Online Data-Driven Adaptive Prediction of Train Event Times

Kecman, Pavle; Goverde, Rob M.P.

doi:10.1109/tits.2014.2347136

Cited by 77 publications

(24 citation statements)

References 11 publications

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“…The train traffic is modelled as an event graph for events associated to trains that share tracks with the freight train. The model is an adapted version of Kecman and Goverde (2015a). In the event graph, each event is modelled by a node.…”

Section: Traffic Model Constructionmentioning

confidence: 99%

“…The weights of train arcs are equal to the running (and dwell) times in the block sections. Kecman and Goverde (2015a) use the TROTS data, combined with the real-time monitored traffic state information, to estimate the running/dwell times. The running/dwell time estimation is based on the method presented in Kecman and Goverde (2015b) and introduced in Section 3.3.2.…”

Section: Traffic Model Constructionmentioning

confidence: 99%

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A coupled flood-agent-institution modelling (CLAIM) framework for urban flood risk management

Abebe

Ghorbani

Nikolič

et al. 2019

Environmental Modelling & Software

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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. In the Amsterdam Westhaven area, a concept for advising freight trains departing from standstill is now accepted using the

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Section: Traffic Model Constructionmentioning

confidence: 99%

Section: Traffic Model Constructionmentioning

confidence: 99%

A coupled flood-agent-institution modelling (CLAIM) framework for urban flood risk management

Abebe

Ghorbani

Nikolič

et al. 2019

Environmental Modelling & Software

View full text Add to dashboard Cite

show abstract

“…The main disadvantages is, of course, that it is difficult—if not impossible—to disentangle physical processes from human or case-dependent influence, specifically those processes that are robust (in many cases the same), rather than incidental (unique per case). Examples of data-driven studies in railways literature have mostly been performed at the micro-scale, e.g., the statistical estimation of specific train activities like running and dwell times [33–36]. Machine learning techniques like support vector machines are use to predict train arrival times from data in Serbia [37] and Italy [38].…”

Section: Introductionmentioning

confidence: 99%

Predicting transitions across macroscopic states for railway systems

et al. 2019

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Railways are classic instances of complex socio-technical systems, whose defining characteristic is that they exist and function by integrating (continuous-time) interactions among technical components and human elements. Typically, unlike physical systems, there are no governing laws for describing their dynamics. Based purely on micro-unit data, here we present a data-driven framework to analyze macro-dynamics in such systems, leading us to the identification of specific states and prediction of transitions across them. It consists of three steps, which we elucidate using data from the Dutch railways. First, we form a dimensionally reduced phase-space by extracting a few relevant components, wherein relevance is proxied by dominance in terms of explained variance, as well as by persistence in time. Secondly, we apply a clustering algorithm to the reduced phase-space, resulting in the revelation of states of the system. Specifically, we identify ‘rest’ and ‘disrupted’ states, for which the system operations deviates respectively little and strongly from the planned timetable. Third, we define an early-warning metric based on the probability of transitions across states, predict whether the system is likely to transit from one state to another within a given time-frame and evaluate the performance of this metric using the Peirce skill score. Interestingly, using case studies, we demonstrate that the framework is able to predict large-scale disruptions up to 90 minutes beforehand with significant skill, demonstrating, for the railway companies, its potential to better track the evolution of large-scale disruptions in their networks. We discuss that the applicability of the three-step framework stretches to other systems as well—i.e., not only socio-technical ones—wherein real-time monitoring can help to prevent macro-scale state transitions, albeit the methods chosen to execute each step may depend on specific system-details.

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“…Prognostics and Health Management (PHM) is considered as an important and efficient way for increasing the safety, benefit, reliability and efficiency of transportation systems, relying on past and current information on environmental, operational and usage records to detect (Liu et al, 2017) and diagnose (Guzinski et al, 2010) degradation, to predict future conditions (Kecman et al, 2015) and schedule proper maintenance interventions (Yan et al, 2016;Zio, 2012). By the recording of data on the system conditions, data-driven methods have been widely integrated for analyzing and managing faults and accidents in transportation systems (Liu, 2017;Zilko et al, 2016;Li and He, 2015).…”

Section: Introductionmentioning

confidence: 99%