Relevant bibliographies by topics / Passenger Train Delay Prediction

Academic literature on the topic 'Passenger Train Delay Prediction'

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Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Passenger Train Delay Prediction"

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Nabian, Mohammad Amin, Negin Alemazkoor, and Hadi Meidani. "Predicting Near-Term Train Schedule Performance and Delay Using Bi-Level Random Forests." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 5 (April 7, 2019): 564–73. http://dx.doi.org/10.1177/0361198119840339.

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Accurate near-term passenger train delay prediction is critical for optimal railway management and providing passengers with accurate train arrival times. In this work, a novel bi-level random forest approach is proposed to predict passenger train delays in the Netherlands. The primary level predicts whether a train delay will increase, decrease, or remain unchanged in a specified time frame. The secondary level then estimates the actual delay (in minutes), given the predicted delay category at primary level. For validation purposes, the proposed model has been compared with several alternative statistical and machine-learning approaches. The results show that the proposed model provides the best prediction accuracy compared with other alternatives. Moreover, constructing the proposed bi-level model is computationally cheap, thereby being easily applicable.
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Chen, Wei, Zongping Li, Can Liu, and Yi Ai. "A Deep Learning Model with Conv-LSTM Networks for Subway Passenger Congestion Delay Prediction." Journal of Advanced Transportation 2021 (May 15, 2021): 1–10. http://dx.doi.org/10.1155/2021/6645214.

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When urban rail transit is faced with a large number of commuter passengers during peak periods, passengers are often waiting for the next train because the subway is running at full load, which causes delays to the overall travel time of passengers. The calculation and prediction of the congestion delay in subway stations can guide the operation department and passengers to make better planning and selection. In this paper, we use a new method based on deep learning technology to evaluate the congestion delay of subway stations. Firstly, we use automatic fare collection (AFC) system data to evaluate the congestion delays of stations. Then, we use a convolutional long short-term memory (Conv-LSTM) network to extract spatial and temporal characteristics to solve the short-term prediction problem of the subway congestion delay in the network structure. The spatiotemporal variables include inbound passenger flow, outbound passenger flow, number of passengers delayed, and average delay time. As a spatiotemporal sequence, the input and prediction targets are both spatiotemporal three-dimensional tensors in the end-to-end training model. The effectiveness of the method is verified by a case study of the Chongqing Rail Transit. Experimental results show that Conv-LSTM is better than the benchmark models in capturing spatial and temporal correlation.
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Wang, Pu, and Qing-peng Zhang. "Train delay analysis and prediction based on big data fusion." Transportation Safety and Environment 1, no. 1 (February 4, 2019): 79–88. http://dx.doi.org/10.1093/tse/tdy001.

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Abstract Despite the fact that punctuality is an advantage of rail travel compared with other long-distance transport, train delays often occur. For this study, a three-month dataset of weather, train delay and train schedule records was collected and analysed in order to understand the patterns of train delays and to predict train delay time. We found that in severe weather train delays are determined mainly by the type of bad weather, while in ordinary weather the delays are determined mainly by the historical delay time and delay frequency of trains. Identifying the factors closely correlated with train delays, we developed a machine-learning model to predict the delay time of each train at each station. The prediction model is useful not only for passengers wishing to plan their journeys more reliably, but also for railway operators developing more efficient train schedules and more reasonable pricing plans.
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Yaghini, Masoud, Mohammad M. Khoshraftar, and Masoud Seyedabadi. "Railway passenger train delay prediction via neural network model." Journal of Advanced Transportation 47, no. 3 (March 3, 2012): 355–68. http://dx.doi.org/10.1002/atr.193.

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Horiguchi, Yuji, Yukino Baba, Hisashi Kashima, Masahito Suzuki, Hiroki Kayahara, and Jun Maeno. "Predicting Fuel Consumption and Flight Delays for Low-Cost Airlines." Proceedings of the AAAI Conference on Artificial Intelligence 31, no. 2 (February 11, 2017): 4686–93. http://dx.doi.org/10.1609/aaai.v31i2.19095.

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Low-cost airlines (LCAs) represent a new category of airlines that provides low-fare flights. The rise and growth of LCAs has intensified the price competition among airlines, and LCAs require continuous efforts to reduce their operating costs to lower flight prices; however, LCA passengers still demand high-quality services. A common measure of airline service quality is on-time departure performance. Because LCAs apply efficient aircraft utilization and the time between flights is likely to be small, additional effort is required to avoid flight delays and improve their service quality. In this paper, we apply state-of-the-art predictive modeling approaches to real airline datasets and investigate the feasibility of machine learning methods for cost reduction and service quality improvement in LCAs. We address two prediction problems: fuel consumption prediction and flight delay prediction. We train predictive models using flight and passenger information, and our experiment results show that our regression model predicts the amount of fuel consumption more accurately than flight dispatchers, and our binary classifier achieves an area under the ROC curve (AUC) of 0.75 for predicting a delay of a specific flight route.
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Zhang, Dalin, Yi Xu, Yunjuan Peng, Yumei Zhang, Daohua Wu, Hongwei Wang, Jintao Liu, Sabah Mohammed, and Alessandro Calvi. "Prediction of Train Station Delay Based on Multiattention Graph Convolution Network." Journal of Advanced Transportation 2022 (February 21, 2022): 1–12. http://dx.doi.org/10.1155/2022/7580267.

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Train station delay prediction is always one of the core research issues in high-speed railway dispatching. Reliable prediction of station delay can help dispatchers to accurately estimate the train operation status and make reasonable dispatching decisions to improve the operation and service quality of rail transit. The delay of one station is affected by many factors, such as spatiotemporal factor, speed limitation or suspension caused by strong wind or bad weather, and high passenger flow caused by major holiday. But previous studies have not fully combined the spatiotemporal characteristics of station delay and the impact of external factors. This paper makes good use of the train operation data, proposes the multiattention mechanism to capture the spatiotemporal characteristics of train operation data and process the external factors, and establishes a Multiattention Train Station Delay Graph Convolution Network (MATGCN) model to predict the train delay at high-speed railway stations, so as to provide references for train dispatching and emergency plan. This paper uses real train operation data coming from China high-speed railway network to prove that our model is superior to ANN, SVR, LSTM, RF, and TSTGCN models in the prediction effect of MAE, RMSE, and MAPE.
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McCarthy, Nicholas, Mohammad Karzand, and Freddy Lecue. "Amsterdam to Dublin Eventually Delayed? LSTM and Transfer Learning for Predicting Delays of Low Cost Airlines." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 9541–46. http://dx.doi.org/10.1609/aaai.v33i01.33019541.

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Flight delays impact airlines, airports and passengers. Delay prediction is crucial during the decision-making process for all players in commercial aviation, and in particular for airlines to meet their on-time performance objectives. Although many machine learning approaches have been experimented with, they fail in (i) predicting delays in minutes with low errors (less than 15 minutes), (ii) being applied to small carriers i.e., low cost companies characterized by a small amount of data. This work presents a Long Short-Term Memory (LSTM) approach to predicting flight delay, modeled as a sequence of flights across multiple airports for a particular aircraft throughout the day. We then suggest a transfer learning approach between heterogeneous feature spaces to train a prediction model for a given smaller airline using the data from another larger airline. Our approach is demonstrated to be robust and accurate for low cost airlines in Europe.
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Gao, Bowen, Dongxiu Ou, Decun Dong, and Yusen Wu. "A Data-Driven Two-Stage Prediction Model for Train Primary-Delay Recovery Time." International Journal of Software Engineering and Knowledge Engineering 30, no. 07 (July 2020): 921–40. http://dx.doi.org/10.1142/s0218194020400124.

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Accurate prediction of train delay recovery is critical for railway incident management and providing passengers with accurate journey time. In this paper, a two-stage prediction model is proposed to predict the recovery time of train primary-delay based on the real records from High-Speed Railway (HSR). In Stage 1, two models are built to study the influence of feature space and model framework on the prediction accuracy of buffer time in each section or station. It is found that explicitly inputting the attribute features of stations and sections to the model, instead of implicit simulation, will improve the prediction accuracy effectively. For validation purpose, the proposed model has been compared with several alternative models, namely, Logistic Regression (LR), Artificial Neutral Network (ANN), Support Vector Machine (SVM) and Gradient Boosting Tree (GBT). The results show that its remarkable performance is better than other schemes. Specifically, when the error is extended to 3[Formula: see text]min, the proposed model can achieve up to the accuracy of 94.63%. It proves that our method has high value in practical engineering application. Considering the delay propagation of trains is a complex process, our future study will focus on building delay propagation knowledge base and dispatcher experience knowledge base.
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Yaghini, Masoud, Maryam Setayesh Sanai, and Hossein Amin Sadrabady. "Passenger Train Delay Classification." International Journal of Applied Metaheuristic Computing 4, no. 1 (January 2013): 21–31. http://dx.doi.org/10.4018/jamc.2013010102.

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One of the most popular data mining areas, which estimate future trends of data, is classification. This research is dedicated to predict Iranian passenger train delay with high accuracy over Iranian railway network. A hybrid method based on neuro-fuzzy inference system and Two-step clustering is used for this purpose. The results indicate that the hybrid method is superior over the other common classification methods. The result can be used by train dispatcher to accurate schedule trains to diminish train delay average.
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Wang, Bing, Peixiu Wu, Quanchao Chen, and Shaoquan Ni. "Prediction and Analysis of Train Passenger Load Factor of High-Speed Railway Based on LightGBM Algorithm." Journal of Advanced Transportation 2021 (June 15, 2021): 1–10. http://dx.doi.org/10.1155/2021/9963394.

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In order to improve the prediction accuracy of train passenger load factor of high-speed railway and meet the demand of different levels of passenger load factor prediction and analysis, the influence factor of the train passenger load factor is analyzed in depth. Taking into account the weather factor, train attribute, and passenger flow time sequence, this paper proposed a forecasting method of train passenger load factor of high-speed railway based on LightGBM algorithm of machine learning. Considering the difference of the influence factor of the passenger load factor of a single train and group trains, a single train passenger load factor prediction model based on the weather factor and passenger flow time sequence and a group of trains’ passenger load factor prediction model based on the weather factor, the train attribute, and passenger flow time sequence factor were constructed, respectively. Taking the train passenger load factor data of high-speed railway in a certain area as an example, the feasibility and effectiveness of the proposed method were verified and compared. It is verified that LightGBM algorithm of machine learning proposed in this paper has higher prediction accuracy than the traditional models, and its scientific and accurate prediction can provide an important reference for the calculation of passenger ticket revenue, operation benefit analysis, etc.
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Dissertations / Theses on the topic "Passenger Train Delay Prediction"

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Minbashi, Niloofar. "Applying Data Analytics to Freight Train Delays in Shunting Yards." Licentiate thesis, KTH, Transportplanering, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-284672.

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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.
Europeiska kommissionen har förutspått en markansandel på 30% framtill 2030 för järnvägstransporter av gods. För att uppnå denna ökning krävsökad tillförlitlighet hos järnvägstransporttjänster. Rangergodsbangårdars optimalafunktion är ett av de områden som kan förbättra denna tillförlitlighet.Rangergodsbangårdar stora områden som är avsedda för att koppla ihopgodståg för sändning till nya destinationer. Deras produktivitet har en direktinverkan på järnvägsnätets totala prestanda. Därför krävs analys och modelleringav avvikelser från dessa noder för att förbättra interaktionen mellanrangergodsbangårdar och järnvägsnätet. I papper I undersöks sannolikheten och den tidsmässiga fördelningen avavvikelser med hjälp av en stor datamängd som omfattar 250 000 avgångaröver sju år från två rangergodsbangårdar (Malmö och Hallsberg) i Sverige.Sannolikhetsdistributioner av avvikelser jämförs med fyra huvuddistributioner,exponentiell, log-normal, gamma och Weibull enligt de maximalasannolikhetsuppskattningarna och resultaten av Anderson-Darling godhetav passningstest. Log-normal och gamma visar sig passa bäst för avvikelser:den förstnämnda vid förseningar och den senare vid tidiga avgångar. I dentidsmässiga fördröjningsfördelningen är de veckovisa och månatliga men inteårliga försenade avgångarna positivt korrelerade med järnvägsnätets nyttjandegrad.För försenade avgångar per timme visar dock en rangergodsbangårdsom är inblandad i internationell trafik ingen korrelation mellan försenadeavgångar och järnvägsnätets nyttjandegrad, medan en inhemsk rangergodsbangårdvisaren signifikant negativ korrelation mellan dessa två parametrar.Resultaten från denna avhandling bidrar till en bättre förståelse av avvikelserfrån rangergodsbangårdar och kan användas för att förbättra drift och kapacitetsutnyttjandeav rangergodsbangårdar växelplatser i framtida modeller. Papper II och III analyserar förhållandet mellan trängsel i ankomstgårdenoch avgångsförseningar med hjälp av samma datamängd som i papperI. Enligt tidigare analyser spelar trängsel en viktig roll vid förseningar förrangergodsbangårdar. Trängsel definieras som antalet ankommande tåg föreavgångstid och papper II analyserar detta förhållande som begränsar ankomsteroch avgångar mellan de två rangergodsbangårdar med beaktande av olikatidsperioder före avgång, medan papper III utvecklar analysen genom attdefiniera trängselnivån under en fast tidsperiod före avgångstid inklusive allaankomster och avgångar. Med tanke på datamängden som användes i analysenvisar resultaten att det inte finns något signifikant samband mellan trängselni ankomstgården och tågens förseningar. Det är möjligt att trängsel kanskeinte påverkar tågens avgångsfördröjningar, men det kan påverka vagnarnasavgångsfördröjningar på grund av missad vagnanslutning eller öka vagnenstomgångstid, vilket kan undersökas med vid tillgång av vagnanslutningsdata.Dessutom kan framtida vidareutveckling av definitionen av trängsel som påen detaljerad nivå täcker rangergodsbangårdars alla delar, leda till ytterligareförbättrade analyser.

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Granlöf, Markus. "A study of the effects of winterclimate and atmospheric icing onhigh-speed passenger trains." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-171868.

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Harsh winter climate causes various problems for both the public andprivate sector in Sweden, especially in the northern part and the railway industryis no exception. This master thesis project covers an investigation of the eects ofthe winter climate and a phenomena called atmospheric icing on the performance ofthe train in a region called the Botnia-Atlantica region. The investigation was donewith data over a short period January-February 2017 with simulated weather datafrom the Weather research and forecast model that was compared with the periodOctober - December 2016. The investigation only included high speed trains.The trains have been analysed based on two dierent performance measurements.The cumulative delay which is the increment in delay over a section and the currentdelay which is the current delay compared to the schedule. Cumulative delaysare investigated with survival analysis and the current delay is investigated with aMulti-state Markov model.The results show that the weather could have an eect on the trains performancewhere the survival analysis detected connection between the weather and cumulativedelays. The Markov model also showed a connection between the weather anddelayed trains including that the presence of atmospheric icing had a negative eecton remaining in a state of non-delay.
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Nilsson, Robert, and Kim Henning. "Predictions of train delays using machine learning." Thesis, KTH, Hälsoinformatik och logistik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-230224.

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Train delays occur on a daily basis in the commuter rail of Stockholm. This means that the travellers might become delayed themselves for their particular destination. To find the most accurate method for predicting train delays, the machine learning methods decision tree with and without AdaBoost and neural network were compared with different settings. Neural network achieved the best result when used with 3 layers and 22 neurons in each layer. Its delay predictions had an average error of 122 seconds, compared to the actual delay. It might therefore be the best method for predicting train delays. However the study was very limited in time and more train departure data would need to be collected.
Tågförseningar inträffar dagligen i Stockholms pendeltågstrafik. Det orsakar att resenärerna själva kan bli försenade till deras destinationer. För att hitta den mest träffsäkra metoden för att förutspå tågförseningar jämfördes maskininlärningsmetoderna beslutsträd, med och utan AdaBoost, och artificiella neuronnät med olika inställningar. Det artificiella neuronnätet gav det bästa resultatet när det användes med 3 lager och 22 neuroner i varje lager. Dess förseningsförutsägelse hade ett genomsnittligt fel på 122 sekunder jämfört med den verkliga förseningen. Det kan därför vara den bästa metoden för att förutspå tågförseningar. Den här studien hade dock väldigt begränsat med tid och mer information om tågavgångar hade behövts samlas in.
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Hsin-IChen and 陳心一. "Long Distance Train Delay Prediction: Evidence from Taiwan Railway System." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/62617643261656329122.

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碩士
國立成功大學
交通管理學系碩博士班
98
Delay of railway trains is highly affecting reliability of the system, and has significant negative impact on customers' satisfaction. Simulation models often used in previous research and in practice to predict the train delay; due to the growth of the scale of system, it turned to be too costly to develop on such problem. This research suggesting an approach on predicts train delay in the short term by linear model and neural network. With the train operation data of Taiwan Railway Administration for three month, we build several models on predicting weather the train will be delayed on the terminal station, the number of delay incidents on the train's journey and the length ratio of delay time, based on the predictive factors of train, route and environment. We'd found that delay occurs much often on weekends and holidays; the congestion delay is not obvious in the research area; specific class of train had seriously high delay rates; delay increase from the morning to the evening in every single day; Tze-Chiang express have different delay condition on each directions of research section. This research suggests that the system operator should build a special holiday timetable, according to the different passenger characteristic and destination between working days and non-working days, also we should increase the headway progressively to avoid the delay accumulate on the time period in a day.
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Book chapters on the topic "Passenger Train Delay Prediction"

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Lapamonpinyo, Pipatphon, Hai-Bang Ly, and Sybil Derrible. "Passenger Train Delay Prediction Using Linear Regression and Ensemble Learning Methods with and Without Ridership and Population Data." In Lecture Notes in Civil Engineering, 1875–85. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-7160-9_190.

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Li, Bo, Xiang-chun Qi, Qiao Li, and Xiao Yang. "Analysis and Prediction of Passenger Flow of High-Speed Night Train." In Green Intelligent Transportation Systems, 565–76. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0302-9_56.

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Singh, Ajay, D. Rajesh Kumar, and Rahul Kumar Sharma. "Prediction of Train Delay System in Indian Railways Using Machine Learning Techniques: Survey." In Lecture Notes in Electrical Engineering, 55–71. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1520-8_5.

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Yaghini, Masoud, Maryam Setayesh Sanai, and Hossein Amin Sadrabady. "Passenger Train Delay Classification." In Transportation Systems and Engineering, 310–19. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-8473-7.ch014.

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One of the most popular data mining areas, which estimate future trends of data, is classification. This research is dedicated to predict Iranian passenger train delay with high accuracy over Iranian railway network. A hybrid method based on neuro-fuzzy inference system and Two-step clustering is used for this purpose. The results indicate that the hybrid method is superior over the other common classification methods. The result can be used by train dispatcher to accurate schedule trains to diminish train delay average.
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Fumeo, Emanuele, Luca Oneto, Giorgio Clerico, Renzo Canepa, Federico Papa, Carlo Dambra, Nadia Mazzino, and Davida Anguita. "Big Data Analytics for Train Delay Prediction." In Innovative Applications of Big Data in the Railway Industry, 320–48. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3176-0.ch014.

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Current Train Delay Prediction Systems (TDPSs) do not take advantage of state-of-the-art tools and techniques for extracting useful insights from large amounts of historical data collected by the railway information systems. Instead, these systems rely on static rules, based on classical univariate statistic, built by experts of the railway infrastructure. The purpose of this book chapter is to build a data-driven TDPS for large-scale railway networks, which exploits the most recent big data technologies, learning algorithms, and statistical tools. In particular, we propose a fast learning algorithm for Shallow and Deep Extreme Learning Machines that fully exploits the recent in-memory large-scale data processing technologies for predicting train delays. Proposal has been compared with the current state-of-the-art TDPSs. Results on real world data coming from the Italian railway network show that our proposal is able to improve over the current state-of-the-art TDPSs.
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Solanki, Arun, and Ela Kumar. "Study and Analysis of Delay Factors of Delhi Metro Using Data Sciences and Social Media." In Innovative Applications of Big Data in the Railway Industry, 209–23. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3176-0.ch009.

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Delhi Metro passengers had a difficult time mostly on Monday morning as trains on the busy corridors are delayed due to technical problems or track circuit failure. This study found different factors like power failure, weather, rider load, festive season, etc. which are responsible for the delay of Delhi Metro. Due to these factors, Metro got delayed and run at a reduced speed causing much inconvenience to the people, who are hoping to reach their offices on time. Delhi Metro data are received from different sources which may be structured (timings, speed, traffic), semi-structured (images and video) and unstructured (maintenance records) form. So, there is heterogeneity in data. Except for this data, the feedback or suggestion of a rider is vital to the system. Nowadays riders are using social media like Facebook and Twitter very frequently. Three-tier architecture is proposed for the delay analysis of Delhi Metro. Different implementation techniques are studied and proposed for the social media module and delay prediction modules for the proposed system.
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Conference papers on the topic "Passenger Train Delay Prediction"

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Sipilä, Hans. "Evaluation of Single Track Timetables Using Simulation." In 2014 Joint Rail Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/jrc2014-3820.

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One way to model train operations and make predictions of future outcome is to use simulation. Many lines and networks connecting major cities have a high capacity utilization, meaning that running additional trains leads to an even more strained situation and delays are likely to increase. The mix of average train speeds is also related to capacity and delay propagation. Considering one line or several lines connected in a network a requested train traffic can consist of different train categories and departure frequencies. There are usually several possible timetables satisfying this traffic demand. The infrastructure often implies limitations on the type and volume of traffic that can be handled. Additionally constraints introduced by requests for regular intervals, minimum headways, passenger transfers between trains etc. can reduce the number of acceptable timetables. This paper presents an approach using combinatorial train initiations and simulation to generate conflict-free timetables. These can then be simulated with random variations in departure and dwell times. This is implemented on a fictive single track line with high speed passenger train traffic. The objective is to study outcome by varying allowance times and delays. Simulations are carried out in RailSys, a software using synchronous simulation to model train traffic operations.
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Wang, Ren, and Daniel B. Work. "Data Driven Approaches for Passenger Train Delay Estimation." In 2015 IEEE 18th International Conference on Intelligent Transportation Systems - (ITSC 2015). IEEE, 2015. http://dx.doi.org/10.1109/itsc.2015.94.

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Hansen, Ingo A., Rob M. P. Goverde, and Dirk J. van der Meer. "Online train delay recognition and running time prediction." In 2010 13th International IEEE Conference on Intelligent Transportation Systems - (ITSC 2010). IEEE, 2010. http://dx.doi.org/10.1109/itsc.2010.5625081.

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Ding, Dong, and Yu Zhou. "Prediction Model of Intercity Passenger Train Volume Based on Grey System Theory." In Third International Conference on Transportation Engineering (ICTE). Reston, VA: American Society of Civil Engineers, 2011. http://dx.doi.org/10.1061/41184(419)204.

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Ren, Yumou, Qi Zhang, Yanhao Sun, Zhi Li, Yunpeng Zhang, and Wei Xu. "Prediction Method for Train Delay Time of High-Speed Railway." In 2020 Chinese Automation Congress (CAC). IEEE, 2020. http://dx.doi.org/10.1109/cac51589.2020.9327307.

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Chen, Shuai, Xingtang Wu, Min Zhou, Haifeng Song, and Hairong Dong. "Digital Twin based Train Delay Prediction System: Design and Realization." In 2022 41st Chinese Control Conference (CCC). IEEE, 2022. http://dx.doi.org/10.23919/ccc55666.2022.9901773.

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Li, Jianmin, Xinyue Xu, Meng Zhao, and Rui Shi. "Train Arrival Delay Prediction Based on a CNN-LSTM Approach." In 21st COTA International Conference of Transportation Professionals. Reston, VA: American Society of Civil Engineers, 2021. http://dx.doi.org/10.1061/9780784483565.054.

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Chen, Shuai, Xingtang Wu, Min Zhou, Bo Yang, Jinhu Lu, and Hairong Dong. "Train Delay Prediction based on a Multimodal Deep-learning Method." In 2021 China Automation Congress (CAC). IEEE, 2021. http://dx.doi.org/10.1109/cac53003.2021.9728179.

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Dou, Shunkun, Liyan Zhang, and Changxian Li. "Improved EMD-PSO-LSSVM train wireless network time-delay prediction." In International Conference on Signal Processing and Communication Security (ICSPCS 2022), edited by Min Xiao and Lisu Yu. SPIE, 2022. http://dx.doi.org/10.1117/12.2655218.

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Dongmei Zhou, Zhibin Zhang, and Ge Meng. "Combined prediction method based on HHT of passenger volume of single inter-city train." In 2016 IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC). IEEE, 2016. http://dx.doi.org/10.1109/imcec.2016.7867375.

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Reports on the topic "Passenger Train Delay Prediction"

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Green, John G., and Francis J. Miller. Examining the Effects of Precision Scheduled Railroading on Intercity Passenger and High-Speed Rail Service. Mineta Transportation Institute, March 2022. http://dx.doi.org/10.31979/mti.2022.2016.

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Abstract:
More than just scheduling terminal-to-terminal trips for trains, “Precision Scheduled Railroading” (PSR) creates entire point-to-point trip plans for individual railroad shipments. Since precision execution was first put into practice, the benefits to shipment arrival reliability and to freight railroads’ profitability have been demonstrated by its use in several Class One freight railroads. However, the effects of the PSR operating strategy on passenger railway operations in shared freight/passenger corridors has not been studied in detail. This research examines the effects of PSR railroad operations on passenger railways, including measuring “Host Railroad Minutes of Delay per 10,000 Train-Miles” and “On-Time Performance” of individual passenger railways, both intercity and high-speed.
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