Closing the loop between data mining and fast decision support for intelligent train scheduling and traffic control
摘要：The existing Big Data of transport flows and railway operations can be mined through advanced statistical analysis and machine learning methods in order to describe and predict well the train speed, punctuality, track capacity and energy consumption. The accurate modelling of the real spatial and temporal distribution of line and network transport, traffic and performance stimulates a faster construction and implementation of robust and resilient timetables, as well as the development of efficient decision support tools for real-time rescheduling of train schedules. In combination with advanced train control and safety systems even (semi-.) automatic piloting of trains on main and regional railway lines will become feasible in near future.
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北京交通大學學報雜志
北京交通大學學報雜志， 雙月刊，本刊重視學術導向，堅持科學性、學術性、先進性、創新性，刊載內容涉及的欄目：數字經濟研究_數字經濟發展、創新與治理、應用經濟研究、管理研究、物流研究、馬克思主義研究、國家社會治理研究等。于1975年經新聞總署批準的正規刊物。
- 北大期刊
- CSCD期刊
- 統計源期刊
- 1-3個月審核
