An evaluation method of bus operation efficiency: a case study in Qingdao

Liye Zhang; Jukong Li; Yanyan Sun; Hao Shi; Ying Wei

doi:10.1117/12.3031005

19 July 2024 An evaluation method of bus operation efficiency: a case study in Qingdao

Liye Zhang, Jukong Li, Yanyan Sun, Hao Shi, Ying Wei

Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 131814I (2024) https://doi.org/10.1117/12.3031005
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China

Abstract

Intelligent Transportation System (ITS) is an essential part of a smart city, which utilizes advanced technologies such as the Internet of Things (IoT), big data analytics and artificial intelligence to optimize the operation of the city's transportation system. Public transport plays an important role in intelligent transport. In this paper, we first start from the IC card and GPS data source, preprocess the key information and fields of the data, and combine the GPS data and OD derivation algorithm to derive the station passenger boarding and alighting site, and get the result of the passenger flow under the fusion processing of public transport data; Combined with the reliability indices of each trip derived using historical data, the entropy-based method bus route operational efficiency evaluation model was constructed. This paper makes extensive use of real data, which enhances the validity of the evaluation findings to some extent and supplements and enhances some of the theoretical approaches used to assess the operational effectiveness of bus lines.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Liye Zhang, Jukong Li, Yanyan Sun, Hao Shi, and Ying Wei "An evaluation method of bus operation efficiency: a case study in Qingdao", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 131814I (19 July 2024); https://doi.org/10.1117/12.3031005

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