A connected driver advisory system framework for merging freight trains

• Published in: Transportation research. Part C, Emerging technologies Vol. 105; pp. 203 - 221
• Main Authors: Wang, Pengling, Goverde, Rob M.P., van Luipen, Jelle
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.08.2019
• Subjects: Driver advisory system; Freight train transport; Optimization; Train traffic prediction; Freight train transport; Driver advisory system; Train traffic prediction; Optimization
• DOI: 10.1016/j.trc.2019.05.043
• ISSN: 0968-090X

•We propose a Driver Advisory System framework for merging freight trains at junctions.•We specify basic requirements for merging Driver Advisory System functionalities.•We present a Connected-DAS architecture for merging freight trains.•We develop a merging window detection algorithm to facilitate smooth merging.•We provide a proof-of-concept by an application to a Dutch railway corridor. 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.