Study on demand forecasting and allocation of expressway emergency vehicle resource

被引：0

作者：

Zhao, Jiandong ^{[1
]}

Chen, Xuzhe ^{[1
]}

Duan, Xiaohong ^{[1
]}

Shen, Tong ^{[2
]}

机构：

[1] School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University

[2] School of Electrical Engineering, Royal Institute of Technology, Stockholm

来源：

Journal of Computational Information Systems | 2014年 / 10卷 / 10期

关键词：

Demand forecasting; Emergency vehicle resource; Expressway; Resource allocation;

D O I：

10.12733/jcis10246

中图分类号：

学科分类号：

摘要：

Emergency vehicles are the key resources of expressway for the rescue mission to traffic accidents. For optimal emergency vehicle resources allocation, an improved Case-Based Reasoning (CBR) and an allocation model are established. First, the effecting factors of traffic safety on expressway are analyzed to establish an accident hazard indicator system. A difference coefficient is introduced to improve Case-Based Reasoning for predicting the demand of emergency vehicle resources. Then, the resource allocation model is established based on the objective constrained optimization method, which tries to find the minimum rescue time with constraints such as road hazard, rescue time, resource demand, and the configuration result is obtained by adopting Particle Swarm Optimization (PSO). Finally, according to the study of a bridge case, it shows that the prediction error of resource demands becomes smaller by using the improved case-based reasoning method and the resource allocation results are reasonable through the calculation of objective constrained optimization model. 1553-9105/Copyright © 2014 Binary Information Press.

引用

页码：4205 / 4215

页数：10

共 15 条

[1]

The road traffic accident statistics report of the people's republic of China, (2011)

[2]

Sullivan E.C., New model for predicting freeway incidents and incident delays, Journal of Transportation Engineering, 123, 4, pp. 267-275, (1997)

[3]

Holguin-Veras J., Jaller M., Immediate resource requirements after hurricane katrina, Natural Hazards Review, 13, 2, pp. 117-131, (2012)

[4]

Zhang X., Yang X., Zhan L., Study on fuzzy similarity inference based model for emergency material demand forecasting, Logistics Technology, 31, 5, pp. 229-231, (2012)

[5]

Tufekci S., Wallace W.A., The emerging area of emergency management and engineering, IEEE Transactions on Engineering Management, 45, pp. 103-111, (1998)

[6]

Iannoni A.P., Morabito R., Saydam C., Et al., An optimization approach for ambulance location and the districting of the response segments on highways, European Journal of Operational Research, 195, 2, pp. 528-542, (2009)

[7]

Yu Y., Yu H., Tian J., Study on simulation model for highway emergency service system, China Safety Science Journal, 16, 7, pp. 13-17, (2006)

[8]

Xu X., Ren Z., Wan J., Adaptive resource allocation policy in virtualized server based on non-cooperative game, Journal of Computational Information Systems, 8, 23, pp. 9637-9646, (2012)

[9]

Ren C., Wang T., Et al., An improved adaptive dynamic programming algorithm for cloud storage resource allocation, Journal of Computational Information Systems, 7, 15, pp. 5401-5408, (2011)

[10]

Saaty T.L., Decision marking with dependence and feedback: The analytic network process, (2011)

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