding Pedestrian Interactive Behaviors under the Different Level of Services on Stairways

Jianhong Ye | Xiaonian Shan | ... | Mengxiao Yu
  • Special Issue
  • - Volume 2019
  • - Article ID 9717208
  • - Research Article

The Influence of Wheelchair Users on Movement in a Bottleneck and a Corridor

Paul Geoerg | Jette Schumann | ... | Anja Hofmann
  • Special Issue
  • - Volume 2019
  • - Article ID 9072358
  • - Research Article

Identifying High-Risk Intersections for Walking and Bicycling Using Multiple Data Sources in the City of San Diego

Mahdie Hasani | Arash Jahangiri | ... | Sahar Ghanipoor Machiani
  • Special Issue
  • - Volume 2019
  • - Article ID 8401318
  • - Research Article

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

Eunbi Jeong | Soyoung Iris You | ... | Daeseop Moon
  • Special Issue
  • - Volume 2019
  • - Article ID 6530897
  • - Research Article

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

Sebastian Seriani | Rodrigo Fernandez | ... | Taku Fujiyama
Journal of Advanced Transportation
 Journal metrics
Acceptance rate36%
Submission to final decision106 days
Acceptance to publication75 days
CiteScore3.000
Impact Factor1.670
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