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  • 1.
    Minbashi, Niloofar
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning.
    Application of Predictive Analytics for Shunting Yard Delays2023Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Increasing the modal share of rail freight transport is one of the main ways to achieve carbon neutrality in Europe. The perceived low reliability and predictability of rail freight services is one of the main challenges to overcome in reaching this target. Shunting yards play an important role in providing more reliable and predictable freight trains. Shunting yard departure deviations impact other trains on mixed-traffic railway networks. Predictable departures from shunting yards increase the overall predictability of freight train runs along the network.

    The primary focus of this thesis is on how to apply data-driven approaches to increase the predictability of shunting yard departures. Descriptive analytics were used to provide enhanced insight into shunting yard departures, and predictive analytics were applied to develop shunting yard departure deviation prediction models. Finally, hybrid modeling was used to integrate the yard departure prediction model with other simulation models for wider application. The results from this thesis contribute to providing a deeper understanding of shunting yard departure deviations, interactions between shunting yards and the network through departure and arrival deviations, and how to model these deviations by applying data-driven approaches. These results from five published research papers are included and presented in this doctoral thesis.

    Descriptive analytics methods are applied in papers I and II to explore the probability distribution of departure deviations and the impact of the network on departure delays. The results show that positive and negative departure deviations have different distributions for different shunting yards. Moreover, network usage fluctuations over shorter timespans impact departure delays, whereas no correlation is established between network impact, defined as congestion in the arrival yard, and departure delays.

    Predictive analytics is applied in paper III by developing tree-based algorithms to classify the status of shunting yard departures. The departure status are imbalanced; the majority are early, and the minority are delayed. The results show that applying methods to overcome imbalanced data sets can improve the prediction of delayed departures.

    The models developed in paper III are extended in papers IV and V to predict departure deviations in a combined modeling approach for two separate applications. In paper IV, a machine learning-assisted macro simulation model framework is introduced to integrate yard departure predictions into a macro simulation network model and predict the arrivals to the next yard. The results show improved prediction accuracy compared to a basic machine learning model and a baseline timetable model.

    Finally, in paper V, the generalization of the yard departure prediction model is explored by applying a simulation-assisted machine learning modeling approach where the model is trained on real-world European yard data and North American simulation yard data. The results show the model has a notable generalized performance with both data types.

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  • 2.
    Minbashi, Niloofar
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning.
    Applying Data Analytics to Freight Train Delays in Shunting Yards2020Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    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.

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  • 3.
    Minbashi, Niloofar
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning.
    Bohlin, Markus
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning.
    Kordnejad, Behzad
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning.
    A departure delay estimation model for freight trains2020In: Proceedings of TRA2020, the 8th Transport Research Arena 2020: Rethinking transport – towards clean and inclusive mobility / [ed] Toni Lusikka, 2020Conference paper (Refereed)
    Abstract [en]

    The main objective of this paper is to develop a macroscopic delay estimation model for freight trains departing from the marshalling yard. Freight trains are made up in large marshalling yards comprising three yards (arrival, classification, departure). On time operations in marshaling yards enhances reliability of rail freight services compared to other modes of freight transport. Currently, freight trains encounter most of their delays in marshalling yards even before entering the railway network. Therefore, it is needed to estimate the departure delay of freight trains from the marshaling yard. So far, studies have mainly focused on classification yard operations to estimate departure delay, whereas a proper delay estimation model should be able to consider processes of all three yards. We have developed our model considering main factors (yard congestion, railcar availability and locomotive availability) from all three yards. Hallsberg and Malmö Marshalling yards in Sweden were used as case study.

  • 4.
    Minbashi, Niloofar
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning.
    Bohlin, Markus
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning.
    Kordnejad, Behzad
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning.
    Analysis of Railyard Congestion and Departure Delay Relationship: a Case Study from Swedish Railways2021Conference paper (Refereed)
    Abstract [en]

    In this paper we propose a macroscopic model framework for departure delay prediction from railyards. The railyard is a large area comprising three sub-yards (arrival, classification, departure). In fact, timely operation at railyard is dependent on coordinated operations in these sub-yards. More importantly, punctual functioning of railyards is crucial for increasing competitiveness of rail freight services throughout the network. Despite previous models, we considered railyard congestion at the arrival yard, time availability of each wagon at the classification yard, and time availability of locomotive at the departure yard. The core part of this paper analyzes the effect of congestion at arrival yard on departure delays. Punctuality data from two Swedish railyards for a seven-year period is used. The congestion is defined as the number of arriving trains three hours before each departure. The results showed that the highest number of delayed departures occur at congestion levels of 4-10, while correlation coefficient is around zero. Analysing the whole dataset reveals that these congestion levels are common for all departures not just the delayed ones. Therefore, we conclude that as three sub-yards are interrelated, a comprehensive definition of congestion at railyard level is required. An elaborate definition of congestion can make it a proper predictor for further delay prediction models.

  • 5.
    Minbashi, Niloofar
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning.
    Bohlin, Markus
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning.
    Palmqvist, Carl-William
    Lund Univ, Div Transport & Rd, POB 118, S-22100 Lund, Sweden..
    Kordnejad, Behzad
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning.
    The Application of Tree-Based Algorithms on Classifying Shunting Yard Departure Status2021In: Journal of Advanced Transportation, ISSN 0197-6729, E-ISSN 2042-3195, Vol. 2021, article id 3538462Article in journal (Refereed)
    Abstract [en]

    Shunting yards are one of the main areas impacting the reliability of rail freight networks, and delayed departures from shunting yards can further also affect the punctuality of mixed-traffic networks. Methods for automatic detection of departures, which are likely to be delayed, can therefore contribute towards increasing the reliability and punctuality of both freight and passenger services. In this paper, we compare the performance of tree-based methods (decision trees and random forests), which have been highly successful in a wide range of generic applications, in classifying the status of (delayed, early, and on-time) departing trains from shunting yards, focusing on the delayed departures as the minority class. We use a total number of 6,243 train connections (representing over 21,000 individual wagon connections) for a one-month period from the Hallsberg yard in Sweden, which is the largest shunting yard in Scandinavia. Considering our dataset, our results show a slight difference between the application of decision trees and random forests in detecting delayed departures as the minority class. To remedy this, enhanced sampling for minority classes is applied by the synthetic minority oversampling technique (SMOTE) to improve detecting and assigning delayed departures. Applying SMOTE improved the sensitivity, precision, and F-measure of delayed departures by 20% for decision trees and by 30% for random forests. Overall, random forests show a relative better performance in detecting all three departure classes before and after applying SMOTE. Although the preliminary results presented in this paper are encouraging, future studies are needed to investigate the computational performance of tree-based algorithms using larger datasets and considering additional predictors.

  • 6.
    Minbashi, Niloofar
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning.
    Palmqvist, Carl-William
    Division of Transport and Roads, Department of Technology and Society, Lund University.
    Bohlin, Markus
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning.
    Kordnejad, Behzad
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning.
    Statistical Analysis of Departure Deviations from Shunting Yards: Case study from Swedish Railways2021In: Journal of Rail Transport Planning & Management, ISSN 2210-9706, E-ISSN 2210-9714, Vol. 18Article in journal (Refereed)
    Abstract [en]

    Departure deviations from shunting yards impact the reliability of rail freight services and the punctuality of a railway network. Therefore, the statistical analysis of these deviations are necessary for improving the operation of trains in mixed-traffic networks. In our paper, we conduct a detailed statistical analysis of departure deviations considering individual shunting yards characteristics. We use a large freight train delay dataset comprising 250,000 departures over seven years for the two largest shunting yards in Sweden, comparable to other medium-sized shunting yards in Europe. To find the probability distribution of departure deviations, we compare four distribution functions including the exponential, the log-normal, the gamma, and the Weibull according to the maximum likelihood estimates and results of the Anderson-Darling goodness of fit test. In our experiments, we show that the log-normal distribution fits best for delayed departures across both shunting yards, and for early departures at one of them, whereas the gamma distribution fits best for early departures at the other yard. For the temporal delay distribution, we find that fluctuations in the network usage impact the percentage of delayed departures across hours and weekdays, but not across months or years. In addition, we find that freight trains are mostly delayed in the winter.  In the case of hourly delayed departures, we demonstrate that a shunting yard involved with domestic traffic showed a negative correlation between delayed departures and the network usage, whereas an international shunting yard did not, which indicates individuality in shunting yard operations impact shunting yard-network interactions. Our findings mainly contribute to better understanding of departure deviations from shunting yards, thus enhancing the operations and capacity utilization of shunting yards. Moreover, delay distributions can be beneficial in handling delays in traffic management models as well as enhancing the outputs of freight train simulation models

  • 7.
    Minbashi, Niloofar
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning.
    Sipilä, Hans
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning.
    Palmqvist, Carl-William
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning.
    Bohlin, Markus
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning.
    Kordnejad, Behzad
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning.
    Machine learning-assisted macro simulation for yard arrival prediction2023In: Journal of Rail Transport Planning & Management, ISSN 2210-9706, E-ISSN 2210-9714, Vol. 25, article id 100368Article in journal (Refereed)
    Abstract [en]

    Increasing the modal share of the single wagonload transport in Europe requires improving the reliability and predictability of freight trains running between the yards. In this paper, we propose a novel machine learning-assisted macro simulation framework to increase the predictability of yard departures and arrivals. Machine learning is applied through a random forest algorithm to implement a yard departure prediction model. Our yard departure prediction approach is less complex compared to previous yard simulation approaches, and provides an accuracy level of 92% in predictions. Then, departure predictions assist a macro simulation network model (PROTON) to predict arrivals to the succeeding yards. We tested this framework using data from a stretch between two main yards in Sweden; our experiments show that the current framework performs better than the timetable and a basic machine learning arrival prediction model by R2 of 0.48 and a mean absolute error of 35 minutes. Our current results indicate that combination of approaches, including yard and network interactions, can yield competitive results for complex yard arrival time prediction tasks which can assist yard operators and infrastructure managers in yard re-planning processes and yard-network coordination respectively.

  • 8.
    Minbashi, Niloofar
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning.
    Zhao, Jiaxi
    University of Texas at Austin, Department of Civil, Architectural and Environmental Engineering, Texas Railway Analysis & Innovation Node (TRAIN) .
    Dick, C. Tyler
    University of Texas at Austin, Department of Civil, Architectural and Environmental Engineering, Texas Railway Analysis & Innovation Node (TRAIN) .
    Bohlin, Markus
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning. School of Innovation, Design and Technology, Malardalen University, Eskilstuna, Sweden.
    Application of Simulation-assisted Machine Learning for Yard Departure Prediction2023Conference paper (Refereed)
    Abstract [en]

    Increasing the modal share of rail freight is an ongoing goal in Europe and North America. Yards can play an important role in realizing this target by their reliable and predictable performance. We aim at predicting yard departures by implementing a simulation-assisted machine learning model via two general and step-wise concepts for including the predictors. The former adds all predictors at once, and the latter adds them per the availability or the sub-yard. The data used for training the model is a one-year real-world operational data set from a European hump yard and multiple two-year simulation data sets from a representative hump yard in North America. To the best of our knowledge, no previous research has attempted to implement a generalizable prediction model between the European and the North American contexts. The model is developed on a decision tree algorithm based on a 10-fold cross-validation process. Comparing the model performance on three data sets: the real-world, a baseline simulation, and an ultimate randomness simulation shows that the model has a similar performance in the first two data sets with a respective R-squared of 0.90 and 0.87, which shows high capturing of the variance in the data. However, adding large randomness in the simulation decreases the R-squared to 0.70. Results for the step-wise inclusion of the predictors are different for the real-world and simulation data. For the former, adding more operational predictors does not change the model performance, whereas for the latter, adding departure yard predictors increases the R-squared substantially. The global feature importance shows that for the real-world data almost all predictors contribute to a great extent to the predictions, with maximum planned length, departure week day, and the number of arriving trains as the most contributing ones, whereas for the simulation data, the departure yard predictors provide the largest contribution.

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