从数据挖掘到智能调度决策支持: 存在问题与实施路径

Journal article (2019)

Authors

I.A. Hansen Transport and Planning - CEG
Research Group
Transport and Planning (CEG) (TU Delft)
Copyright: © 2019 I.A. Hansen
DOI: https://doi.org/10.11860/j.issn.1673-0291.2019.01.003
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Published Date

2019

Language

Chinese

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Faculty

Civil Engineering & Geosciences

Department

Transport and Planning

Research Group

Transport and Planning

Abstract

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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