Li, Qing (Beijing National Railway Research & Design Institute of Signal &), Liu, Ling (Beijing National Railway Research& Design Institute of Signal &C), Liu, Gehui (Beijing National Railway Research & Design Institute of Single &), Liu, Jun (Beijing National Railway Research & Design Institute of Signal &), zhang, wanqiu (Beijing National Railway Research& Design Institute of Signal &C), Bai, Lei (Beijing Jiuzhouyigui Environmental Technology Co.,ltd.)

Graphical Visualization Model for Big Data to Assess Safety in the Regional Rail Transit System*

Scheduled for presentation during the Poster Session "Railway systems and applications" (ThBT13), Thursday, September 26, 2024, 14:30−16:30, Foyer

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on December 26, 2024

Abstract

Accurately quantifying and comprehensively assessing the safety status of regional rail transit systems is critical to management decision making. These processes allow managers to understand the variations in safety status during multimodal rail transit operations and to effectively allocate transportation capacity and maintenance resources. In this study, a Big Data graphical visualization model was presented for assessing the safety of these systems that is tailored to the multi-level assessment elements of nodes, lines, and the network within regional rail systems. It uses a multidimensional scaling analysis algorithm and a hybrid hierarchical K-means clustering algorithm to visualize the distribution of safety status and provides an intuitive representation of the similarities or differences of each state feature. The assessment results minimize subjective bias and accurately reflect the actual conditions. The efficiency of the model is demonstrated using real-world operational data from typical urban agglomerations in China.