A multi-objective route optimization method for passenger evacuations at subway stations during a fire outbreak

YANG Xiao-xia; ZHANG Rui; LI Yong-xing; QU Da-yi

doi:10.19818/j.cnki.1671-1637.2023.05.013

Volume 23 Issue 5

Oct. 2023

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YANG Xiao-xia, ZHANG Rui, LI Yong-xing, QU Da-yi. A multi-objective route optimization method for passenger evacuations at subway stations during a fire outbreak[J]. Journal of Traffic and Transportation Engineering, 2023, 23(5): 192-209. doi: 10.19818/j.cnki.1671-1637.2023.05.013

Citation:

YANG Xiao-xia, ZHANG Rui, LI Yong-xing, QU Da-yi. A multi-objective route optimization method for passenger evacuations at subway stations during a fire outbreak[J]. Journal of Traffic and Transportation Engineering, 2023, 23(5): 192-209. doi: 10.19818/j.cnki.1671-1637.2023.05.013

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A multi-objective route optimization method for passenger evacuations at subway stations during a fire outbreak

doi: 10.19818/j.cnki.1671-1637.2023.05.013

1.
School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266520, Shandong, China
2.
School of Civil Engineering, Qingdao University of Technology, Qingdao 266520, Shandong, China
3.
Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China
4.
School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, Shandong, China

Funds:

National Natural Science Foundation of China 62373209

National Natural Science Foundation of China 62003182

National Natural Science Foundation of China 52272311

More Information

Author Bio:
YANG Xiao-xia(1988-), female, associate professor, PhD, yangxiaoxiaaza@163.com

QU Da-yi(1973-), male, professor, PhD, dyqu@263.net
Received Date: 2023-05-10
Available Online: 2023-11-17
Publish Date: 2023-10-25

Abstract

Abstract

A passenger evacuation network at subway stations was established, the degrees of network nodes were analyzed to identify key nodes, and a modified walking evacuation network was proposed after node failures. A time prediction model for passengers traveling through the gate and stair/escalator nodes was designed based on the support vector regression algorithm, and its prediction performance was analyzed. A quantitative relationship between the number of passengers and travel time was obtained. Three objective functions, namely the total evacuation time including the node and road section travel time, the total road risk, and the total congestion cost, were established. A multi-objective route optimization model for passenger evacuation at subway stations during a fire outbreak was constructed, and a solution method of optimization model was proposed based on the genetic algorithm. The evacuation movement of passengers at subway stations during a fire outbreak was simulated, and the evacuation efficiencies under the Pareto solution of the route optimization model was analyzed, then the optimization degree of the route optimization strategy was evaluated. A WeChat applet for passenger dynamic guidance was designed, providing a possible solution for the timely release of evacuation route recommendation information. Research results indicate that the mean absolute error of the passenger travel time prediction model at the nodes for the validation set can be as low as 0.000 375, and the robust indicator value is up to 0.999 334, indicating a high degree of consistency between predicted data and real data. The average relative error between the field collected data at the gate and the simulated data is 4.9%. The significance values of normality test, homogeneity test of variance, and independent sample test are all greater than 0.05, verifying that the PathFinder software can simulate passenger movements accurately. Compared with the normal evacuation without optimization strategies, the optimization degrees of the route optimization model under three sets of Pareto solutions are 16.7%, 15.9%, and 18.0%, respectively. Therefore, corresponding evacuation optimization strategies can be selected based on specific evacuation scenarios, risk factors, service quality requirements, and other indicators.
- traffic planning,
- route planning,
- multi-objective optimization,
- passenger evacuation,
- fire,
- subway station

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References(31)

References

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