A connected driver advisory system framework for merging freight trains
Impact Factor:8.089
DOI number:10.1016/j.trc.2019.05.043
Affiliation of Author(s):Department of Transport and Planning, Delft University Technology, Delft, the Netherlands
Teaching and Research Group:IVT-Institute for Transport Planning and Systems,
Journal:Transportation Research Part C: Emerging Technologies
Place of Publication:England
Key Words:Freight train transport Driver advisory system Train traffic prediction Optimization
Abstract:This paper proposes an approach to facilitate smooth merging of freight trains into a stream of passenger trains with short headways, to help drivers better control freight trains and avoid red signals. An algorithm architecture is proposed for Driver Advisory Systems (DASs) to compute time/speed advice for freight train drivers. The framework includes four parts: buffer stairway prediction, freight train movement prediction, merging window detection and merging optimization. The basic idea is to predict the traffic state in the merging area regularly and find the feasible merging time window. Proper advice can be presented to freight train drivers and help them to merge smoothly, by comparing the freight train movement to the feasible merging window. The performance of the proposed algorithms is illustrated on examples of merging freight trains in the Meteren and Kijfhoek areas on the Dutch railway network. The experimental results show the efficiency and quality of the proposed algorithms on real world size problems.
Indexed by:Article
Document Code:S0968090X18315778
Discipline:Engineering
Document Type:J
Volume:105
Issue:Aug.
Page Number:203-221
Number of Words:12096
ISSN No.:0968-090X
Translation or Not:no
Date of Publication:2019-06-07
Included Journals:SCI、EI
Links to published journals:https://www.sciencedirect.com/science/article/pii/S0968090X18315778