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Palmqvist, Carl-WilliamORCID iD iconorcid.org/0000-0002-3906-1033
Publications (7 of 7) Show all publications
Tiong, K. Y., Ma, Z. & Palmqvist, C.-W. (2023). A review of data-driven approaches to predict train delays. Transportation Research Part C: Emerging Technologies, 148, 104027, Article ID 104027.
Open this publication in new window or tab >>A review of data-driven approaches to predict train delays
2023 (English)In: Transportation Research Part C: Emerging Technologies, ISSN 0968-090X, E-ISSN 1879-2359, Vol. 148, p. 104027-, article id 104027Article, review/survey (Refereed) Published
Abstract [en]

Accurate train delay prediction is vital for effective railway traffic planning and management as well as for providing satisfactory passenger service quality. Despite significant advances in data-driven train delay predictions, it lacks of a systematic review of studies and unified modelling development framework. The paper reviews existing studies with an explicit focus on synthesizing a structural framework that could guide effective data-driven train delay prediction model development. The framework consists of three stages including design concept, modelling and evaluation. The study synthesize and discusses six important modules of the framework: (1) Problem scope, (2) Model inputs, (3) Data quality, (4) Methodologies, (5) Model outputs, and (6) Evaluation techniques. For each module, the important problems and techniques reported are synthesized and research gaps are discussed. The review found that most studies focus on developing complex methodologies for the next stop delay predictions that have limited applications in practice. All studies validate the model accuracy, but very few consider other model performance aspects which makes it difficult to assess their usfulness in practical deployment. Future studies need a holistic view on defining the train delay prediction problem considering both application requirements and implementation challenges. Also, the modelling studies should place more attention to data quality and comprehensive model evaluations in representation power, explainability and validity.

Place, publisher, year, edition, pages
Elsevier BV, 2023
Keywords
Train delay prediction, Data-driven prediction, Technical development, Railway operations and information
National Category
Transport Systems and Logistics
Identifiers
urn:nbn:se:kth:diva-328427 (URN)10.1016/j.trc.2023.104027 (DOI)000991257400001 ()2-s2.0-85146594886 (Scopus ID)
Note

QC 20231122

Available from: 2023-06-12 Created: 2023-06-12 Last updated: 2023-11-22Bibliographically approved
Tiong, K. Y., Ma, Z. & Palmqvist, C.-W. (2023). Analyzing factors contributing to real-time train arrival delays using seemingly unrelated regression models. Transportation Research Part A: Policy and Practice, 174, Article ID 103751.
Open this publication in new window or tab >>Analyzing factors contributing to real-time train arrival delays using seemingly unrelated regression models
2023 (English)In: Transportation Research Part A: Policy and Practice, ISSN 0965-8564, E-ISSN 1879-2375, Vol. 174, article id 103751Article in journal (Refereed) Published
Abstract [en]

Understanding the impact of various factors on train arrival delays is a prerequisite for effective railway traffic operating control and management. Existing studies analyze the train delay factors using a single, generic regression equation, restricting their capability in accounting for heterogeneous impacts of spatiotemporal factors on arrival delays as the train travels along its route. The paper proposes a set of equations conditional on the train location for analyzing train arrival delay factors at stations. We develop a seemingly unrelated regression equation (SURE) model to estimate the coefficients simultaneously while considering potential correlations between regression residuals caused by shared unobserved variables among equations. The railway data from 2017 to 2020 in Sweden are used to validate the proposed model and explore the effects of various factors on train arrival delays. The results confirm the necessity of developing a set of station-specific train arrival delay models to understand the heterogeneous impact of explanatory variables. The results show that the significant factors impacting train arrival delays are primarily train operations, including dwell times, running times, and operation delays from previous trains and upstream stations. The factors of the calendar, weather, and maintenance are also significant in impacting delays. Importantly, different train operating management strategies should be targeted at different stations since the impacts of these factors could vary depending on where the station is.

Place, publisher, year, edition, pages
Elsevier BV, 2023
Keywords
Train arrival delays, Seemingly unrelated regression models, Factors analysis, Heterogeneous impact
National Category
Transport Systems and Logistics
Identifiers
urn:nbn:se:kth:diva-334310 (URN)10.1016/j.tra.2023.103751 (DOI)001038238400001 ()2-s2.0-85164274665 (Scopus ID)
Note

QC 20230818

Available from: 2023-08-18 Created: 2023-08-18 Last updated: 2023-08-18Bibliographically approved
Minbashi, N., Sipilä, H., Palmqvist, C.-W., Bohlin, M. & Kordnejad, B. (2023). Machine learning-assisted macro simulation for yard arrival prediction. Journal of Rail Transport Planning & Management, 25, Article ID 100368.
Open this publication in new window or tab >>Machine learning-assisted macro simulation for yard arrival prediction
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2023 (English)In: Journal of Rail Transport Planning & Management, ISSN 2210-9706, E-ISSN 2210-9714, Vol. 25, article id 100368Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Elsevier BV, 2023
Keywords
Yards, Delay prediction, Macroscopic simulation, Machine learning, Rail traffic
National Category
Transport Systems and Logistics
Identifiers
urn:nbn:se:kth:diva-324874 (URN)10.1016/j.jrtpm.2022.100368 (DOI)000923576100001 ()2-s2.0-85145972631 (Scopus ID)
Note

QC 20231122

Available from: 2023-03-20 Created: 2023-03-20 Last updated: 2023-11-22Bibliographically approved
Johansson, I., Palmqvist, C.-W., Sipilä, H., Warg, J. & Bohlin, M. (2022). Microscopic and macroscopic simulation of early freight train departures. Journal of Rail Transport Planning & Management, 21, Article ID 100295.
Open this publication in new window or tab >>Microscopic and macroscopic simulation of early freight train departures
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2022 (English)In: Journal of Rail Transport Planning & Management, ISSN 2210-9706, E-ISSN 2210-9714, Vol. 21, article id 100295Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Elsevier BV, 2022
Keywords
Railway, Simulation, Microscopic, Macroscopic, Freight trains, Early departures
National Category
Transport Systems and Logistics
Identifiers
urn:nbn:se:kth:diva-310275 (URN)10.1016/j.jrtpm.2022.100295 (DOI)000761117600002 ()2-s2.0-85124169483 (Scopus ID)
Note

QC 20220328

Available from: 2022-03-28 Created: 2022-03-28 Last updated: 2024-03-18Bibliographically approved
Tiong, K., Ma, Z. & Palmqvist, C.-W. (2022). Real-time Train Arrival Time Prediction at Multiple Stations and Arbitrary Times. In: 2022 IEEE 25TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC): . Paper presented at IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), OCT 08-12, 2022, Macau, PEOPLES R CHINA (pp. 793-798). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Real-time Train Arrival Time Prediction at Multiple Stations and Arbitrary Times
2022 (English)In: 2022 IEEE 25TH INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC), Institute of Electrical and Electronics Engineers (IEEE) , 2022, p. 793-798Conference paper, Published paper (Refereed)
Abstract [en]

Real-time prediction of train arrivals is important for proactive traffic control and information provision in passenger rails. Despite many studies in predicting arrival times or delays at stations, they are essentially the next-step time series prediction problem which may limit their applications in practice. For example, passengers on the trains or waiting on platforms may have different destinations and need the predicted train arrival times for any downstream stations rather than only the next station. The paper aims to formulate a real-time train arrival times prediction problem at multiple stations and arbitrary times. We develop multi-output machine learning models and systematically evaluate their performance using train operation data in Sweden. The direct multi-output regression models with different regression functions are tested, including LightGBM, linear regression, random forest regression, and gradient boosting regression models. The hyperparameters are optimized using random grid search and five-fold cross-validation methods. The results show that the Direct Multi-Output LightGBM significantly outperformed other models in terms of accuracy. The predictions at downstream stations improve as the train moves along given more real-time information is observed.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2022
Series
IEEE International Conference on Intelligent Transportation Systems-ITSC, ISSN 2153-0009
Keywords
Multi-Output Regression, Train arrival times, LightGBM, Machine learning, High-speed railway
National Category
Transport Systems and Logistics
Identifiers
urn:nbn:se:kth:diva-326436 (URN)10.1109/ITSC55140.2022.9922299 (DOI)000934720600123 ()2-s2.0-85141846305 (Scopus ID)
Conference
IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), OCT 08-12, 2022, Macau, PEOPLES R CHINA
Note

QC 20230503

Available from: 2023-05-03 Created: 2023-05-03 Last updated: 2024-03-18Bibliographically approved
Johansson, I., Sipilä, H. & Palmqvist, C.-W. (2022). Simulating the Punctuality Impacts of Early Freight Train Departures. In: Proceedings of The 13th World Congress on Railway Research (WCRR): . Paper presented at The 13th World Congress on Railway Research (WCRR), Birmingham, UK, 6-10 June 2022.
Open this publication in new window or tab >>Simulating the Punctuality Impacts of Early Freight Train Departures
2022 (English)In: Proceedings of The 13th World Congress on Railway Research (WCRR), 2022Conference paper, Published paper (Refereed)
Abstract [en]

Railway traffic usually adheres to a timetable, but in Sweden, around two-thirds of the freight trains depart before they are scheduled, often by hours. Even though they occur in real operations, early departures have rarely been included in simulation studies and the effects on punctuality are not fully investigated. With a macroscopic simulation tool such as PROTON, large networks can be simulated in a short time, which makes the simulation process easier. This paper uses the tool PROTON to perform a macroscopic simulation case study on the Swedish Western mainline to investigate how early departures of freight trains affect punctuality. The resulting output is a marginal overall punctuality improvement of about +0.5 percentage points. In addition, different levels of primary run time and dwell time delays have been used as simulation input, based on empirical data. The resulting ratio between primary and secondary delays appears to vary greatly between different train types, but overall about 30% were primary and 70% secondary. Future work includes modelling and calibration of departure deviations, which vary more between different train types, and where it is more difficult to separate between primary and secondary delays. Separating distributions based on train type or location will also be considered.

Keywords
railway traffic, freight trains, macroscopic simulation, early departures, delays
National Category
Transport Systems and Logistics
Research subject
Transport Science, Transport Systems
Identifiers
urn:nbn:se:kth:diva-323368 (URN)
Conference
The 13th World Congress on Railway Research (WCRR), Birmingham, UK, 6-10 June 2022
Projects
Punctual Metropolitan Railways 2
Note

QC 20230130

Available from: 2023-01-27 Created: 2023-01-27 Last updated: 2024-03-18Bibliographically approved
Minbashi, N., Palmqvist, C.-W., Bohlin, M. & Kordnejad, B. (2021). Statistical Analysis of Departure Deviations from Shunting Yards: Case study from Swedish Railways. Journal of Rail Transport Planning & Management, 18
Open this publication in new window or tab >>Statistical Analysis of Departure Deviations from Shunting Yards: Case study from Swedish Railways
2021 (English)In: Journal of Rail Transport Planning & Management, ISSN 2210-9706, E-ISSN 2210-9714, Vol. 18Article in journal, Meeting abstract (Refereed) Published
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

Place, publisher, year, edition, pages
Elsevier BV, 2021
Keywords
Shunting yards, departure deviations, delays, the probability distribution, the temporal delay distribution, exploratory data analysis
National Category
Transport Systems and Logistics
Research subject
Transport Science; Transport Science, Transport Systems; Järnvägsgruppen - Effektiva tågsystem för godstrafik; Järnvägsgruppen - Kapacitet
Identifiers
urn:nbn:se:kth:diva-284669 (URN)10.1016/j.jrtpm.2021.100248 (DOI)000658933800003 ()2-s2.0-85103315788 (Scopus ID)
Projects
Shift2RailFR8HUBFR8RAIL III
Funder
Swedish Transport Administration
Note

QC 20210331

Available from: 2020-11-02 Created: 2020-11-02 Last updated: 2024-03-18Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-3906-1033

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