Reliability analysis of dynamic travel time of public transport based on Advanced Public Transportation System data

Jun Chen; Chun Wang; Gaoqiang Wei; Xiaowei Li

doi:10.1117/12.2613935

10 November 2021 Reliability analysis of dynamic travel time of public transport based on Advanced Public Transportation System data

Jun Chen, Chun Wang, Gaoqiang Wei, Xiaowei Li

Author Affiliations +

Proceedings Volume 12050, International Conference on Smart Transportation and City Engineering 2021; 120501X (2021) https://doi.org/10.1117/12.2613935
Event: 2021 International Conference on Smart Transportation and City Engineering, 2021, Chongqing, China

Abstract

Studying the dynamic reliability of urban public transport travel can accurately describe the dynamics, randomness, temporal and spatial characteristics, and supply demand relationship of the public transport network, which has important practical significance for improving the planning and management of the public transport system and guiding passengers to travel. Although the research on reliability started earlier, there is no unified standard in reliability calculation. In addition, the study of travel time reliability generally assumes normal distribution, which reduces the computational accuracy and model applicability of travel time reliability. Therefore, this paper first proposes a set of methods to extract OD travel time in the APTS data environment, and then selects five distribution models with different forms to carry out probability distribution fitting of travel time. Finally, based on the dynamic reliability analysis of OD pair, a set of reliability analysis indexes is proposed to analyze the temporal and spatial characteristics of OD travel time.

Citation Download Citation

Jun Chen, Chun Wang, Gaoqiang Wei, and Xiaowei Li "Reliability analysis of dynamic travel time of public transport based on Advanced Public Transportation System data", Proc. SPIE 12050, International Conference on Smart Transportation and City Engineering 2021, 120501X (10 November 2021); https://doi.org/10.1117/12.2613935

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