Autonomous characteristic analysis of public transport network using a supply-demand matrix approach

Yao Xiao; Jun Xu

doi:10.1117/12.2637033

8 May 2022 Autonomous characteristic analysis of public transport network using a supply-demand matrix approach

Yao Xiao, Jun Xu

Proceedings Volume 12249, 2nd International Conference on Internet of Things and Smart City (IoTSC 2022）; 122491R (2022) https://doi.org/10.1117/12.2637033
Event: 2022 2nd International Conference on Internet of Things and Smart City (IoTSC 2022）, 2022, Xiamen, China

Abstract

The investigation of public transport network (PTN) characteristics is significant for optimizing public transport planning and alleviating traffic congestion. Most of the current researches consider the travel demand from the IC card data or the cellular signaling data (CSD), but a systematic and quantitative comparison between the two types of demands is still lacking. In this paper, a supply-demand matrix approach based on multi-source data is proposed. The supply matrix of PTN is calculated based on the bus GPS data, and the demand matrices are established using the IC card data and CSD, respectively. A practical PTN containing 116 bus lines and 997 stations in the Sanshui district of Foshan is analyzed using the multi-source data for 21 workdays in November 2020. The Sanshui district is divided into 72 regions, and the matrices are matched to them to compare the demand and supply. The characteristics of PTN can be autonomously revealed using the average Euclidean distance and Cosine similarity. The supply and demand imbalances in several regions are autonomously discovered with the analysis results, and the CSD can particularly benefit the discovery of potential travel demand. We believe the proposed approach can provide further directions and suggestions for optimizing PTN.

Citation Download Citation

Yao Xiao and Jun Xu "Autonomous characteristic analysis of public transport network using a supply-demand matrix approach", Proc. SPIE 12249, 2nd International Conference on Internet of Things and Smart City (IoTSC 2022）, 122491R (8 May 2022); https://doi.org/10.1117/12.2637033

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