不确定条件下的城市应急车辆调度模型研究

交通运输系统工程与信息 ›› 2014, Vol. 14 ›› Issue (1): 187-191.

不确定条件下的城市应急车辆调度模型研究

赵韩涛^*，毛宏燕²，黄瑞锦³

1. 哈尔滨工业大学（威海）汽车工程学院，山东威海 264209； 2. 哈尔滨工业大学（威海）图书馆，山东威海 264209；3. 哈尔滨工业大学交通科学与工程学院，哈尔滨 150090

收稿日期:2013-05-14 修回日期:2013-08-03 出版日期:2014-02-25 发布日期:2014-07-07
作者简介:赵韩涛（1978-），男，河南开封县人，讲师，博士.
基金资助:
国家自然科学基金资助（51308165）；哈尔滨工业大学科研创新基金资助（HIT.NSRIF.2014134）；山东省科技攻关项目 (2009GG20008020).

Model of Emergency Vehicle Scheduling under Uncertainty

ZHAO Han-tao¹, MAO Hong-yan², HUANG Rui-jin³

1.School of Automobile Engineering, Harbin Institute of Technology, Weihai 264209, Shandong, China; 2.Library, Harbin Institute of Technology, Weihai 264209, Shandong, China; 3.School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150090, China

Received:2013-05-14 Revised:2013-08-03 Online:2014-02-25 Published:2014-07-07

摘要/Abstract

摘要：

为了改善不确定条件下突发事件的响应效率，用数学规划的方法优化了城市应急车辆调度模型.针对现有调度数学模型存在着立足于当前事件而忽略防范未来事件的问题，建立了以带权重响应时间成本为目标函数的数学公式，并从时空限制的角度初步探讨了不同交通流量下的行程时间计算.案例研究结果表明：未来事件成真时，所建模型生成的调度策略减少了总体响应时间成本；置信水平的选取决定了系统对未来事件的重视程度，趋向于1时侧重防范未来严重事件，趋向于0时优先考虑当前事件.

关键词: 交通工程, 应急, 车辆调度, 不确定条件, 行程时间

Abstract:

To improve emergency response efficiency under uncertainty, the urban emergency vehicle scheduling model is optimized by the use of mathematical planning. Existing scheduling models are only built in accordance with current incidents while ignoring future incidents. Differently, to prevent future incidents, this paper establishes the mathematical formula which minimized the overall response time cost with weight as the objective function. From the perspective of time and space constraints, travel time calculation of emergency vehicle is discussed under different traffic flow. Case results show that, if the potential incidents really occur, the overall response time cost will be minimized by the scheduling policy. Furthermore, confidence level is selected to measure what extent the future incidents should be considered in response system. Meantime future serious incidents should be focused on prevention as confidence level tends to one, while current incidents would be responded with a priority as confidence level tends to zero.

Key words: traffic engineering, emergency, vehicle scheduling, uncertainty, travel time

中图分类号:

U491

赵韩涛，毛宏燕，黄瑞锦. 不确定条件下的城市应急车辆调度模型研究[J]. 交通运输系统工程与信息, 2014, 14(1): 187-191.

ZHAO Han-tao1, MAO Hong-yan2, HUANG Rui-jin3. Model of Emergency Vehicle Scheduling under Uncertainty[J]. Journal of Transportation Systems Engineering and Information Technology, 2014, 14(1): 187-191.

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链接本文: http://www.tseit.org.cn/CN/abstract/abstract18731.shtml

http://www.tseit.org.cn/CN/Y2014/V14/I1/187

参考文献

[1] Goldberg J B. Operations research models for the deployment of emergency services vehicles[J].EMS Management Journal,2004, 1 (1): 20-39.

[2] Lei H,Cheu R L, Aldouri R. Optimal allocation of emergency response service units to cover critical infrastructures with time dependent service demand and travel time[C]//88th Annual Meeting of the Transportation Research Board. Washington, DC: National Research Council, 2009:74-84.

[3] Budge S, Ingolfsson A, Zerom D. Empirical analysis of ambulance travel times: the case of calgary emergency medical services[J]. Management Science, 2010, 56(4):716-723.

[4] Huang Y, Fan Y, Cheu R L. Optimal allocation of multiple emergency service sources for critical transportation infrastructure protection[C]//TRB 86th Annual Meeting Compendium of Papers CD-ROM, Washington, DC: National Research Council, 2007: 0028.

[5] Pal R, Sinha K, Simulation model for evaluating and improving effectiveness of freeway service patrol programs[J]. Journal of Transportation Engineering, 2002, 128(4):355-365.

[6] Haghani A, Huijun Hu, Qiang Tian. An optimization model for real-time emergency vehicle dispatching and routing[C]//TRB 82nd Annual Meeting Compendium of Papers CD-ROM. Washington, DC: National Research Council, 2003:0710.

[7] Ozbay K，Weihua Xiao，Cem Iyigun. Probabilistic programming models for response vehicle dispatching and resource allocation in traffic incident management[C]//TRB 83rd Annual Meeting Compendium of Papers CD-ROM, Washington, DC: National Research Council, 2004:0014.

[8] 杨晓光，彭春露.救援车辆通行畅通可靠度模糊综合评价方法研究[J].土木工程学报，2007，40(1):79-84. [YANG X G, PENG C L. Travel reliability evaluation of emergency vehicles based on multilevel fuzzy synthesis judgment method[J]. China Clvil Engineering Journal, 2007, 40(1):79-84.]

[9] Gu Yong,Yan Xinping. A dispatch model of emergency vehicles based on stochastic travel time[C]//8th International Conference of Chinese Logistics and Transportation Professionals - Logistics: The Emerging Frontiers of Transportation and Development in China, Chengdu, 2008: 1565-1570.

[10] 杨孝宽,宫建,曹静.奥运会突发事件疏散路径动态路段行程时间[J].北京工业大学学报,2007,33(7):702-706.[YANG X K,GONG J, CAO J.Dynamic link travel time for evacuation route in an emergency of Olympic Games[J]. Journal of Beijing University of Technology, 2007, 33(7): 702-706.]

[11] 刘杨,郑黎黎,常云涛.城市道路应急车辆路段行程时间计算模型[J].交通与计算机, 2008,26(5):30-33.[LIU Y, ZHENG L L, CHANG Y T. Travel time model of emergency vehicles on urban road[J]. Computer and Communications, 2008, 26(5): 30-33.]

[12] 刘杨,沈海州,彭国雄.基于神经网络的应急车辆行程时间计算模型[J].武汉理工大学学报(交通科学与工程版),2007,31(6): 973-975. [LIU Y, SHEN H Z, PENG G X.Travel time model of emergency vehicle based on BP network[J].Journal of Wuhan University of Technology(Transportation Science & Engineering), 2007,31(6): 973-975.]

[13] 赵韩涛,王云鹏,王俊喜,等.高速公路应急车辆指挥调度优化模型[J].吉林大学学报(工学版), 2006, 36(3):336-339.[ZHAO H T, WANG Y P, WANG J X, et al. Study on optimization model for freeway emergency vehicle dispatching[J]. Journal of Jilin University(Engineering and Technology Edition),2006, 36(3):336-339.]

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