A State-of-the-Art Review on Empirical Data Collection for External Governed Pedestrians Complex Movement. (2nd September 2018)

• Record Type: Journal Article
• Authors: Shi, Xiaomeng; Ye, Zhirui; Shiwakoti, Nirajan; Grembek, Offer
• Other Names: Yasmin Shamsunnahar Academic Editor.
• Is Part Of: Journal of advanced transportation. Volume 2018(2018)
• Publication Date: 2018-09-02
• Subjects: Transportation -- Periodicals; 388.05
• DOI: 10.1155/2018/1063043
• Languages: English
• ISSNs: 0197-6729

Abstract

Abstract : Complex movement patterns of pedestrian traffic, ranging from unidirectional to multidirectional flows, are frequently observed in major public infrastructure such as transport hubs. These multidirectional movements can result in increased number of conflicts, thereby influencing the mobility and safety of pedestrian facilities. Therefore, empirical data collection on pedestrians' complex movement has been on the rise in the past two decades. Although there are several reviews of mathematical simulation models for pedestrian traffic in the existing literature, a detailed review examining the challenges and opportunities on empirical studies on the pedestrians complex movements is limited in the literature. The overall aim of this study is to present a systematic review on the empirical data collection for uni- and multidirectional crowd complex movements. We first categorized the complex movements of pedestrian crowd into two general categories, namely, external governed movements and internal driven movements based on the interactions with the infrastructure and among pedestrians, respectively. Further, considering the hierarchy of movement complexity, we decomposed the externally governed movements of pedestrian traffic into several unique movement patterns including straight line, turning, egress and ingress, opposing, weaving, merging, diverging, and random flows. Analysis of the literature showed that empirical data were highly rich in straight line and egress flow while medium rich in turning, merging, weaving, and opposing flows, but poor in ingress, diverging, and random flows. We put emphasis on the need for the future global collaborative efforts on data sharing for the complex crowd movements.