Towards Safer Pedestrian Traffic: Investigation of the Impact of Social Field Characteristic on Crowd Dynamics

Jingwan Fu (), Boxiao Cao (), Samer H. Hamdar () and Tianshu Li ()
Additional contact information
Jingwan Fu: The George Washington University, School of Engineering and Applied Science, Department of Civil and Environmental Engineering
Boxiao Cao: The George Washington University
Samer H. Hamdar: The George Washington University
Tianshu Li: The George Washington University

A chapter in Traffic and Granular Flow '17, 2019, pp 373-381 from Springer

Abstract: Abstract The objective of this paper is to investigate pedestrian safety on vehicle-free platforms. Towards realizing such objective, a microscopic pedestrian movement model is expanded to analyze the implications of different pedestrian behavioral characteristics on pedestrian movement under different density levels. This microscopic modeling approach is flexible and may be efficiently implemented while accounting for different traffic dynamics caused by complex geometric and operational features, such as those observed in transit stations, football stadiums, and rallies. The integrated modeling framework is built based on the Social Field method (i.e., the social force model): the surrounding stimulus is considered, while adding a stopping/vibration module and a tangential force module to the basic Social Field method to account for additional behavioral dimensions based on atomistic interactions between particles. C++ was used to build a simulator to obtain the trajectory of pedestrians in the system. The research has been already translated to two simulated scenarios: the bottleneck scenario and the bi-direction flow scenario. Realistic flow patterns have been produced with a triangular fundamental diagram (flow-density curve) as observed in real-life conditions. When the density goes up, the flow will go up then go down to meet the capacity of the system. The expanded social force model provided improved (lower) error levels once the simulated pedestrian trajectories are compared to the observed pedestrian trajectories. Results show the expanded model produces high-density congestion dynamics that are not captured by the traditional social force model. The dynamics represented the higher clustering of flow-density data points at low flow and high pedestrian levels represent these dynamics.

Date: 2019
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-11440-4_41

Ordering information: This item can be ordered from
http://www.springer.com/9783030114404

DOI: 10.1007/978-3-030-11440-4_41

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().