Applying Data Analytics to Freight Train Delays in Shunting Yards
Time: Mon 2020-11-30 15.00
Location: Via Zoom https://kth-se.zoom.us/j/64405987038, Du som saknar dator/datorvana kan kontakta firstname.lastname@example.org, If you lack a computer or computer skills, please contact email@example.com, Stockholm (English)
Subject area: Transport Science, Transport Systems
Doctoral student: Niloofar Minbashi , Transportplanering, Train traffic and logistics
Opponent: Dr C. Tyler Dick, the Railway Transportation and Engineering Center (RailTEC), University of Illinois at Urbana-Champaign (UIUC)
Supervisor: Docent Markus Bohlin, Transportplanering; Tekn.Dr Behzad Kordnejad, Transportplanering; Tekn.Dr Carl-William Palmqvist, Division of Transport and Roads, Department of Technology and Society, Lund University
The European Commission has foreseen a modal share of 30% by 2030 for rail freight transport. To achieve this increase in the modal share, enhanced reliability of rail freight services is required. Optimal functioning of shunting yards is one of the areas that can improve this reliability. Shunting yards are large areas allocated to reassemble freight trains for dispatching to new destinations. Their productivity has a direct impact on the overall performance of a rail freight network. Therefore, analysing and modelling of departure deviations from shunting yards are required to enhance the interactions between shunting yards and the network; this thesis contributes to this gap. Paper I investigates the probability and temporal distribution of departure deviations using a large data set comprising 250,000 departures over seven years from two main shunting yards (Malmö and Hallsberg) in Sweden. The probability distribution of departure deviations is found comparing four main distributions including the exponential, the log-normal, the gamma, and the Weibull according to the maximum likelihood estimates and the results of the Anderson-Darling goodness of fit test. The log-normal and the gamma are shown the best fits for departure deviations: the former on delays, and the latter on early departures. In the temporal delay distribution, the weekly and monthly, but not yearly delayed departures are positively correlated with the network usage. However, for hourly delayed departures, a shunting yard involved with international traffic does not show any correlation between delayed departures and the network usage, whereas a domestic shunting yard shows a significant negative correlation between these two parameters. The findings obtained from this thesis contribute to a better understanding of departure deviations from shunting yards, and can be applied in enhancing the operations and capacity utilization of shunting yards in future models. Papers II and III analyse the relationship between congestion in the arrival yard and departure delays using the same data set as paper I. According to previous research, congestion plays an important role in shunting yard delays. With defining congestion as the number of arriving trains before departure time, paper II analyses this relationship limiting the arrivals and departures between the two shunting yards considering varying time periods before departure,whereas Paper III elaborates the analysis by defining congestion level in a fixed period of time before departure time including all arrivals and departures. Considering the data set used in the analysis, the results show that there is no significant relationship between the congestion in the arrival yard and departure delays of trains. It is possible that congestion may not impact the departure delays of trains, but it may impact the departure delays of wagons due to missed wagon connection or increasing wagon idle time, which can be explored with the availability of wagon connection data. Additionally, future elaboration of congestion definition, covering congestion at the shunting yard level, may lead to further improved analyses.