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(xué)學(xué)報(bào)雜志
北京交通大學(xué)學(xué)報(bào)雜志， 雙月刊，本刊重視學(xué)術(shù)導(dǎo)向，堅(jiān)持科學(xué)性、學(xué)術(shù)性、先進(jìn)性、創(chuàng)新性，刊載內(nèi)容涉及的欄目：數(shù)字經(jīng)濟(jì)研究_數(shù)字經(jīng)濟(jì)發(fā)展、創(chuàng)新與治理、應(yīng)用經(jīng)濟(jì)研究、管理研究、物流研究、馬克思主義研究、國(guó)家社會(huì)治理研究等。于1975年經(jīng)新聞總署批準(zhǔn)的正規(guī)刊物。
- 北大期刊
- CSCD期刊
- 統(tǒng)計(jì)源期刊
- 1-3個(gè)月審核
