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.
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.
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.
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.
Abstract
For traffic management under sudden disaster in high-density areas, the first and foremost step is to prevent traffic congestion in the disaster-affected area by traffic flow control, 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 manage 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 439pcu/lane/h, 529pcu/lane/h, 377pcu/lane/h at 5s, 10s, 15s data collection interval 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 10s 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.
Keywords
catastrophe border identification; sudden fire disaster; emergency management; cusp catastrophe theory; high-density urban area
Subject
Engineering, Civil Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
