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Shortest path selection approach based on dynamic traffic simulation models

YU Yan-fang,LU Jun,

计算机应用研究 , 2010,
Abstract: Constructed dynamic traffic simulation system by using the dynamic traffic model INTEGRATION.Tackled the shortest path selection with dynamic traffic information by the component-based ant colony optimization.The simulation example suggests that the shortest path selection approach based on dynamic traffic simulation models is feasible,correct and effective.This proposed approach is very easy to understand and use,it has the robust reusage and expansibility;indeed provide an excellent framework that can continually improve for solving different optimization problems.
Dynamic traffic model under emergency incident and bidirectional dynamic shortest path algorithm

REN Zi-hui,WANG Jian,

计算机应用 , 2008,
Abstract: A macroscopic dynamic traffic model for emergent incidents was presented with consideration of the number of the driveway, on-ramps and off-ramps and guidance instruction based on model METANET. And a bidirectional dynamic shortest path guidance algorithm for dealing with the emergent incidents timely and efficiently was also proposed. The weight altered dynamically with the change of the freeway and traffic jam. The shortest path from the two directions, which were timely and dynamically to strive for time in the processing of dealing with emergent incidents and succor, was searched. The simulation results show that the algorithm is feasible and effective and the efficiency of this algorithm has been improved.
Constrained shortest path problem in stochastic traffic network based on reliability

- , 2017, DOI: 10.3969/j.issn.1001-0505.2017.06.028
Abstract: 为了仿真交通网络中资源约束条件下的路径选择行为,建立了随机交通网络约束最优路径问题数学模型并进行求解.采用期望-方差为路径目标函数,将约束最优路径问题建模为混合非线性整数约束优化问题,构造基于线性规划的分支定界算法以求解该问题.针对Sioux Falls网络展开数值试验,将无资源约束和不同资源约束条件下的交通网络最优路径计算结果进行比较分析.试验结果表明:无资源约束和有资源约束条件下交通网络中相同起迄点之间的最优值和最优路径是不同的;在不同资源上限的约束条件下,相同起迄点之间的最优值和最优路径也是不同的,约束上限值与最优值成反比例关系.交通网络中资源约束条件对最优路径的选择具有重大影响.
To simulate the behavior of the path choice under the resource constraints in the traffic network, the mathematical model of the constrained shortest path problem in the stochastic traffic network is established and solved. The mean-variance is defined as the objective function of the path. The constrained shortest path problem is modeled as a nonlinear mixed integer constrained optimization problem and solved by the proposed branch-and-bound algorithm based on linear programming. Numerical experiments in the Sioux Falls network are carried out, and the calculation results of the constrained shortest path without resource constraint and with different resource constraints are compared and analyzed. The experimental results show that the optimal values and the shortest paths obtained without resource constraints and with resource constraints are different. The optimal values and the shortest paths obtained with different resource constraints are also different, and the upper value of the resource constraints is in inverse proportion to the optimal value. The resource constraints have a great influence on the choice of the optimal path in the traffic network
International Journal of Electrical, Electronics and Data Communication , 2013,
Abstract: - Shortest path routing is very effective as it saves time and remains economically beneficial in terms of cost. One of the most important characteristics in federated cloud-based wireless sensor networks is the topology dynamics, that is, the network topology changes over time due to energy conservation and node mobility. The cloud server considered as the final destination node can change over time along with the path towards it. In recent years, the routing problem has been well addressed using intelligent optimization techniques, e.g., Artificial Neural Networks (ANNs), Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), etc. In this paper we compare the effectiveness of these existing algorithms on various wireless sensor networks and build up a novel hybrid algorithm suitable for federated cloud-based environment. Finally, an implementation using clouds like Pachube, ThingSpeak, and Amazon EC2 constitutes the very future extension of this research work
Neural Networks for Dynamic Shortest Path Routing Problems - A Survey  [PDF]
R. Nallusamy,K. Duraiswamy
Computer Science , 2009,
Abstract: This paper reviews the overview of the dynamic shortest path routing problem and the various neural networks to solve it. Different shortest path optimization problems can be solved by using various neural networks algorithms. The routing in packet switched multi-hop networks can be described as a classical combinatorial optimization problem i.e. a shortest path routing problem in graphs. The survey shows that the neural networks are the best candidates for the optimization of dynamic shortest path routing problems due to their fastness in computation comparing to other softcomputing and metaheuristics algorithms
Shortest Alternate Path Discovery through Recursive Bounding Box Pruning  [PDF]
Rajendra S. Parmar, Bhushan H. Trivedi
Journal of Transportation Technologies (JTTs) , 2017, DOI: 10.4236/jtts.2017.72012
Abstract: Congestion is a dynamic phenomenon and hence efficiently computing alternate shortest route can only help expedite decongestion. This research is aimed to efficiently compute shortest path for road traffic network so that congestion can be eased resulting in reduced CO2 emission and improved economy. Congestion detection is achieved after evaluating road capacity and road occupancy. Congestion index, a ratio of road occupancy to road capacity is computed, congestion index higher than 0.6 necessitates computation of alternate shortest route. Various algorithms offer shortest alternate route. The paper discusses minimization of graph based by removing redundant nodes which don’t play a role in computation of shortest path. The proposal is based on continuous definition of a bounding box every time a next neighboring node is considered. This reduces maximum number of contentious nodes repeatedly and optimizes the network. The algorithm is deployed from both the ends sequentially to ensure zero error and validate the shortest path discovery. While discovering shortest path, the algorithm also offers an array of shortest path in ascending order of the path length. However, vehicular traffic exhibits network duality viz. static and dynamic network graphs. Shortest route for static distance graph is pre-computed and stored for look-up, alternate shortest path based on assignment of congestion levels to edge weights is triggered by congestion index. The research also supports directed graphs to address traffic rules for lanes having unidirectional and bidirectional traffic.
On the Maximal Shortest Path in a Connected Component in V2V  [PDF]
Michel Marot,Adel Mounir Sa?d,Hossam Afifi
Computer Science , 2015, DOI: 10.1016/j.peva.2015.09.003
Abstract: In this work, a VANET (Vehicular Ad-hoc NETwork) is considered to operate on a simple lane, without infrastructure. The arrivals of vehicles are assumed to be general with any traffic and speed assumptions. The vehicles communicate through the shortest path. In this paper, we study the probability distribution of the number of hops on the maximal shortest path in a connected component of vehicles. The general formulation is given for any assumption of road traffic. Then, it is applied to calculate the z-transform of this distribution for medium and dense networks in the Poisson case. Our model is validated with the Madrid road traces of the Universitat Polit\`ecnica de Catalunya. These results may be useful for example when evaluating diffusion protocols through the shortest path in a VANET, where not only the mean but also the other moments are needed to derive accurate results.
Multi-Objective PSO- and NPSO-based Algorithms for Robot Path Planning
Advances in Electrical and Computer Engineering , 2010, DOI: 10.4316/aece.2010.04011
Abstract: In this paper two novel Particle Swarm Optimization (PSO)-based algorithms are presented for robot path planning with respect to two objectives, the shortest and smoothest path criteria. The first algorithm is a hybrid of the PSO and the Probabilistic Roadmap (PRM) methods, in which the PSO serves as the global planner whereas the PRM performs the local planning task. The second algorithm is a combination of the New or Negative PSO (NPSO) and the PRM methods. Contrary to the basic PSO in which the best position of all particles up to the current iteration is used as a guide, the NPSO determines the most promising direction based on the negative of the worst particle position. The two objective functions are incorporated in the PSO equations, and the PSO and PRM are combined by adding good PSO particles as auxiliary nodes to the random nodes generated by the PRM. Both the PSO+PRM and NPSO+PRM algorithms are compared with the pure PRM method in path length and runtime. The results showed that the NPSO has a slight advantage over the PSO, and the generated paths are shorter and smoother than those of the PRM and are calculated in less time.
Generalized Shortest Path Kernel on Graphs  [PDF]
Linus Hermansson,Fredrik D. Johansson,Osamu Watanabe
Computer Science , 2015,
Abstract: We consider the problem of classifying graphs using graph kernels. We define a new graph kernel, called the generalized shortest path kernel, based on the number and length of shortest paths between nodes. For our example classification problem, we consider the task of classifying random graphs from two well-known families, by the number of clusters they contain. We verify empirically that the generalized shortest path kernel outperforms the original shortest path kernel on a number of datasets. We give a theoretical analysis for explaining our experimental results. In particular, we estimate distributions of the expected feature vectors for the shortest path kernel and the generalized shortest path kernel, and we show some evidence explaining why our graph kernel outperforms the shortest path kernel for our graph classification problem.
Shortest path algorithm under dynamic road network

SONG Xiao-yu,YU Lan-yang,SUN Huan-liang,

计算机应用研究 , 2009,
Abstract: Since the speed of road changes with the variation of traffic, it is necessary to monitor the flow of traffic.This paper established the speed model database to update the speed model of each road. Based on A* algorithm and speed model database,proposed a shortest path algorithm under dynamic road network. The experiments on real datasets show that this method can be used to find the shortest path in dynamic road network, and it makes the shortest path queries more efficiently and more accurately.
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