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Home > Archives > Volume 16, No 6 (2018) > Article

DOI: 10.14704/nq.2018.16.6.1578

Application in Dynamic Path Selection for Emergency Vehicles Based on Hybrid Cuckoo Algorithm Optimizing Neural Network Model

Feng Yang, Chunming Ye, Jianfeng Ren


If any emergency occurs in a city, emergency vehicle scheduling must be such as to shorten the path travel time. The biggest difficulty in scheduling is how to measure the real-time changes in the traffic conditions of urban road network. To study the dynamic path selection of emergency vehicles under city emergency, this paper abstracts the urban road network into a map composed of nodes and edges, and takes the shortest path as the optimization objective. Firstly, the author takes the K-nearest sample set from the similar historical sample sets, predicts the real-time vehicle speed and establishes the path travel time function. Then, the author uses path reliability to measure the impacts of real-time traffic conditions on the overall travel time and constructs the two-stage objective optimization model for dynamic optimal path selection. Finally, based on this model, the author proposes a hybrid cuckoo search algorithm and uses it to optimize the weights and thresholds of neural network model to solve the K shortest-time paths in the dynamic road network, and take a partial road network in Yangpu District of Shanghai as an example for simulation test. The test results show that the proposed dynamic path selection model can reflect the actual scenario of emergency vehicle scheduling under emergency, and that the neural network model based on the hybrid cuckoo search algorithm is used to train weights and thresholds, so that the algorithm has a fast convergence speed and can solve the problem well. Compared with the classic cuckoo search algorithm and the particle swarm optimization algorithm, this algorithm has better performance.


Emergency Vehicles, Dynamic Path, Hybrid Cuckoo Search Algorithm, Neural Network, FIFO Network

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Duan X, Song S, Zhao J. Emergency vehicle dispatching and redistribution in highway network based on bilevel programming. Mathematical Problems in Engineering 2015; 2015(1923): 1-12.

Feng LY, Yuan LW, Luo W. Geometric algebra-based algorithm for solving nodes constrained shortest path. Acta Electronica Sinica 2014; 42(5): 846-51.

Figliozzi MA. A route improvement algorithm for the vehicle routing problem with time dependent travel times//Proceeding of the 88th Transportation Research Board Annual Meeting CD ROM, 2009.

Fu Y, Zhu LJ, Han HG. Analysis of K Shortest paths algorithms and applications. Technology Intelligence Engineering 2015; 1(1): 112-19.

Guerrero M, Castillo O, García M. Cuckoo search via lévy flights and a comparison with genetic algorithms//Fuzzy Logic Augmentation of Nature-Inspired Optimization Metaheuristics. Springer, Cham 2015: 91-103.

Kanagaraj G, Ponnambalam SG, Jawahar N. An effective hybrid cuckoo search and genetic algorithm for constrained engineering design optimization. Engineering Optimization2014; 46(10): 1331-51.

Li X, Yin M. Modified cuckoo search algorithm with self adaptive parameter method. Information Sciences 2015; 298: 80-97.

Nanda SJ, Panda G. A survey on nature inspired metaheuristic algorithms for partitional clustering. Swarm and Evolutionary Computation 2014; 16: 1-18.

Nasa-ngium P, Sunat K, Chiewchanwattana S. Enhancing modified cuckoo search by using Mantegna Lévy flights and chaotic sequences//Computer Science and Software Engineering (JCSSE), 2013 10th International Joint Conference on. IEEE 2013: 53-57.

Ozbay K, Iyigun C, Baykal-Gursoy M. Probabilistic programming models for traffic incident management operations planning. Annals of Operations Research 2013; 203(1): 389-406.

Pinto ARF, Crepaldi AF, Nagano MS. A Genetic Algorithm applied to pick sequencing for billing. Journal of Intelligent Manufacturing 2015: 1-18.

Ran B, Boyce D. Dynamic urban transportation network models: theory and implications for intelligent vehicle-highway systems. Springer Science & Business Media 2012.

Santillan JH, Tapucar S, Manliguez C. Cuckoo search via Lévy flights for the capacitated vehicle routing problem. Journal of Industrial Engineering International 2017:1-12.

Sharma H, Bansal J C, Arya KV. Lévy flight artificial bee colony algorithm. International Journal of Systems Science2016; 47(11): 2652-70.

Xu SH, Han CF, Meng LP. Research on adaptive of emergency management organization system based on NK model. Systems Engineering-Theory & Practice 2017;37(6): 1619-29.

Xu T, Ding XL, Li JF. Review on K shortest paths algorithms. Computer engineering and design 2013; 34(11): 3900-06.

Yang JM, Ma MM, Che HJ. Multi-objective adaptive chaotic particle swarm optimization algorithm. Control and Decision 2015; 30(12): 2168-74.

Yang XF Stochastic scenario-based two-stage model and algorithm for the least expected time shortest path. Beijing Jiaotong University, 2013.

Zhang J, Zheng J. Capacitated vehicle routing problem using a novel hybrid swarm optimization approach. Academic Journal of Manufacturing Engineering 2016;14(1):90-95.

Zhang L, Tang Y, Hua C. A new particle swarm optimization algorithm with adaptive inertia weight based on Bayesian techniques. Applied Soft Computing 2015;28: 138-49.

Zhao HY, Wang JD, Liu SL. Compound Fault Diagnosis Technique Based on Artificial Neural Network and Support Vector Machine. Fluid Machinery 2008;36(1): 39-63.