Research on the optimal route choice based on improved Dijkstra

Wei, Mingjun; Meng, Yonggang

doi:10.1109/wartia.2014.6976257

Cited by 5 publications

(1 citation statement)

References 6 publications

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“…Bauer et al [1] introduced layering technology and target acceleration technology in the Dijkstra algorithm, which improved the data storage capacity of the road network and the efciency of the algorithm. Wei and Meng [2] optimized the dynamic path using the improved Dijkstra algorithm, a weight function was introduced to the improved algorithm, and the weight was determined according to the degree of trafc congestion. Te A * algorithm for the unbalanced search was proposed by Pijls and Post [3].…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic Path Optimization Based on Improved Ant Colony Algorithm

Cheng

2023

Journal of Advanced Transportation

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Dynamic path optimization is an important part of intelligent transportation systems (ITSs). Aiming at the shortcomings of the current dynamic path optimization method, the improved ant colony algorithm was used to optimize the dynamic path. Through the actual investigation and analysis, the influencing factors of the multiobjective planning model were determined. The ant colony algorithm was improved by using the analytic hierarchy process (AHP) to transform path length, travel time, and traffic flow into the comprehensive weight-influencing factor. Meanwhile, directional guidance and dynamic optimization were introduced to the improved ant colony algorithm. In the simulated road network, the length of the optimal path obtained by the improved ant colony algorithm in the simulation road network is 3.015, which is longer than the length of the optimal path obtained by the basic ant colony algorithm (2.902). The travel time of the optimal path obtained by the improved ant colony algorithm (376 s) is significantly shorter than that of the basic ant colony algorithm (416.3 s). The number of iterations of the improved ant colony algorithm (45) is less than that of the basic ant colony algorithm (58). In the instance network, the number of iterations of the improved ant colony algorithm (18) is less than that of the basic ant colony algorithm (26). The travel time of the optimal path obtained by the improved ant colony algorithm (377.1 s) is significantly shorter than that of the basic ant colony algorithm (426 s) and the spatial shortest distance algorithm (424 s). Compared with the basic ant colony algorithm and the spatial shortest distance algorithm, the results of the optimal path obtained by the improved ant colony algorithm were more accurate, and the effectiveness of the improved ant colony algorithm was verified.

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Section: Literature Reviewmentioning

confidence: 99%

Dynamic Path Optimization Based on Improved Ant Colony Algorithm

Cheng

2023

Journal of Advanced Transportation

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Real-time safest route identification: Examining the trade-off between safest and fastest routes

Ghoul

Sayed

2023

Analytic Methods in Accident Research

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A Survey on Enhancing Wire/Wireless Routing Protocol Using Machine Learning Algorithms

Yas

Hashem

2020

IOP Conf. Ser.: Mater. Sci. Eng.

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Routing is a technique used for choosing the best network’s path and forwarding the data over the selected path. This paper investigates the enhanced routing algorithms for wire/wireless networks through making a deep study of the most new routing algorithms in those networks, then analyzing these algorithms to examine the efficiency and effectiveness of the analyzed algorithms. Moreover, the paper deals with OSPF for the wired network and AODV for the wireless network. The emphasis of this research paper is concentrated on the survey in routing algorithms that used in wired and wireless networks such as OSPF and AODV because such algorithms are the best suitable kinds for the two types of Networks. The next subsection describes the basic features of these protocols. This paper also focuses on the common points in wire/wireless routing algorithms and using machine learning techniques for enhancements and improvements.

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Research on the optimal route choice based on improved Dijkstra

Cited by 5 publications

References 6 publications

Dynamic Path Optimization Based on Improved Ant Colony Algorithm

Dynamic Path Optimization Based on Improved Ant Colony Algorithm

Real-time safest route identification: Examining the trade-off between safest and fastest routes

A Survey on Enhancing Wire/Wireless Routing Protocol Using Machine Learning Algorithms

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