Prof. Weixiang Xu
Beijing Jiaotong University, China
Intelligent Transportation Systems
Transportation System Analysis and Integration
Transportation Management Modernization and Information Technology
Research on Maintenance of Rail Transit Switch Machine Based on Big Data
Turnout equipment, as a key component to ensure the safe operation of trains, is a key maintenance object for railway engineering and electrical services. Due to its own installation, external environment, and wear and tear, the equipment will malfunction or even fail to operate, posing a hidden danger to driving safety.
In this research, first, use big data technology to study the mechanical motion characteristics of turnout equipment, discover and extract the data characteristics of key parameters. Secondly, classify the mechanical characteristic data and explore its association with turnout failures and hidden dangers. Third, the machine learning method is used to analyze the similarity of the mechanical characteristic data when the switch moves, and the optimal threshold is determined to diagnose the fault. Fourth, realize the intelligent diagnosis of switch faults under the background of big data. Finally, the comprehensive electrical characteristic data is used for the judgment and early warning analysis of the turnout working condition, which provides support for the state repair and preventive repair of the turnout equipment.
Prof. Lei Yang
South China University of Technology, China
Supply Chain Management
Choice of technology for emission control in port areas: A supply chain perspective
Prof. Hui Liu
School of Traffic & Transportation Engineering, Central South University, China
Big Data on Transportation and Energy
Intelligent Carrier Robot
Big Data Prediction Methods and Applications in Smart Logistics and Intelligent Transportation Systems
The keynote talk will present the big data forecasting techniques for the key aspects (e.g., urban rail, traffic environment, traffic energy, etc.) of smart cities, and explores three key areas that can be studied using big data prediction in the smart cities, such as grid energy, intelligent train systems and environmental health. Some new time series processing and forecasting strategies are provided. The big data prediction methods and applications proposed in this talk can provide some useful references for the development of intelligent traffic systems and smart cities.
Prof. Qizhou Hu
College of Automation, Nanjing University of Science and Technology, China
Rail Traffic Safety Management
The Development Trend and Feasibility Study of Ultra-high-speed Railway
Railway is the first step in the strategy of a powerful country in transportation. As an important part of the transportation power, high-speed railway has become the mainstream development mode of the world’s transportation industry with its safe, reliable, fast, comfortable, and large-capacity transportation method. It have a certain impact on the country’s economy, society, and global competitiveness and the world has also entered the era of high-speed rail. However, with the advent of the post-high-speed rail era, the speed of high-speed rail no longer meets people's requirements, and people hope that faster railways will appear. Therefore, ultra-high-speed railway has become a research hotspot in various countries. Based on the interpretation of the development trend of ultra-high-speed railways, this report analyzes the feasibility of ultra-high-speed railways theoretically, and interprets the reliability and operability of ultra-high-speed railways technically.