Optimization of Urban Bus Stops Setting Based on Data Mining,International Journal of Pattern Recognition and Artificial Intelligence

当前位置： X-MOL 学术 › Int. J. Pattern Recognit. Artif. Intell. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Optimization of Urban Bus Stops Setting Based on Data Mining
International Journal of Pattern Recognition and Artificial Intelligence ( IF 0.9 ) Pub Date : 2021-03-22 , DOI: 10.1142/s021800142159028x
Ganglong Duan ₁ , Xin Ma ₁ , Jianren Wang ₁ , Zhishi Wang ₁ , Yan Wang ₁

Affiliation

The unreasonable setting of urban bus stops is a common problem in real life, which seriously affects people’s happiness, sense of belonging and brand in the city. However, the existing related research on the above problems generally has the defects of high technical complexity and high cost. Therefore, we aim to propose a way to optimize the setting of urban public transportation stations and reduce the technical complexity and high cost of existing public transportation station optimization by using artificial intelligence algorithms. First, we extract and integrate bus GPS data and bus card swipe data in the business system and perform exploratory analysis on the pre-processed data. Second, the original k-NN algorithm is improved, and an ik-NN algorithm is proposed to determine the cardholder’s boarding point. Then, we divide the upstream and downstream lines to calculate the total number of upstream and downstream passengers. Third, we propose an algorithm for calculating the number of passengers getting off at bus stations and calculating the number of passengers getting on and off at each bus station. Finally, according to the number of passengers getting on and off at each bus station, the OD matrix is constructed, the residents’ travel rules are analyzed, and optimization suggestions for the setting of urban bus stations are proposed. This paper selects the public transit GPS data set and swipe card data set of Shenzhen, China for experiments. The experimental results show that: (1) Compared with K-means, the ik-NN algorithm we proposed can effectively determine the actual car station of each cardholder, and the algorithm is less sensitive to feature dimensions. At the same time, the ik-NN algorithm has a high operating efficiency and is less affected by the “k” value. (2) The calculation algorithm for the number of passengers getting off at bus stations can effectively use the existing data of the business system to determine the number of passengers getting off at each bus station. Therefore, the calculation times of this algorithm are low, and the accuracy is high. (3) The optimization suggestions for bus stations based on the OD matrix analysis of residents’ travel rules meet the needs of urban development and have certain reference value.

更新日期：2021-03-22

点击分享查看原文

点击收藏

阅读更多本刊最新论文本刊介绍/投稿指南11