Next Article in Journal
Influence of Plasma Characteristics on the Inactivation Mechanism of Cold Atmospheric Plasma (CAP) for Listeria monocytogenes and Salmonella Typhimurium Biofilms
Previous Article in Journal
Density, Viscosity, and Excess Properties of MDEA + H2O, DMEA + H2O, and DEEA + H2O Mixtures
Open AccessArticle

Traffic Flow Catastrophe Border Identification for Urban High-Density Area Based on Cusp Catastrophe Theory: A Case Study under Sudden Fire Disaster

by Ciyun Lin 1, Yongli Yu 1, Dayong Wu 2,* and Bowen Gong 1,3
1
Department of Traffic Information and Control Engineering, Jilin University, Changchun 130022, China
2
Texas A&M Transportation Institute, Texas A&M University, College Station, TX 77843, USA
3
Jilin Engineering Research Center for ITS, Changchun 130022, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(9), 3197; https://doi.org/10.3390/app10093197
Received: 7 April 2020 / Revised: 23 April 2020 / Accepted: 30 April 2020 / Published: 4 May 2020
(This article belongs to the Section Civil Engineering)
For traffic management under sudden disasters in high-density areas, the first and foremost step is to prevent traffic congestion in the disaster-affected area by traffic flow management and control, so as to provide enough and flexible traffic capacity for emergency evacuation and emergency rescue. Catastrophe border identification is the foundation and the key to traffic congestion prediction under sudden disaster. This paper uses a mathematical model to study the regional traffic flow in the high-density area under sudden fire disaster based on the Cusp Catastrophe Theory (CCT). The catastrophe border is identified by fitting the CCT-based regional traffic flow model to explore the stable traffic flow changing to the instable state, as to provide a theoretical basis for traffic flow management and control in disaster-affected areas, and to prevent the traffic flow being caught into disorder and congestion. Based on VISSIM simulator data by building simulation scenarios with and without sudden fire disaster in a Sudoku traffic network, the catastrophe border is identified as 439 pcu/lane/h, 529 pcu/lane/h, 377 pcu/lane/h at 5 s, 10 s, 15 s data collection interval in a Sudoku traffic network respectively. The corresponding relative precision, which compares to the method of Capacity Assessment Approach (CAA), is 89.1%, 92.7% and 76.5% respectively. It means that 10 s data collection interval would be the suitable data collection interval in catastrophe border identification and regional traffic flow control in high-density area under sudden fire disaster. View Full-Text
Keywords: catastrophe border identification; traffic congestion; dynamic traffic management; risk management; cusp catastrophe theory; VISSIM catastrophe border identification; traffic congestion; dynamic traffic management; risk management; cusp catastrophe theory; VISSIM
Show Figures

Figure 1

MDPI and ACS Style

Lin, C.; Yu, Y.; Wu, D.; Gong, B. Traffic Flow Catastrophe Border Identification for Urban High-Density Area Based on Cusp Catastrophe Theory: A Case Study under Sudden Fire Disaster. Appl. Sci. 2020, 10, 3197.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop