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  • 1.
    Bohlin, Markus
    et al.
    KTH, School of Architecture and the Built Environment (ABE).
    Hansmann, R.
    Zimmermann, U. T.
    Optimization of Railway Freight Shunting2018In: Handbook of Optimization in the Railway Industry, Springer-Verlag New York, 2018, p. 181-212Chapter in book (Refereed)
    Abstract [en]

    Railway freight shunting is the process of forming departing trains from arriving freight trains. The process is continuously performed at rail yards. The shunting procedure is complex and rail yards constitute bottlenecks in the rail freight network, often causing delays to individual shipments. One of the problems is that planning for the allocation of tracks at rail yards is difficult, given that the planner has limited resources (tracks, shunting engines, etc.) and needs to foresee the consequences of committed actions for the current inbound trains. The required schedules highly depend on the particular infrastructure of the rail yard, on the configuration of inbound and outbound trains, and on the business objectives. Thus, new optimization tools as active decision support for the dispatchers are closely tailored to the actual processes. Due to its practical relevance, a broad range of variants has been discussed and solved by the scientific community in recent years. For selected relevant variants, we describe their fruitful relation to scientific research topics such as graph coloring, sequence partitioning, and scheduling, we discuss their computational complexity and approximability, and we outline efficient optimization procedures. In particular, we consider a set of models and algorithms which are applicable in practice, and discuss their application to the shunting yards in Ludwigshafen, Germany and in Hallsberg, Sweden. We also discuss similarities and differences between the different approaches and outline the need for future research.

  • 2.
    Högdahl, Johan
    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.
    A Combined Simulation-Optimization Approach for Robust Timetabling on Main Railway Lines2023In: Transportation Science, ISSN 0041-1655, E-ISSN 1526-5447, Vol. 57, no 1, p. 52-81Article in journal (Refereed)
    Abstract [en]

    Performance aspects such as travel time, punctuality and robustness are conflicting goals of utmost importance for railway transports. To successfully plan railway traffic, it is therefore important to strike a balance between planned travel times and expected delays. In railway operations research, a lot of attention has been given to construct models and methods to generate robust timetables—that is, timetables with the potential to withstand design errors, incorrect data, and minor everyday disturbances. Despite this, the current state-of-practice in railway planning is to construct timetables manually, possibly with support of microsimulation for robustness evaluation. This paper aims to narrow the gap between the state-of-the-art optimization-based research approaches, and the current state-of-practice to construct timetables by combining simulation and optimization. The paper proposes a combined simulation-optimization approach for double-track lines, which generalizes previous work to allow full flexibility in the order of trains by including a new and more generic model to predict delays. By utilizing delay data from simulation, the approach can make socio-economically optimal modifications of a given timetable by minimizing predicted disutility—the weighted sum of scheduled travel time and total predicted delay.  In a large simulation experiment on the heavily congested Swedish Western Main Line, it is demonstrated that compared with a real-life, manually constructed, timetable large reductions of delays as well as improvements in punctuality could be obtained to a small cost of marginally longer travel times. The cost of scheduled in-vehicle travel time and mean delay was reduced by 5% on average, representing a large improvement for a highly utilized railway line. Furthermore, a separate scaling experiment indicate that the approach can be suitable also for larger problems. 

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  • 3.
    Högdahl, Johan
    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. Mälardalen University, Västerås, Sweden.
    Maximizing railway punctuality: A microsimulation evaluation of robust timetabling methods2023Conference paper (Refereed)
    Abstract [en]

    Punctuality is commonly recognized as one of the most important quality indicators for passenger traffic. Despite this, surprisingly few methods for explicitly maximizing punctuality by optimizing the timetable exists in the literature. We study how late-stage adjustments during the capacity allocation can improve punctuality of the traffic. In this paper, we therefore extend a combined simulation-optimization method so it can be used to explicitly maximize the predicted punctuality of a given nonperiodic timetable on a double-track line. The method is evaluated in two microsimulation experiments in the southbound direction of the Swedish Western Main Line using Railsys. We compare the method in simulation with our previous method for minimizing total disutility, two methods from the scientific literature (light robustness, and robustness in critical points) and two naïve strategies. The methods’ effectiveness is assessed in a detailed statistical analysis considering end-station punctuality, total punctuality, and the robustness measure total disutility. Only light robustness results in timetables that in simulation performs better or equally well as the given timetable (based on the national timetable) with respect to all performance measures and evaluated scenarios. The method for maximizing punctuality performs best with respect to total punctuality.

  • 4.
    Högdahl, Johan
    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.
    Maximizing Railway Punctuality by Combined Simulation and Timetable OptimizationManuscript (preprint) (Other academic)
    Abstract [en]

    Punctuality of railway traffic is one of the most important quality indicators for passenger traffic. In previous research it has been shown that the timetable has substantial impact on punctuality. Despite this, surprisingly few scientific approaches for explicitly maximizing the punctuality exists in the literature. The only two methods with this purpose, that we are aware of, also seems to be too computationally expensive to be applicable in practice. In this paper, we therefore propose a combined simulation-optimization method for improving the punctuality of a given main line timetable. The intended use case for this method is in tactical timetabling, for improving daily graphs in the annual timetabling process through small adjustments of the time supplements in the timetable. Throughout Europe and the world, this step is commonly done based on ad-hoc simulations or experience only. We evaluated the proposed method in a simulation experiment on the Swedish Western Main Line and compared its impact on punctuality and robustness with five other methods---three state-of-the-art methods and two more basic approaches. The most important result from the simulation experiment was that in terms of total punctuality, the proposed method was better than all other methods, and significantly so except in one case. Scalability was evaluated by solving a scenario with 10 replications of the original timetable within a little more than 2 hours, on average.

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  • 5.
    Högdahl, Johan
    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.
    Fröidh, Oskar
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning.
    A combined simulation-optimization approach for minimizing travel time and delays in railway timetables2019In: Transportation Research Part B: Methodological, ISSN 0191-2615, E-ISSN 1879-2367, Vol. 126, p. 192-212Article in journal (Refereed)
    Abstract [en]

    Minimal travel time and maximal reliability are two of the most important properties of a railway transportation service. This paper considers the problem of finding a timetable for a given set of departures that minimizes the weighted sum of scheduled travel time and expected delay, thereby capturing these two important socio-economic properties of a timetable. To accurately represent the complex secondary delays in operational railway traffic, an approach combining microscopic simulation and macroscopic timetable optimization is proposed. To predict the expected delay in the macroscopic timetable, a surrogate function is formulated, as well as a subproblem to calibrate the parameters in the model. In a set of computational experiments, the approach increased the socio-economic benefit by 2-5% and improved the punctuality by 8-25%.

  • 6.
    Högdahl, Johan
    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. SICS Swedish ICT Västerås AB, Kopparbergsvägen 10, SE-722 13 Västerås, Sweden.
    Fröidh, Oskar
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning.
    Combining Optimization and Simulation to Improve Railway Timetable Robustness2017Conference paper (Refereed)
    Abstract [en]

    The Train Timetabling Problem (TTP) is the problem of finding the timetable that utilizes the infrastructure as efficient as possible, while satisfying market demands and operational constraints. As reliability is important to passengers it is important that timetables are robust. In this paper we propose a method that combines optimization and simulation to find the timetable that minimizes the travel times and maximizes the expected punctuality. The core method consists of iteratively re-optimizing a bi-objective mixed integer sequencing timetable model, where both planned travel time and simulated delays are taken into account. Each generated timetable is validated and re-evaluated using the micro-simulation tool RailSys. The advantage of the method is that it captures both the uncertainty of a timetable at the planning stage and the validity of the generated timetable. The method is evaluated on a unidirectional track section of the Western Main Line in Sweden and shows promising results for future research.

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  • 7.
    Johansson, Ingrid
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning. KTH, School of Engineering Sciences (SCI), Centres, The KTH Railway Group.
    Palmqvist, Carl-William
    Lund University, Department of Technology and Society.
    Sipilä, Hans
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning. KTH, School of Engineering Sciences (SCI), Centres, The KTH Railway Group.
    Warg, Jennifer
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning. KTH, School of Engineering Sciences (SCI), Centres, The KTH Railway Group.
    Bohlin, Markus
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning. KTH, School of Engineering Sciences (SCI), Centres, The KTH Railway Group.
    Microscopic and macroscopic simulation of early freight train departuresManuscript (preprint) (Other academic)
    Abstract [en]

    In Sweden and other countries it is not an uncommon practice that freight trains depart more or less on-demand instead of strictly following a pre-planned timetable. However, the systematic effects of freight trains departing late or, in particular, early has long been a contested issue. Although some microscopic simulation tools currently have the capability to evaluate the effect of freight trains departing before schedule, it has yet not been established how macroscopic simulation tools, capable of fast simulation of nation-wide networks, can manage such tasks. This paper uses a case study on a line between two large freight yards in Sweden to investigate how the results of microscopic and macroscopic simulation, represented by two modern simulation tools, differ when it comes to this particular problem. The main findings are that both the microscopic and the macroscopic tools could replicate the empirical punctuality fairly well, with the macroscopic case study results being closer to the empirical data. Furthermore, allowing early departures of freight trains increased overall freight train punctuality without any major impact on passenger train punctuality, as determined by both tools. The results are encouraging, but further studies are needed to determine if macroscopic simulation is on-par with microscopic simulation.

  • 8.
    Johansson, Ingrid
    et al.
    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. Lund Univ, Fac Engn, Box 118, SE-22100 Lund, Sweden..
    Sipilä, Hans
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning.
    Warg, Jennifer
    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.
    Microscopic and macroscopic simulation of early freight train departures2022In: Journal of Rail Transport Planning & Management, ISSN 2210-9706, E-ISSN 2210-9714, Vol. 21, article id 100295Article in journal (Refereed)
    Abstract [en]

    In Sweden and other countries it is not an uncommon practice that freight trains depart more or less on-demand instead of strictly following a pre-planned timetable. However, the systematic effects of freight trains departing late or (in particular) early has long been a contested issue. Although some microscopic simulation tools currently have the capability to evaluate the effect of freight trains departing before schedule, it has yet not been established how macroscopic simulation tools, capable of fast simulation of nation-wide networks, can manage such tasks. This paper uses a case study on a line between two large freight yards in Sweden to investigate how the results of microscopic and macroscopic simulation, represented by two modern simulation tools, differ when it comes to this particular problem. The main findings are that both the microscopic and the macroscopic tools replicated the empirical punctuality fairly well. Furthermore, allowing early departures of freight trains increased overall freight train punctuality while the passenger train punctuality decreased slightly, as determined by both tools. The results are encouraging, but further studies are needed to determine if macroscopic simulation is on-par with microscopic simulation.

  • 9.
    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.

  • 10.
    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.

  • 11.
    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.

  • 12.
    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

  • 13.
    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.

  • 14.
    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.

  • 15. Peterson, A.
    et al.
    Bohlin, Markus
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning. Mälardalen University, School of Innovation, Design and Engineering, Box 325, SE-631 05 Eskilstuna, Sweden.
    Joborn, M.
    Guest editorial for the best papers of RailNorrköping 20192020In: Journal of Rail Transport Planning & Management, ISSN 2210-9706, E-ISSN 2210-9714, Vol. 15, article id 100204Article in journal (Refereed)
  • 16.
    Warg, Jennifer
    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.
    Passenger-orientated Analysis of Railway Capacity Allocation with Help of Simulation2019Conference paper (Refereed)
  • 17.
    Warg, Jennifer
    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.
    Simulation-based Timetable Evaluation with Focus on Passengers2017In: Proc. of the 7th International Conference on Railway Operations Modelling and Analysis, 2017, 2017Conference paper (Refereed)
  • 18.
    Warg, Jennifer
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
    Bohlin, Markus
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering. Mälardalens Högskola.
    The Use of Railway Simulation as an Input to Economic Assessment2015Conference paper (Refereed)
    Abstract [en]

    Capacity is an important factor for assessing a railway. Capacity limitations restrict the possibilities to adjust the service supply to the market demand and can lead to disturban­ces that affect the travellers negatively. For this reason, it is important that the available capacity and the effects of using it are estimated and assessed when benefits are analysed. However, estimations often focus on either socio-economic or capacity aspects only.

    In this paper, a method for evaluating timetable alternatives using time equivalents by combining economic assessment and capacity analysis is developed. Parameters describing each alternative´s characteristics and their effect are stepwise added to an existing model. Both real and simulated delay statistics for express trains on a double-track line with dense, mixed traffic are used to first determine relevant input parameters and calibrate the model, and later compare different alternatives. The results show that the choice of input parameters for the delays and the way how to include them in the model affected the result to a large extent. That highlights the importance of making adequate classifications of data and choosing the right parameters. Simulation is suitable for estimating the effect of changes on reliability which is an important input in an estimation model combining capacity and socio-economic aspects.

  • 19.
    Warg, Jennifer
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering. KTH, School of Engineering Sciences (SCI), Centres, The KTH Railway Group.
    Bohlin, Markus
    KTH, School of Architecture and the Built Environment (ABE), Transport Science.
    The use of railway simulation as an input to economic assessment of timetables2016In: Journal of Rail Transport Planning & Management, ISSN 2210-9706, E-ISSN 2210-9714, Vol. 6, no 3, p. 255-270Article in journal (Refereed)
    Abstract [en]

    Assessment of capacity for highly-used railways is an important and challenging task. This paper describes a method for evaluation of timetables based on capacity and economic assessment. Common methods from both fields are combined. For developing and analysing purposes, the model is first tested with historical delay data for express trains on a double-track line with dense, mixed traffic in Sweden. An assessment aiming to compare the departures is made by combining common weights for different variables. Differences in the results based on the model structure are discussed. In the second step, microscopic simulation is used to reveal delay characteristics of timetable alternatives that are then compared and discussed in a similar way to step 1.

    The presented method using simulation makes it possible to reveal and evaluate characteristics that are important for both timetable planning and economic analysis, for example evaluation of strategies. Timetable and delay times are important input variables that affect the travellers' choice. Using simulation and other methods from capacity planning gives the opportunity to find characteristics for analysing alternatives and improve economic evaluation, at the same time as the use of economic parameters provides more possibilities to make a relevant capacity analysis.

  • 20.
    Weik, Norman
    et al.
    Institute of Transport Science, RWTH Aachen University, Mies-van-der-Rohe-Str. 1, 52074, Aachen, Germany.
    Warg, Jennifer
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning.
    Johansson, Ingrid
    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.
    Nießen, Nils
    Institute of Transport Science, RWTH Aachen University, Mies-van-der-Rohe-Str. 1, 52074, Aachen, Germany.
    Extending UIC 406-based capacity analysis – New approaches for railway nodes and network effects2020In: Journal of Rail Transport Planning & Management, ISSN 2210-9706, E-ISSN 2210-9714, Vol. 15, p. 100199-, article id 100199Article in journal (Refereed)
    Abstract [en]

    Railway capacity planning aims to determine the amount of traffic that can be operated on a given infrastructure. The timetable compression method described in UIC Code 406 has become one of the standard tools in this area. Motivated by the Swedish Transportation Administration's timetable independent adaptation of the methodology and its need for extension we explore how the compression method can be applied to evaluate the capacity of the underlying infrastructure for strategic planning rather than the occupation ratio of a specific timetable. By performing ensemble averaging of scheduled train sequences we abstract from a single timetable concept and perform a distributional analysis of timetable utilization. To mitigate decomposition-induced underestimation of network effects the compression area is extended and approaches to include interdependencies between stations and lines are investigated. The methodology is applied for capacity assessment of railway stations and line segments in a case study based on the Swedish Southern Main Line rail corridor.

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  • 21. Zinser, Markus
    et al.
    Betz, Torsten
    Warg, Jennifer
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning.
    Solinen, Emma
    Bohlin, Markus
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering, Transport planning.
    Comparison of microscopic and macroscopic approaches to simulating the effects of infrastructure disruptions on railway networks2018Conference paper (Refereed)
1 - 21 of 21
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