Article Details

Abstract

With the rapid increase in urban population and the number of motor vehicles, the problem of traffic congestion has become increasingly prominent. This not only seriously affects the daily travel efficiency and quality of life of residents, but also increases commuting costs and energy consumption. In order to address this challenge, governments at all levels and urban planners are seeking solutions and striving to alleviate congestion through scientific planning and management. Against this backdrop, a research proposes an algorithm for urban traffic smooth engineering planning. This approach abandons complex algorithms such as disjoint-set data structures. By constructing an urban traffic network model and innovatively introducing a two-stage sorting mechanism, it streamlines the operation process of the Kruskal algorithm, thus achieving the optimal layout of the traffic network. The experimental results show that this algorithm is not only simple and easy to understand, but also has good stability and robustness. It provides strong support for enhancing the overall efficiency of the transportation system and becomes an important part of the municipal planning of governments at all levels.

Keywords

urban transportation; Minimum Spanning Tree; Kruskal algorithm; C language algorithm

References

1. [1] Pan Liyan, Zhang Dacheng Application of Improved Floyd Algorithm in Urban Transportation Network Optimization [J]. Logistics Technology, 2018, 37 (11): 71-74+115
2. [2] Ren Hengni, Liu Yanrong, Miao Rong Research on Intelligent Algorithm Optimization and Modeling of Urban Transportation Network [J]. Information Technology, 2022 (7): 70-74+80
3. [3] Chen Lerui, Pan Qiuping, Kong Jinsheng Optimization of urban transportation network based on edge deletion and expansion strategy [J]. Computer System Application, 2016, 25 (8): 145-148
4. [4] Liang Gaoming Optimization and Application of A * Algorithm for Urban Transportation Network [J]. Computing Technology and Automation, 2012, 31 (2): 86-89
5. [5] Song Liuyong, Wang Rui, Zhou Yongwang, etc Research and Algorithm Design of Dynamic Urban Transportation Network Optimization Model [J]. Surveying and Mapping Science, 2011, 36 (1): 134-136
6. [6] Chen Xiaoming, Li Yinzhen, Shen Qiang, etc Collaborative optimization method for urban transportation network based on dual layer complex network [J]. Computer Applications, 2019, 39 (10): 3079-3087
7. [7] Lin Xue, Zhang Zhijia, Liu Liqiang, etc VGG network pruning and quantification for urban traffic sign recognition [J]. Small Microcomputer System, 2021, 42 (6): 1293-1296
8. [8] Chen Yanqin Research on Optimization Method of Multi time Dynamic Traffic Restriction Scheme in Urban Transportation Network [D]. Changsha: Central South University, 2022
9. [9] Shi Baoming, He Yuanxiang, Ma Shaobin Research and Design of Improved Maze Map Generation Algorithm for Parallel Search Set [J]. Journal of Changchun Normal University, 2022, 41 (4): 51-55
10. [10] Yan Weimin, Wu Weimin Data Structures (C Language Version) [M]. Beijing: Tsinghua University Press, 2018

About the authors

Mao Rui (2004-), male, Han ethnicity, born in Nanjing, Jiangsu, is currently pursuing a bachelor's degree. His research interests include computer simulation, deep learning, and machine learning; Zhang Yuan (1984-), male, Han ethnicity, from Huanggang, Hubei, professor, doctoral student, research direction: cybersecurity.