Traffic Flow Catastrophe Border Identification for Urban High-Density Area Based on Cusp Catastrophe Theory: A Case Study under Sudden Fire Disaster
Abstract
:1. Introduction
2. Literature Review
3. CCT-Based Traffic Flow Model
3.1. Cusp Catastrophe Theory
3.2. CCT-Based Traffic Flow Model under Sudden Fire Disaster
4. Simulation and Calibration
4.1. Simulation Scenarios and Data Collection
4.2. Data Processing and Analysis
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Location | HDC | GDC | Sudoku | Compare to HDC | Compare to GDC | ||
---|---|---|---|---|---|---|---|
Actual Volume (veh/h) | Actual Volume (veh/h) | Simulated Volume (veh/h) | Difference | Error (%) | Difference | Error (%) | |
1 | 1583 | 1613 | 1674 | 91 | 5.44 | 61 | 3.64 |
2 | 1784 | 1573 | 1711 | −73 | 4.27 | 138 | 8.07 |
3 | 1671 | 1771 | 1732 | 61 | 3.52 | −39 | 2.25 |
4 | 1569 | 1505 | 1525 | −44 | 2.89 | 20 | 1.31 |
5 | 1407 | 1313 | 1449 | 42 | 2.90 | 136 | 9.39 |
6 | 1607 | 1689 | 1804 | 197 | 10.92 | 115 | 6.37 |
Link | HDC | GDC | Sudoku | Compare to HDC | Compare to GDC | ||
---|---|---|---|---|---|---|---|
Actual Travel Time(s) | Actual Travel Time(s) | Simulated Travel Time(s) | Difference | Error (%) | Difference | Error (%) | |
1 | 665 | 806 | 737 | 71 | 9.70 | −69 | 9.38 |
2 | 661 | 711 | 798 | 136 | 17.09 | 87 | 10.93 |
3 | 742 | 758 | 876 | 134 | 15.33 | 118 | 13.43 |
4 | 713 | 732 | 816 | 103 | 12.58 | 83 | 10.23 |
5 | 757 | 819 | 782 | 25 | 3.18 | −37 | 4.79 |
6 | 727 | 731 | 628 | −99 | 15.69 | −103 | 16.33 |
Data Collection Interval (s) | Critical Speed (km/h) | Capacity (pcu//h) | Maximum Occupancy (%) |
---|---|---|---|
5 | 25.2375 | 1200 | 30.175 |
10 | 27.4333 | 1080 | 27.49167 |
15 | 24.62913 | 1030 | 27.34583 |
Data Collection Interval (s) | ||
---|---|---|
5 | −0.0001511 | −0.0621 |
10 | −0.0005264 | −1.059 |
15 | −0.001583 | −0.2745 |
Data Collection Interval (s) | Catastrophe Border | |
---|---|---|
5 | 20 | 439 |
10 | 20 | 529 |
15 | 20 | 377 |
Data Collection Interval | Catastrophe Border based on CCT (pcu/lane/h) | Catastrophe Border based on CAA (pcu/lane/h) | Relative Precision (%) |
---|---|---|---|
5 s | 439 | 493 | 89.1 |
10 s | 529 | 92.7 | |
15 s | 377 | 76.5 |
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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. https://doi.org/10.3390/app10093197
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. Applied Sciences. 2020; 10(9):3197. https://doi.org/10.3390/app10093197
Chicago/Turabian StyleLin, Ciyun, Yongli Yu, Dayong Wu, and Bowen Gong. 2020. "Traffic Flow Catastrophe Border Identification for Urban High-Density Area Based on Cusp Catastrophe Theory: A Case Study under Sudden Fire Disaster" Applied Sciences 10, no. 9: 3197. https://doi.org/10.3390/app10093197
APA StyleLin, C., Yu, Y., Wu, D., & Gong, B. (2020). Traffic Flow Catastrophe Border Identification for Urban High-Density Area Based on Cusp Catastrophe Theory: A Case Study under Sudden Fire Disaster. Applied Sciences, 10(9), 3197. https://doi.org/10.3390/app10093197