Journal articles: '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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Lalinská, Jana, Jozef Gašparík, and Denis Šipuš. "Factors Affecting the Delay of Passenger Trains." LOGI – Scientific Journal on Transport and Logistics 8, no. 1 (May 1, 2017): 74–81. http://dx.doi.org/10.1515/logi-2017-0009.

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Abstract Paper deals with the problematic about the information quality impact and the basic methods which can optimize the costs of low quality of using information. First of all, it is important to purify the input data from the inconsistencies and measure the quality of data. This process assures to minimize the reasons that are responsible of poor quality of processes. Targets area of this paper is to identify and minimize the main reasons of delaying the passenger train by comparing years 2012 and 2013. Target groups of passenger trains were divided in three parts responsibilities of delaying – type of train, code of delay, group responsible for the train delay.

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Xu, Wenkai, Peng Zhao, and Liqiao Ning. "A Passenger-Oriented Model for Train Rescheduling on an Urban Rail Transit Line considering Train Capacity Constraint." Mathematical Problems in Engineering 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/1010745.

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The major objective of this work is to present a train rescheduling model with train capacity constraint from a passenger-oriented standpoint for a subway line. The model expects to minimize the average generalized delay time (AGDT) of passengers. The generalized delay time is taken into consideration with two aspects: the delay time of alighting passengers and the penalty time of stranded passengers. Based on the abundant automatic fare collection (AFC) system records, the passenger arrival rate and the passenger alighting ratio are introduced to depict the short-term characteristics of passenger flow at each station, which can greatly reduce the computation complexity. In addition, an efficient genetic algorithm with adaptive mutation rate and elite strategy is used to solve the large-scale problem. Finally, Beijing Subway Line 13 is taken as a case study to validate the method. The results show that the proposed model does help neutralize the effect of train delay, with a 9.47% drop in the AGDT in comparison with the train-oriented model.

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de Rezende Francisco, Fábio, Pedro Leite Sabino, Luiz Antônio Silveira Lopes, Paulo Afonso Lopes da Silva, and Newton José Ferro. "Determination of passenger train reliability through travel delay." Journal of Rail Transport Planning & Management 25 (March 2023): 100369. http://dx.doi.org/10.1016/j.jrtpm.2023.100369.

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Wang, Yao, Dan Zheng, Shi Min Luo, Dong Ming Zhan, and Peng Nie. "The Research of Railway Passenger Flow Prediction Model Based on BP Neural Network." Advanced Materials Research 605-607 (December 2012): 2366–69. http://dx.doi.org/10.4028/www.scientific.net/amr.605-607.2366.

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Based on analyzing the principle of BP neural network and time sequence characteristics of railway passenger flow, the forecast model of railway short-term passenger flow based on BP neural network was established. This paper mainly researches on fluctuation characteristics and short-time forecast of holiday passenger flow. Through analysis of passenger flow and then be used in passenger flow forecasting in order to guide the transport organization program especially the train plan of extra passenger train. And the result shows the forecast model based on BP neural network has a good effect on railway passenger flow prediction.

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Barron, Alexander, Patricia C. Melo, Judith M. Cohen, and Richard J. Anderson. "Passenger -Focused Management Approach to Measurement of Train Delay Impacts." Transportation Research Record: Journal of the Transportation Research Board 2351, no. 1 (January 2013): 46–53. http://dx.doi.org/10.3141/2351-06.

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König, Eva, and Cornelia Schön. "Railway delay management with passenger rerouting considering train capacity constraints." European Journal of Operational Research 288, no. 2 (January 2021): 450–65. http://dx.doi.org/10.1016/j.ejor.2020.05.055.

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Zhang, Y. D., L. Liao, Q. Yu, W. G. Ma, and K. H. Li. "Using the gradient boosting decision tree (GBDT) algorithm for a train delay prediction model considering the delay propagation feature." Advances in Production Engineering & Management 16, no. 3 (September 30, 2021): 285–96. http://dx.doi.org/10.14743/apem2021.3.400.

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Accurate prediction of train delay is an important basis for the intelligent adjustment of train operation plans. This paper proposes a train delay prediction model that considers the delay propagation feature. The model consists of two parts. The first part is the extraction of delay propagation feature. The best delay classification scheme is determined through the clustering method of delay types for historical data based on the density-based spatial clustering of applications with noise algorithm (DBSCAN), and combining the best delay classification scheme and the k-nearest neighbor (KNN) algorithm to design the classification method of delay type for online data. The delay propagation factor is used to quantify the delay propagation relationship, and on this basis, the horizontal and vertical delay propagation feature are constructed. The second part is the delay prediction, which takes the train operation status feature and delay propagation feature as input feature, and use the gradient boosting decision tree (GBDT) algorithm to complete the prediction. The model was tested and simulated using the actual train operation data, and compared with random forest (RF), support vector regression (SVR) and multilayer perceptron (MLP). The results show that considering the delay propagation feature in the train delay prediction model can further improve the accuracy of train delay prediction. The delay prediction model proposed in this paper can provide a theoretical basis for the intelligentization of railway dispatching, enabling dispatchers to control delays more reasonably, and improve the quality of railway transportation services.

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Zhen, Qu, and Shi Jing. "Train rescheduling model with train delay and passenger impatience time in urban subway network." Journal of Advanced Transportation 50, no. 8 (December 2016): 1990–2014. http://dx.doi.org/10.1002/atr.1441.

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Yin, Yonghao, Dewei Li, Kai Zhao, and Ruixia Yang. "Optimum Equilibrium Passenger Flow Control Strategies with Delay Penalty Functions under Oversaturated Condition on Urban Rail Transit." Journal of Advanced Transportation 2021 (February 24, 2021): 1–27. http://dx.doi.org/10.1155/2021/3932627.

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When passengers are oversaturated in the urban rail transit system and a further increase of train frequency is impossible, passenger flow control strategy is an indispensable approach to avoid congestion and ensure safety. To make the best use of train capacity and reduce the passenger waiting time, coordinative flow control is necessary at each station on a line. In most published studies, the equilibrium of passenger distributions among different stations and periods is not considered. As a result, two issues occur making it hard to implement in practical. First, a large number of passengers are held up outside a small number of stations for very long time. Second, there is a large variation of controlled flows for successive time intervals. To alleviate this problem, a single-line equilibrium passenger flow control model is constructed, which minimizes the total passenger delay. By applying different forms of the delay penalty function (constant and linear), flow control strategies such as independent flow control and equilibrium flow control can be reproduced. An improved simulated annealing algorithm is proposed to solve the model. A numerical case is studied to analyze the sensitivity of the functions, and the best parameter relationship in different functions could be confirmed. A real-world case from Batong Line corridor in Beijing subway is used to test the applicability of the model and algorithm, and the result shows that the solution with linear delay penalty functions can not only reduce the total passenger delay but also equilibrate the number of flow control passengers on spatial and temporal.

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Spanninger, Thomas, Alessio Trivella, Beda Büchel, and Francesco Corman. "A review of train delay prediction approaches." Journal of Rail Transport Planning & Management 22 (June 2022): 100312. http://dx.doi.org/10.1016/j.jrtpm.2022.100312.

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Feng, Ziyan, Chengxuan Cao, and Yutong Liu. "Train delay propagation under random interference on high-speed rail network." International Journal of Modern Physics C 30, no. 08 (August 2019): 1950059. http://dx.doi.org/10.1142/s0129183119500591.

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To simulate passenger train movements on the high-speed rail network, this paper proposes a new dynamic model based on the discrete time method and provides some efficient control policies correspondingly. Besides that, an improved minimum safe headway in the moving-block system on the high-speed rail network is presented. Using the proposed method, the dynamic characteristics of railway traffic flow are analyzed under random interferences on the high-speed rail network. Then, some sensitivity analyses are implemented to investigate the propagation features of delays under different interferences. The results indicate that the proposed dynamic model and control policies for the passenger train movements on the high-speed rail network are effective and can be a fundamental research for subsequent research of delay propagation, rerouting and rescheduling problems.

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Wang, Lili, Xuedong Yan, and Yun Wang. "Modeling and Optimization of Collaborative Passenger Control in Urban Rail Stations under Mass Passenger Flow." Mathematical Problems in Engineering 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/786120.

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With the rapid development of urban rail transit, the phenomenon of outburst passenger flows flocking to stations is occurring much more frequently. Passenger flow control is one of the main methods used to ensure passengers’ safety. While most previous studies have only focused on control measures inside the target station, ignoring the collaboration between stops, this paper puts emphasis on joint passenger control methods during the occurrence of large passenger flows. To provide a theoretic description for the problem under consideration, an integer programming model is built, based on the analysis of passenger delay and the processes by which passengers alight and board. Taking average passenger delay as the objective, the proposed model aims to disperse the pressure of oversaturated stations into others, achieving the optimal state for the entire line. The model is verified using a case study and the results show that restricted access measures taken collaboratively by stations produce less delay and faster evacuation. Finally, a sensitivity analysis is conducted, from which we find that the departure interval and maximum conveying capacity of the train affect passenger delay markedly in the process of passenger control and infer that control measures should be taken at stations near to the one experiencing an emergency.

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Ge, Meng, Zhang Junfeng, Wu Jinfei, Han Huiting, Shan Xinghua, and Wang Hongye. "ARIMA-FSVR Hybrid Method for High-Speed Railway Passenger Traffic Forecasting." Mathematical Problems in Engineering 2021 (May 29, 2021): 1–5. http://dx.doi.org/10.1155/2021/9961324.

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In order to improve the prediction accuracy of railway passenger traffic, an ARIMA model and FSVR are combined to propose a hybrid prediction method. The ARIMA prediction model is established based on the known railway passenger traffic data, and then, the ARIMA prediction results are used as the training set of the FSVR method. At the same time, the air price and historical passenger traffic data are introduced to predict the future passenger traffic, to realize the mixed prediction of railway passenger traffic. The case study demonstrates that the hybrid prediction method can effectively improve the prediction performance of railway passenger traffic. Compared with the single ARIMA method, the hybrid prediction method improves the delay of the prediction results. Compared with the FSVR prediction result, the hybrid prediction method greatly reduces the errors in the extreme points of passenger traffic and long-term prediction. The relevant research results of this paper provide a useful reference for the prediction of railway passenger traffic.

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Yang, Ruixia, Weiteng Zhou, Baoming Han, Dewei Li, Bin Zheng, and Fangling Wang. "Research on Coordinated Passenger Inflow Control for the Urban Rail Transit Network Based on the Station-to-Line Spatial-Temporal Relationship." Journal of Advanced Transportation 2022 (March 28, 2022): 1–13. http://dx.doi.org/10.1155/2022/8895935.

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This study proposes a coordinated inflow control organization for the urban rail transit network to improve train capacity utilization and reduce inbound delay rate, considering existing operational requirements and station-to-line spatial-temporal relationship of passenger flow. The coordinated passenger flow control model is proposed with the objective to maximize the inbound flow rate and the train full load rate. A station-to-line spatial-temporal correlation formula is constructed to characterize the relationship between station inbound passenger volume and section passenger volume. A two-stage approach is employed to solve this passenger flow control problem. The proposed model and solution strategy are evaluated on a well-known Beijing network with 10 operating lines. The refined inflow control scheme is displayed with the accurate inbound volume at each station during each time period. The comparison between the proposed control strategy and the current control strategy shows that the former can effectively improve the service level and capacity utilization.

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Yamashiro, Masao, Rieko Otsuka, Toru Sahara, Takeshi Kawasaki, and Sei Sakairi. "Development of passenger flow prediction model during train service disruption." Proceedings of the Transportation and Logistics Conference 2021.30 (2021): SS5–2–4. http://dx.doi.org/10.1299/jsmetld.2021.30.ss5-2-4.

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Asad, Syed Muhammad, Jawad Ahmad, Sajjad Hussain, Ahmed Zoha, Qammer Hussain Abbasi, and Muhammad Ali Imran. "Mobility Prediction-Based Optimisation and Encryption of Passenger Traffic-Flows Using Machine Learning." Sensors 20, no. 9 (May 5, 2020): 2629. http://dx.doi.org/10.3390/s20092629.

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Information and Communication Technology (ICT) enabled optimisation of train’s passenger traffic flows is a key consideration of transportation under Smart City planning (SCP). Traditional mobility prediction based optimisation and encryption approaches are reactive in nature; however, Artificial Intelligence (AI) driven proactive solutions are required for near real-time optimisation. Leveraging the historical passenger data recorded via Radio Frequency Identification (RFID) sensors installed at the train stations, mobility prediction models can be developed to support and improve the railway operational performance vis-a-vis 5G and beyond. In this paper we have analysed the passenger traffic flows based on an Access, Egress and Interchange (AEI) framework to support train infrastructure against congestion, accidents, overloading carriages and maintenance. This paper predominantly focuses on developing passenger flow predictions using Machine Learning (ML) along with a novel encryption model that is capable of handling the heavy passenger traffic flow in real-time. We have compared and reported the performance of various ML driven flow prediction models using real-world passenger flow data obtained from London Underground and Overground (LUO). Extensive spatio-temporal simulations leveraging realistic mobility prediction models show that an AEI framework can achieve 91.17% prediction accuracy along with secure and light-weight encryption capabilities. Security parameters such as correlation coefficient (<0.01), entropy (>7.70), number of pixel change rate (>99%), unified average change intensity (>33), contrast (>10), homogeneity (<0.3) and energy (<0.01) prove the efficacy of the proposed encryption scheme.

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Guo, Jianyuan, Limin Jia, Yong Qin, and Huijuan Zhou. "Cooperative Passenger Inflow Control in Urban Mass Transit Network with Constraint on Capacity of Station." Discrete Dynamics in Nature and Society 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/695948.

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In urban mass transit network, when passengers’ trip demands exceed capacity of transport, the numbers of passengers accumulating in the original or transfer stations always exceed the safety limitation of those stations. It is necessary to control passenger inflow of stations to assure the safety of stations and the efficiency of passengers. We define time of delay (TD) to evaluate inflow control solutions, which is the sum of waiting time outside of stations caused by inflow control and extra waiting time on platform waiting for next coming train because of insufficient capacity of first coming train. We build a model about cooperative passenger inflow control in the whole network (CPICN) with constraint on capacity of station. The objective of CPICN is to minimize the average time of delay (ATD) and maximum time of delay (MTD). Particle swarm optimization for constrained optimization problem is used to find the optimal solution. The numeral experiments are carried out to prove the feasibility and efficiency of the model proposed in this paper.

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Xie, Chengguang, Xiaofeng Li, Bingfa Chen, Feng Lin, Yushun Lin, and Hainan Huang. "Subway Sudden Passenger Flow Prediction Method Based on Two Factors: Case Study of the Dongsishitiao Station in Beijing." Journal of Advanced Transportation 2021 (July 27, 2021): 1–8. http://dx.doi.org/10.1155/2021/5577179.

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A sudden increase in passenger flow can primitively lead to continuous congestion of a subway network and thus have a profound impact on the subway system. To prevent the risk caused by sudden overcrowding, the prediction of passenger flow is a daily task of the rail transit management. Most current short-term passenger flow forecasts rely only on inbound passenger flow, which cannot accurately characterize the total impact of sudden passenger flow. To enhance the prediction accuracy, we propose a sudden passenger flow prediction model with two factors, the outbound and inbound passenger flows. The wavelet neural network (WNN) model was used to detect the sudden passenger flow, and subsequently, it is optimized by the genetic algorithm (GA), according to two-factor data characteristics. Sudden passenger flow events from 2014 to 2016 in the Beijing Dongsishitiao Station (DS) were used to train and verify the reliability of the prediction model. The optimized WNN results proved better than the conventional WNN, and the error of models based on two factors was significantly smaller than the models with a single-factor.

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Hou, Yafei, Chao Wen, Ping Huang, Liping Fu, and Chaozhe Jiang. "Delay recovery model for high-speed trains with compressed train dwell time and running time." Railway Engineering Science 28, no. 4 (November 24, 2020): 424–34. http://dx.doi.org/10.1007/s40534-020-00225-8.

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AbstractModeling the application of train operation adjustment actions to recover from delays is of great importance to supporting the decision-making of dispatchers. In this study, the effects of two train operation adjustment actions on train delay recovery were explored using train operation records from scheduled and actual train timetables. First, the modeling data were sorted to extract the possible influencing factors under two typical train operation adjustment actions, namely the compression of the train dwell time at stations and the compression of the train running time in sections. Stepwise regression methods were then employed to determine the importance of the influencing factors corresponding to the train delay recovery time, namely the delay time, the scheduled supplement time, the running interval, the occurrence time, and the place where the delay occurred, under the two train operation adjustment actions. Finally, the gradient-boosted regression tree (GBRT) algorithm was applied to construct a delay recovery model to predict the delay recovery effects of the train operation adjustment actions. A comparison of the prediction results of the GBRT model with those of a random forest model confirmed the better performance of the GBRT prediction model.

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Wu, Jianqing, Bo Du, Qiang Wu, Jun Shen, Luping Zhou, Chen Cai, Yanlong Zhai, Wei Wei, and Qingguo Zhou. "A Hybrid LSTM-CPS Approach for Long-Term Prediction of Train Delays in Multivariate Time Series." Future Transportation 1, no. 3 (December 3, 2021): 765–76. http://dx.doi.org/10.3390/futuretransp1030042.

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In many big cities, train delays are among the most complained-about events by the public. Although various models have been proposed for train delay prediction, prior studies on both primary and secondary train delay prediction are limited in number. Recent advances in deep learning approaches and increasing availability of various data sources has created new opportunities for more efficient and accurate train delay prediction. In this study, we propose a hybrid deep learning solution by integrating long short-term memory (LSTM) and Critical Point Search (CPS). LSTM deals with long-term prediction tasks of trains’ running time and dwell time, while CPS uses predicted values with a nominal timetable to identify primary and secondary delays based on the delay causes, run-time delay, and dwell time delay. To validate the model and analyse its performance, we compare the standard LSTM with the proposed hybrid model. The results demonstrate that new variants outperform the standard LSTM, based on predicting time steps of dwell time feature. The experiment results also showed many irregularities of historical trends, which draws attention for further research.

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Solikhin, Solikhin, Septia Lutfi, Purnomo Purnomo, and Hardiwinoto Hardiwinoto. "Prediction of passenger train using fuzzy time series and percentage change methods." Bulletin of Electrical Engineering and Informatics 10, no. 6 (December 1, 2021): 3007–18. http://dx.doi.org/10.11591/eei.v10i6.2822.

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In the subject of railway operation, predicting railway passenger volume has always been a hot topic. Accurately forecasting railway passenger volume is the foundation for railway transportation companies to optimize transit efficiency and revenue. The goal of this research is to use a combination of the fuzzy time series approach based on the rate of change algorithm and the Holt double exponential smoothing method to forecast the number of train passengers. In contrast to prior investigations, we focus primarily on determining the next time period in this research. The fuzzy time series is employed as the forecasting basis, the rate of change is used to build the set of universes, and the Holt's double exponential smoothing method is utilized to forecast the following period in this case study. The number of railway passengers predicted for January 2020 is 38199, with a tiny average forecasting error rate of 0.89 percent and a mean square error of 131325. It can also help rail firms identify future passenger needs, which can be used to decide whether to expand train cars or run new trains, as well as how to distribute tickets.

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Wang, Hao. "A Two-Stage Train Delay Prediction Method Based on Data Smoothing and Multimodel Fusion Using Asymmetry Features in Urban Rail Systems." Wireless Communications and Mobile Computing 2022 (June 22, 2022): 1–10. http://dx.doi.org/10.1155/2022/5188105.

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During the operation of urban rail transit trains, train delays have a great negative impact on subway safety and operation management. It is caused by various external environmental disturbances or internal equipment failures. In order to better grasp the situation of train delays, adjust train operation schedules in time, and improve the quality of rail transit command and transportation services, this paper proposes a two-stage train delay prediction method based on data smoothing and multimodel fusion. In the first stage, the Singular Spectrum Analysis (SSA) method is used to smooth the train operation data, and the smoothed components and residual components are extracted. In the second stage, we use different machine learning methods to train the smoothed data and use the K -nearest neighbor (KNN) method to fuse different trainers. Finally, the predicted values of the smoothed and residual components are combined to improve the overall performance of the train delay prediction model, especially under asymmetry features. The research results show that the prediction accuracy of the two-stage train delay prediction method based on KNN multimodel fusion is significantly better than that of the independent machine learning model.

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Solikhin, Solikhin, Septia Lutfi, Purnomo Purnomo, and Hardiwinoto Hardiwinoto. "A machine learning approach in Python is used to forecast the number of train passengers using a fuzzy time series model." Bulletin of Electrical Engineering and Informatics 11, no. 5 (October 1, 2022): 2746–55. http://dx.doi.org/10.11591/eei.v11i5.3518.

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Train passenger forecasting assists in planning, resource use, and system management. forecasts rail ridership. Train passenger predictions help prevent stranded passengers and empty seats. Simulating rail transport requires a low-error model. We developed a fuzzy time series forecasting model. Using historical data was the goal. This concept predicts future railway passengers using Holt's double exponential smoothing (DES) and a fuzzy time series technique based on a rate-of-change algorithm. Holt's DES predicts the next period using a fuzzy time series and the rate of change. This method improves prediction accuracy by using event discretization. positive, since changing dynamics reveal trends and seasonality. It uses event discretization and machine-learning-optimized frequency partitioning. The suggested method is compared to existing train passenger forecasting methods. This study has a low average forecasting error and a mean squared error.

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Li, Wen-Jun, and Xiao Feng. "Delay propagation cellular automata model based on max-plus algebra for robustness evaluations of non-periodic train operation plans." Modern Physics Letters B 34, no. 21 (May 26, 2020): 2050213. http://dx.doi.org/10.1142/s0217984920502139.

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Based on discrete-event dynamic system theory, train operation events in high-speed railway transportation systems are regarded as the basic elements of these dynamic systems. For non-periodic timetable railways in China, based on a max-plus algebra method, a delay propagation cellular automata model is proposed to evaluate the robustness of high-speed train operation plans. The cellular automata evolution rules that can reproduce the delay propagation state of trains mainly consider train safety headway constraints, passenger transfer constraints, and electric multiple unit (EMU) connection constraints. A simulation analysis of actual cases is performed. The simulation results show that the model can be used to evaluate the robustness of the train operation plan. The numerical results show that in the preparation of train operation plans, the proposed model can predict which trains have significant influences on delays in advance and improve the possibility of reducing the occurrence of delays to maintain high-quality service.

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Laifa, Hassiba, Raoudha khcherif, and Henda Hajjami Ben Ghezalaa. "Train delay prediction in Tunisian railway through LightGBM model." Procedia Computer Science 192 (2021): 981–90. http://dx.doi.org/10.1016/j.procs.2021.08.101.

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Oneto, Luca, Emanuele Fumeo, Giorgio Clerico, Renzo Canepa, Federico Papa, Carlo Dambra, Nadia Mazzino, and Davide Anguita. "Train Delay Prediction Systems: A Big Data Analytics Perspective." Big Data Research 11 (March 2018): 54–64. http://dx.doi.org/10.1016/j.bdr.2017.05.002.

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Lv, Wanjun, Yongbo Lv, Qi Ouyang, and Yuan Ren. "A Bus Passenger Flow Prediction Model Fused with Point-of-Interest Data Based on Extreme Gradient Boosting." Applied Sciences 12, no. 3 (January 18, 2022): 940. http://dx.doi.org/10.3390/app12030940.

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Bus operation scheduling is closely related to passenger flow. Accurate bus passenger flow prediction can help improve urban bus planning and service quality and reduce the cost of bus operation. Using machine learning algorithms to find the rules of urban bus passenger flow has become one of the research hotspots in the field of public transportation, especially with the rise of big data technology. Bus IC card data are an important data resource and are more valuable to passenger flow prediction in comparison with manual survey data. Aiming at the balance between efficiency and accuracy of passenger flow prediction for multiple lines, we propose a novel passenger flow prediction model based on the point-of-interest (POI) data and extreme gradient boosting (XGBoost), called PFP-XPOI. Firstly, we collected POI data around bus stops based on the Amap Web service application interface. Secondly, three dimensions were considered for building the model. Finally, the XGBoost algorithm was chosen to train the model for each bus line. Results show that the model has higher prediction accuracy through comparison with other models, and thus this method can be used for short-term passenger flow forecasting using bus IC cards. It plays a very important role in providing decision basis for more refined bus operation management.

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Han, Baoming, Weiteng Zhou, Dewei Li, and Haodong Yin. "Dynamic Schedule-Based Assignment Model for Urban Rail Transit Network with Capacity Constraints." Scientific World Journal 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/940815.

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There is a great need for estimation of passenger flow temporal and spatial distribution in urban rail transit network. The literature review indicates that passenger flow assignment models considering capacity constraints with overload delay factor for in-vehicle crowding are limited in schedule-based network. This paper proposes a stochastic user equilibrium model for solving the assignment problem in a schedule-based rail transit network with considering capacity constraint. As splitting the origin-destination demands into the developed schedule expanded network with time-space paths, the model transformed into a dynamic schedule-based assignment model. The stochastic user equilibrium conditions can be equivalent to the equilibrium passenger overload delay with crowding penalty in the transit network. The proposal model can estimate the path choice probability according to the equilibrium condition when passengers minimize their perceptive cost in a schedule-based network. Numerical example in Beijing urban rail transit (BURT) network is used to demonstrate the performance of the model and estimate the passenger flow temporal and spatial distribution more reasonably and dynamically with train capacity constraints.

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Bao, Xu, Yanqiu Li, Jianmin Li, Rui Shi, and Xin Ding. "Prediction of Train Arrival Delay Using Hybrid ELM-PSO Approach." Journal of Advanced Transportation 2021 (June 14, 2021): 1–15. http://dx.doi.org/10.1155/2021/7763126.

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In this study, a hybrid method combining extreme learning machine (ELM) and particle swarm optimization (PSO) is proposed to forecast train arrival delays that can be used for later delay management and timetable optimization. First, nine characteristics (e.g., buffer time, the train number, and station code) associated with train arrival delays are chosen and analyzed using extra trees classifier. Next, an ELM with one hidden layer is developed to predict train arrival delays by considering these characteristics mentioned before as input features. Furthermore, the PSO algorithm is chosen to optimize the hyperparameter of the ELM compared to Bayesian optimization and genetic algorithm solving the arduousness problem of manual regulating. Finally, a case is studied to confirm the advantage of the proposed model. Contrasted to four baseline models (k-nearest neighbor, categorical boosting, Lasso, and gradient boosting decision tree) across different metrics, the proposed model is demonstrated to be proficient and achieve the highest prediction accuracy. In addition, through a detailed analysis of the prediction error, it is found that our model possesses good robustness and correctness.

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Corman, Francesco. "Interactions and Equilibrium Between Rescheduling Train Traffic and Routing Passengers in Microscopic Delay Management: A Game Theoretical Study." Transportation Science 54, no. 3 (May 2020): 785–822. http://dx.doi.org/10.1287/trsc.2020.0979.

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In the last decade, optimization models for railway traffic rescheduling mostly focused on incorporating an increasing detail of the infrastructure, with the goal of proving feasibility and quality from the point of view of the managers of the infrastructure (tracks and stations). Different approaches that manage only the passenger flows instead focus more explicitly on the quality of service perceived by the passengers. This paper investigates microscopic railway traffic optimization models and algorithms, merging these two streams of research. In particular, we analyze the characterization of an equilibrium point between the reordering choices of train dispatchers in railway traffic optimization and the route choice of passengers in the available services of the railway transport network. We describe how passenger choice at stations along the route intertwines deeply with the problem of rescheduling trains over tracks and station resources in a very complicated setting that might not exhibit equilibrium points in general. Delaying trains and/or dropping passenger connections and/or giving particular route advice to passengers might influence the behavior of traffic controllers and passengers, determining a trade-off between the delays of trains, weighted by the passenger load, and the travel time of passengers. We study this problem with a game theoretical approach, focusing on the solutions corresponding to Nash equilibria of a game involving passengers and infrastructure managers. The proposed game theoretical approach is able to easily consider information and interdependence of the actions of multiple stakeholders. Computational results based on a real-world Dutch railway network quantify the trade-off between the minimization of train delays and passenger travel times and the performance, stability, and convergence of the equilibrium point given different algorithms and information available. The final aim of this work is to study the impact of effective implementations of railway traffic management and dissemination of information to passengers and operators.

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Gu, Yunyan, Jianhua Yang, Conghui Wang, Guo Xie, and Bingqing Cai. "Prediction Model of Passenger Disturbance Behavior in Flight Delay in Terminal." IOP Conference Series: Earth and Environmental Science 242 (March 30, 2019): 052044. http://dx.doi.org/10.1088/1755-1315/242/5/052044.

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Jiang, Chaozhe, Ping Huang, Javad Lessan, Liping Fu, and Chao Wen. "Forecasting primary delay recovery of high-speed railway using multiple linear regression, supporting vector machine, artificial neural network, and random forest regression." Canadian Journal of Civil Engineering 46, no. 5 (May 2019): 353–63. http://dx.doi.org/10.1139/cjce-2017-0642.

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Accurate prediction of recoverable train delay can support the train dispatchers’ decision-making with timetable rescheduling and improving service reliability. In this paper, we present the results of an effort aimed to develop primary delay recovery (PDR) predictor model using train operation records from Wuhan-Guangzhou (W-G) high-speed railway. To this end, we first identified the main variables that contribute to delay, including dwell buffer time, running buffer time, magnitude of primary delay time, and individual sections’ influence. Different models are applied and calibrated to predict the PDR. The validation results on test datasets indicate that the random forest regression (RFR) model outperforms the other three alternative models, namely, multiple linear regression (MLR), support vector machine (SVM), and artificial neural networks (ANN) regarding prediction accuracy measure. Specifically, the evaluation results show that when the prediction tolerance is less than 1 min, the RFR model can achieve up to 80.4% of prediction accuracy, while the accuracy level is 44.4%, 78.5%, and 78.5% for MLR, SVM, and ANN models, respectively.

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Zhou, Tan, Qiang Gao, Xin Chen, and Zongwei Xun. "Flight Delay Prediction Based on Characteristics of Aviation Network." MATEC Web of Conferences 259 (2019): 02006. http://dx.doi.org/10.1051/matecconf/201925902006.

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In recent years, the increasingly serious flight delay affects the development of the civil aviation. It is meaningful to establish an effective model for predicating delay to help airlines take responsive measures. In this study, we collect three years’ operation data of a domestic airline company. To analyse the temporal pattern of the Aviation Network (AN), we obtain a time series of topological statistics through sliding the temporal AN with an hourly time window. In addition, we use K-means clustering algorithm to analyse the busy level of airports, which makes the airport property value more precise. Finally, we add delay property and use CHAID decision tree algorithm to train the data of an airline for nearly 3 years and use the train?ing model to predicate recent half a year delay. The experimental results show that the accuracy of the model is close to 80%.

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Prokhorchenko, Andrii, Dmytro Gurin, Iryna Lahuta, and Viktoriia Sunytska. "IMPROVEMENT OF THE PROCEDURE FOR SEARCHING RATIONAL TIME RESERVES FOR RECOVERY OF TRAIN MOVEMENT OF DIFFERENT CATEGORIES." Collected scientific works of Ukrainian State University of Railway Transport, no. 193 (October 5, 2021): 44–53. http://dx.doi.org/10.18664/1994-7852.193.2020.229812.

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This article improves the procedure for finding rational time reserves for theresumption of trains of different categories depending on the number of detained trains on the basisof the epidemiological SIR-model. A modified epidemiological SIR-model has been implemented forthe experimental railway line, which allows to take into account the interaction of trains of differentpriorities in the train schedule and the possibility of resumption of delayed trains due to the set timereserve. The adjustment of the delay rate transfer coefficients on the real data of train delays at theline has been performed. The solution of the system of differential equations of the SIR-model isproposed to be performed by the numerical Runge-Kutta method of the 4th order. Experimentalstudies of the impact of passenger train delays on the reliability of the regulatory schedule havebeen conducted. Experimental studies of the impact of passenger train delays on the reliability ofthe regulatory schedule have been conducted. The dependences of the number of detained trains ofdifferent categories on the change in the amount of time reserve for the resumption of trains ofdifferent categories are obtained. Rational time reserves for passenger, suburban and freight trainshave been established. These results were expertly assessed and confirm the adequacy of theobtained decisions in the practice of developing a regulatory schedule of trains at JSCUkrzaliznytsia. The application of the proposed approach will automate the complex process offinding rational values of compensation time in the threads of trains of different categories on therailway line and, as a consequence, increase the level of reliability of the regulatory train schedule.

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Yuan, Yuan, Chunfu Shao, Zhichao Cao, Wenxin Chen, Anteng Yin, Hao Yue, and Binglei Xie. "Urban Rail Transit Passenger Flow Forecasting Method Based on the Coupling of Artificial Fish Swarm and Improved Particle Swarm Optimization Algorithms." Sustainability 11, no. 24 (December 19, 2019): 7230. http://dx.doi.org/10.3390/su11247230.

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Urban rail transit passenger flow forecasting is an important basis for station design, passenger flow organization, and train operation plan optimization. In this work, we combined the artificial fish swarm and improved particle swarm optimization (AFSA-PSO) algorithms. Taking the Window of the World station of the Shenzhen Metro Line 1 as an example, subway passenger flow prediction research was carried out. The AFSA-PSO algorithm successfully preserved the fast convergence and strong traceability of the original algorithm through particle self-adjustment and dynamic weights, and it effectively overcame its shortcomings, such as the tendency to fall into local optimum and lower convergence speed. In addition to accurately predicting normal passenger flow, the algorithm can also effectively identify and predict the large-scale tourist attractions passenger flow as it has strong applicability and robustness. Compared with single PSO or AFSA algorithms, the new algorithm has better prediction effects, such as faster convergence, lower average absolute percentage error, and a higher correlation coefficient with real values.

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Pullagura, Lokaiah. "Analysis of Train Delay Prediction System based on Hybrid Model." Journal of Advanced Research in Dynamical and Control Systems 12, SP7 (July 25, 2020): 2900–2903. http://dx.doi.org/10.5373/jardcs/v12sp7/20202433.

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Jin, Jun, Yan Hui Wang, and Man Li. "Prediction of the Metro Section Passenger Flow Based on Time-Space Characteristic." Applied Mechanics and Materials 397-400 (September 2013): 1038–44. http://dx.doi.org/10.4028/www.scientific.net/amm.397-400.1038.

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At present, the real-time data of the section passenger flow can’t be acquired in the process of urban rail transit operation, which brings some difficulties to the passenger monitoring and controlling. After analyzing the spatio-temporal complexity characters, this paper adopted BP neural network to predict the section passenger flow, which was based on the first three consecutive periods of the in and out station traffic and the next period of the section passenger flow data. Finally, Fuxingmen and Xidan in Beijing urban rail transit were selected, their first three consecutive periods of the in and out traffic are put as the input data, and the next period of the section traffic as the output data, then BP neural network was used to train and predict under MATLAB. The anticipant and the actual output results are well fitted, which proves that the data processing method is effective and the parameters of the BP neural network are reasonable, and it can provide theoretical reference for the transport operators to some extent.

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Du, Xue Dong, and Na Ren. "Railway Passenger Flow Volume Prediction Model Analysis Based on Cobb-Douglas Function." Applied Mechanics and Materials 178-181 (May 2012): 1961–64. http://dx.doi.org/10.4028/www.scientific.net/amm.178-181.1961.

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A prediction model of train passenger flow volume is proposed in this article to help the railway administration's analysis of running strategies. The model is analysed based on industrial economic indexes and Cobb-Douglas theory to make the prediction. The model is illustrated by applying it to a numeral example, and the analysis of the error rate is made for further research.

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Wesonga, Ronald, Fabian Nabugoomu, and Brian Masimbi. "Airline Delay Time Series Differentials." International Journal of Aviation Systems, Operations and Training 1, no. 2 (July 2014): 64–76. http://dx.doi.org/10.4018/ijasot.2014070105.

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Flight delays affect passenger travel satisfaction and increase airline costs. The authors explore airline differences with a focus on their delays based on autoregressive integrated moving averages. Aviation daily data were used in the analysis and model development. Time series modelling for six airlines was done to predict delays as a function of airport's timeliness performance. Findings show differences in the time series prediction models by airline. Differential analysis in the time series prediction models for airline delay suggests variations in airline efficiencies though at the same airport. The differences could be attributed to different management styles in the countries where the airlines originate. Thus, to improve airport timeliness performance, the study recommends airline disaggregated studies to explore the dynamics attributable to determinants of airline unique characteristics.

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Sun, Qi, Fang Sun, Cai Liang, Chao Yu, and Yamin Zhang. "Research on digital flow control model of urban rail transit under the situation of epidemic prevention and control." Smart and Resilient Transport 3, no. 1 (February 15, 2021): 78–92. http://dx.doi.org/10.1108/srt-09-2020-0010.

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Purpose Beijing rail transit can actively control the density of rail transit passenger flow, ensure travel facilities and provide a safe and comfortable riding atmosphere for rail transit passengers during the epidemic. The purpose of this paper is to efficiently monitor the flow of rail passengers, the first method is to regulate the flow of passengers by means of a coordinated connection between the stations of the railway line; the second method is to objectively distribute the inbound traffic quotas between stations to achieve the aim of accurate and reasonable control according to the actual number of people entering the station. Design/methodology/approach This paper analyzes the rules of rail transit passenger flow and updates the passenger flow prediction model in time according to the characteristics of passenger flow during the epidemic to solve the above-mentioned problems. Big data system analysis restores and refines the time and space distribution of the finely expected passenger flow and the train service plan of each route. Get information on the passenger travel chain from arriving, boarding, transferring, getting off and leaving, as well as the full load rate of each train. Findings A series of digital flow control models, based on the time and space composition of passengers on trains with congested sections, has been designed and developed to scientifically calculate the number of passengers entering the station and provide an operational basis for operating companies to accurately control flow. Originality/value This study can analyze the section where the highest full load occurs, the composition of passengers in this section and when and where passengers board the train, based on the measured train full load rate data. Then, this paper combines the full load rate control index to perform reverse deduction to calculate the inbound volume time-sharing indicators of each station and redistribute the time-sharing indicators for each station according to the actual situation of the inbound volume of each line during the epidemic. Finally, form the specified full load rate index digital time-sharing passenger flow control scheme.

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Journal articles on the topic 'Passenger Train Delay Prediction'

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