动态网络算法在城市路网应急场景中的应用综述

doi:10.16265/j.cnki.issn1003-3033.2025.10.1633

摘要/Abstract

摘要： 为解决城市应急场景日益复杂化、多元化对动态网络算法应用带来的异构数据融合和算法协同应用新挑战,系统梳理动态网络算法的情景应用特征,探索多算法协同与集成的实现路径。首先,基于Scopus数据库近5年128篇文献,采用关键词词频统计与聚类分析方法,识别出路径规划、流量调控、风险防范及韧性分析4大研究主题;然后,通过文献内容分析归纳规划、模拟、聚类与深度学习4类核心算法,结合实证、仿真及混合数据源,解析其理论架构与任务适配逻辑;最后,建立多维度评估体系,综合对比算法在不同应急场景中的适用性、优势及短板。结果表明:通过规划、模拟、聚类和深度学习4类算法的多元集成,可实现城市应急管理中路径规划、流量调控、风险防范与韧性分析的多维响应,提升应急体系的适应性与稳健性;数据驱动下的智能算法将进一步提高城市路网响应效率,促进实时策略调整与资源优化;未来发展方向应聚焦于多网融合的耦合分析及算法集成的智能协同架构建设,助力动态网络算法在多场景应急管理中的高效应用。

关键词: 城市路网, 应急管理, 动态网络算法, 路径规划, 流量调控, 风险防范, 韧性分析

Abstract:

In response to new challenges, the application characteristics of dynamic network algorithms were reviewed. These challenges were posed by complex and diversified urban emergency scenarios. Heterogeneous data fusion and algorithmic collaboration were involved. Additionally, approaches for multi-algorithm collaboration and integration were explored. Firstly, based on 128 publications from the past five years in the Scopus database, four major research themes: path planning, traffic regulation, risk prevention, and resilience analysis, which were identified using keyword frequency statistics and cluster analysis. Then, through content analysis, four core algorithm categories: planning, simulation, clustering, and deep learning were summarized. Their theoretical frameworks and task adaptation logic were examined in conjunction with empirical data, simulations, and hybrid data sources. Finally, a multidimensional evaluation system was constructed to comparatively assess the applicability, strengths, and limitations of different algorithms across various emergency scenarios. The results show that multiple algorithms are integrated in application,which include planning, simulation, clustering, and deep learning. This integrated approach enables a multidimensional response to urban emergency management tasks. Such tasks involve path planning, traffic regulation, risk prevention, and resilience analysis. The adaptability of emergency systems are enhanced. The robustness of these systems is also improved. Data-driven intelligent algorithms further improve the responsiveness of urban road networks, supporting real-time strategy adjustment and resource optimization. Future development should focus on the construction of intelligent collaborative architectures for algorithm integration and coupled analysis across multiple networks, to advance the efficient application of dynamic network algorithms in multi-scenario emergency management.

Key words: urban road network, emergency management, dynamic network algorithms, path planning, traffic flow control, risk prevention, resilience analysis

中图分类号:

X913.4安全系统工程

张子扬, 杨赛霓. 动态网络算法在城市路网应急场景中的应用综述[J]. 中国安全科学学报, 2025, 35(10): 149-156.

ZHANG Ziyang, YANG Saini. Review of application of dynamic network algorithms in urban road network emergency scenarios[J]. China Safety Science Journal, 2025, 35(10): 149-156.

图/表 2

参考文献 40

[1]	KAPUCU N. Disaster and emergency management systems in urban areas[J]. Cities, 2012, 29: 41-49. doi: 10.1016/j.cities.2011.11.009 pmid: 32287822
[2]	ROHR A, PRIESMEIER P, TZAVELLA K, et al. System criticality of road network areas for emergency management services:spatial assessment using a tessellation approach[J]. Infrastructures, 2020, 5(11):DOI:10.3390/infrastructures5110099.
[3]	DING Lin, ZHANG Hanchao, LI Deren. Monitoring and analysis of urban sprawl based on road network data and high-resolution remote sensing imagery: a case study of China's provincial capitals[J]. Photogrammetric Engineering & Remote Sensing, 2022, 88(7): 479-486.
[4]	詹伟, 程春鑫. 基于GWO-BP的震后过渡安置阶段应急物资需求预测[J]. 中国安全科学学报, 2024, 34(10):17-23. doi: 10.16265/j.cnki.issn1003-3033.2024.10.0131
	ZHAN Wei, CHENG Chunxin. GWO-BP-based forecasting of emergency material demand in post-earthquake transitional resettlement phase[J]. China Safety Science Journal, 2024, 34(10): 17-23. doi: 10.16265/j.cnki.issn1003-3033.2024.10.0131
[5]	PARIZI S M, TALEAI M, SHARIFI A. A spatial evaluation framework of urban physical resilience considering different phases of disaster risk management[J]. Natural Hazards, 2024, 120(14): 1-36. doi: 10.1007/s11069-023-06176-7
[6]	耿劭卿, 侯汉平, 周洲. 基于文献计量分析的应急避难所救援系统综述[J]. 中国安全科学学报, 2023, 33(3):195-203. doi: 10.16265/j.cnki.issn1003-3033.2023.03.0676
	GENG Shaoqing, HOU Hanping, ZHOU Zhou. A review of emergency shelter rescue system based on bibliometric analysis[J]. China Safety Science Journal, 2023, 33(3): 195-203. doi: 10.16265/j.cnki.issn1003-3033.2023.03.0676
[7]	戢晓峰, 杨春丽, 郝京京, 等. 国内外应急物流研究热点对比与展望[J]. 中国安全科学学报, 2021, 31(12):144-152. doi: 10.16265/j.cnki.issn 1003-3033.2021.12.019
	JI Xiaofeng, YANG Chunli, HAO Jingjing, et al. Research prospects and hotspot comparison for emergency logistics in China and foreign countries[J]. China Safety Science Journal, 2021, 31(12): 144-152. doi: 10.16265/j.cnki.issn 1003-3033.2021.12.019
[8]	DING Rui, NORSIDAH U, HUSSAIN B H, et al. Application of complex networks theory in urban traffic network researches[J]. Networks and Spatial Economics, 2019, 19: 1281-1317. doi: 10.1007/s11067-019-09466-5
[9]	HUMAGAIN S, SINHA R, LAI E, et al. A systematic review of route optimisation and pre-emption methods for emergency vehicles[J]. Transport Reviews, 2020, 40(1): 35-53. doi: 10.1080/01441647.2019.1649319
[10]	HU Qian, YEO J, KAPUCU N. A systematic review of empirical emergency management network research: formation and development, properties, and performance[J]. The American Review of Public Administration, 2022, 52(4): 280-297. doi: 10.1177/02750740221077348
[11]	ABUSALAMA J, RAZALI S, CHOO Y H, et al. Dynamic real-time capacity constrained routing algorithm for evacuation planning problem[J]. Indonesian Journal of Electrical Engineering and Computer Science, 2020, 20(3): 1 388-1 396.
[12]	TANG Junqing, LIN Huali, FAN Xudong, et al. A topology-based evaluation of resilience on urban road networks against epidemic spread: implications for COVID-19 responses[J]. Frontiers in Public Health, 2022, 10: DOI:10.3389/fpubh.2022.1023176.
[13]	ZHENG Jijie, BAO Fuguang, SHEN Zhonghua, et al. RETRACTED ARTICLE: Data-driven dynamic adjustment and optimization model of emergency logistics network in public health[J]. Risk Management and Healthcare Policy, 2022, 12(15): 151-169.
[14]	PETCHROMPO S, COIT D W, BRINTRUP A, et al. A review of pareto pruning methods for multi-objective optimization[J]. Computers & Industrial Engineering, 2022, 167: DOI:10.1016/j.cie.2022.108022.
[15]	NATH H N, DEMPE S, DHAMALA T N. A bicriteria approach for saving a path maximizing dynamic contraflow[J]. Asia-Pacific Journal of Operational Research, 2022, 39(3): DOI:10.1142/S0217595921500275.
[16]	CHEN Min. Retracted Article: optimal path planning and data simulation of emergency material distribution based on improved neural network algorithm[J]. Soft Computing, 2023, 27(9): 5995-6005. doi: 10.1007/s00500-023-08073-4 pmid: 37041764
[17]	MAVROVOUNIOTIS M, ANASTASIADOU M N, HADJIMITSIS D. Measuring the performance of ant colony optimization algorithms for the dynamic traveling salesman problem[J]. Algorithms, 2023, 16(12): DOI:10.3390/a16120545.
[18]	JOSE C, VIJULAGRACE K S. Optimization based routing model for the dynamic path planning of emergency vehicles[J]. Evolutionary Intelligence, 2022, 15(2): 1425-1439. doi: 10.1007/s12065-020-00448-y
[19]	AMARAL R R, SEMANJSKI I, GAUTAMA S, et al. Exploring the issue of integrating logistics and traffic control in urban areas[J]. Transportation Planning and Technology, 2019, 42(6): 606-624. doi: 10.1080/03081060.2019.1622253
[20]	DE FRANCESCO C, PALAZZI C E, RONZANI D. Fast message broadcasting in vehicular networks: model analysis and performance evaluation[J]. IEEE Communications Letters, 2020, 24(8): 1669-1672. doi: 10.1109/COML.4234
[21]	KAYA Y, VENTURA C. Seismic structural health monitoring of bridges in British Columbia, Canada[J]. Seismic Structural Health Monitoring: From Theory to Successful Applications, 2019, 15(1): 31-49.
[22]	RENZI E, ZAMPINO S, PALERMO G, et al. An integrated risk management system for road infrastructures: focus on seismic risk and network performance[J]. Procedia Structural Integrity, 2023,44: 355-362.
[23]	DE SORTIS A, VESTRONI F, MARCHESINI S, et al. Analysis of the recorded response of a school building heavily damaged by the 2016 central Italy earthquake[J]. Buildings, 2022, 12(7): DOI:10.3390/buildings12070907.
[24]	XIA Dengyou, QIAN Xinming, DUAN Zaipeng. Scenario deduction model of unconventional emergency based on dynamic Bayesian network[J]. Journal of Northeastern University:Natural Science, 2015, 36(6): 897-902.
[25]	南锐, 肖叶静, 王静. 突发事件应急管理情景分析:宏观审视与微观解构[J]. 矿业科学学报, 2023, 8(2):265-276.
	NAN Rui, XIAO Yejing, WANG Jing. Scenario analysis of emergency management: macroscopic review and microscopic deconstruction[J]. Journal of Mining Science and Technology, 2023, 8(2): 265-276.
[26]	AZZAOUI N, KORICHI A, BRIK B, et al. Towards optimal dissemination of emergency messages in internet of vehicles: a dynamic clustering-based approach[J]. Electronics, 2021, 10(8): DOI:10.3390/electronics10080979.
[27]	YIN Qi. Design and application of smart city internet of things service platform based on fuzzy clustering algorithm[J]. Mobile Information Systems, 2022, 2022(1): DOI:10.1155/2022/8405306.
[28]	LIU Chaofeng, YIN He, SUN Yixin, et al. A grade identification method of critical node in urban road network based on multi-attribute evaluation correction[J]. Applied Sciences, 2022, 12(2): DOI:10.3390/app12020813.
[29]	张敏, 于剑. 基于划分的模糊聚类算法[J]. 软件学报, 2004, 15(6):858-868.
	ZHANG Min, YU Jian. Fuzzy partitional clustering algorithms[J]. Journal of Sofware, 2004, 15(6):858-869.
[30]	IKIDID A, EL FAZZIKI A, SADGAL M. A multi-agent framework for dynamic traffic management considering priority link[J]. International Journal of Communication Networks and Information Security, 2021, 13(2): 324-330.
[31]	KHAN A, AFTAB F, ZHANG Zhongshan. UAPM: an urgency-aware packet management for disaster management using flying ad-hoc networks[J]. China Communications, 2019, 16(11): 167-182.
[32]	HASSIJA V, CHAMOLA V, MAHAPATRA A, et al. Interpreting black-box models: a review on explainable artificial intelligence[J]. Cognitive Computation, 2024, 16(1): 45-74. doi: 10.1007/s12559-023-10179-8
[33]	KEARNEY S P, COOPS N C, SETHI S, et al. Maintaining accurate, current, rural road network data: an extraction and updating routine using RapidEye, participatory GIS and deep learning[J]. International Journal of Applied Earth Observation and Geoinformation, 2020, 87: DOI:10.1016/j.jag.2019.102031.
[34]	SUN Bin, GENG Renkang, SHEN Tao, et al. Dynamic emergency transit forecasting with IoT sequential data[J]. Mobile Networks and Applications, 2023, 28(6): 2015-2029. doi: 10.1007/s11036-022-02027-0
[35]	RAJAN K, KUMAR K S, KANNAPIRAN T, et al. Intelligent traffic management system for smart cities utilizing reinforcement learning algorithm[C]. 2022 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS). IEEE, 2022: 170-177.
[36]	ROLF B, JACKSON I, MÜLLER M, et al. A review on reinforcement learning algorithms and applications in supply chain management[J]. International Journal of Production Research, 2023, 61(20): 7151-7179. doi: 10.1080/00207543.2022.2140221
[37]	朱仕威, 叶宝林, 吴维敏. 基于深度学习的短时交通流预测方法综述与仿真研究[J]. 软件导刊, 2024, 23(2):182-193.
	ZHU Shiwei, YE Baolin, WU Weimin. Review and simulation of short-term traffic flow prediction methods based on deep learning[J]. Software Guide, 2024, 23(2):182-193.
[38]	CHEN Saifei, FU Hui, QIAO Yan, et al. Route choice behavior modeling for emergency evacuation and efficiency analysis based on type-II fuzzy theory[J]. IEEE Transactions on Intelligent Transportation Systems, 2021, 23(7): 6934-6949. doi: 10.1109/TITS.2021.3064085
[39]	程方明, 王琛琛, 袁晓芳. 安全发展视角下城市应急管理能力评估[J]. 中国安全科学学报, 2023, 33(5):158-167. doi: 10.16265/j.cnki.issn1003-3033.2023.05.2014
	CHENG Fangming, WANG Chenchen, YUAN Xiaofang. Evaluation of urban emergency management capabilities from perspective of safe development[J]. China Safety Science Journal, 2023, 33(5): 158-167. doi: 10.16265/j.cnki.issn1003-3033.2023.05.2014
[40]	CHEN Yingxin, LIU Xin, TADIKAMALLA P R, et al. Evolutionary game analysis for multi-level collaborative governance under public crisis in China: from a value perception perspective[J]. Risk Analysis, 2024, 44(3): 582-611. doi: 10.1111/risa.v44.3