A two-layer modelling framework for predicting passenger flow on trains: A case study of London underground trains

Qian, Zhang; Liu, Huan; Spurgeon, Sarah K.; Yu, Dingli

doi:10.1016/j.tra.2021.07.001

Cited by 9 publications

(4 citation statements)

References 89 publications

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“…The global model predicts the overall situation, while the local model predicts changes in passenger flow due to specific factors, such as events and weather. [6]. Some studies have focused on pedestrian behavior.…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction-based early warning system for overflow of people in metro stations

2024

ACE

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As cities rapidly urbanize, handling the management of crowded transportation hubs, notably metro stations, has become an immediate concern. High passenger traffic can lead to severe risks, including stampedes. While many past passenger flow forecasting systems aim to enhance prediction accuracy, the inherently noisy nature of passenger flow data makes it challenging for existing technologies to provide stable and precise predictions, making passenger flow management based solely on these models risky. This paper introduces a novel system that mitigates this risk by integrating a predictive model with managerial methods. The proposed management framework formulates a unique model for each station, determining a risk deviation coefficient grounded in the station's historical prediction accuracy. Station management is then holistically executed based on this coefficient and the predictive model. This paper employs the LSTM model for station-specific passenger flow prediction and defines the risk-related parameter , taking prediction accuracy into account. This adjusted LSTM prediction is then utilized to proactively streamline resource allocation, targeting improved passenger safety and overall station experience.

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Section: Literature Reviewmentioning

confidence: 99%

Prediction-based early warning system for overflow of people in metro stations

2024

ACE

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“…According to the above, this paper uses short distance, medium distance and long distance to generate the total distance S in the algorithm, and then performs a large number of 3-stage, 4-stage and 5-stage train speed running curves for it respectively (namely N=3, 4,5) to verify the effectiveness of our algorithm in optimising the speed curve.…”

Section: Case Studiesmentioning

confidence: 99%

“…Therefore, urban rail-based metro systems are the most effective solution to relieve traffic pressure. Although many major cities have been improving their subway service levels, the subway system is not improving fast enough to meet the growing demand for passenger transportation, especially in big cities like New York, Beijing and London [4,5]. So, with such a vast passenger flow, subway trains operation system and efficiency deserve to be studied in depth, especially the intelligent transportation system bred by combining the subway system with today's rapidly developing artificial intelligence technology.…”

Section: Introductionmentioning

confidence: 99%

Rapid Algorithm for Generating and Selecting Optimal Metro Train Speed Curves Based on Alpha Zero and Expert Experience

CHEN,

WU,

et al. 2024

PROMTT

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According to the current research status of urban rail transit’s fully automatic operation (FAO), the train driving speed curves are usually obtained through simulation and calculation. The train driving speed curves obtained by this method not only have low efficiency but also are not suitable for complex road conditions. Inspired by AlphaZero, a reinforcement learning algorithm that utilised vast amounts of artificial data to defeat AlphaGo, an AI Go program, this paper investigates and analyses methods for rapidly generating a large number of speed curves and selecting those with superior performance for train operation. Firstly, we use the powerful third-party library in Python as the basis, combined with the idea of AlphaZero, to produce artificial speed curves for metro train driving. Secondly, we set relevant parameters with reference to expert experience to quickly produce massive reasonable artificial speed curves. Thirdly, we analysed relevant indicators such as energy consumption, running time error and passenger comfort to select some speed curves with better comprehensive performance. Finally, through the many observations with different running distances and different speed limits, we found that the speed curves produced and selected by our algorithm are more productive, diverse and conducive to the research of train driving operation than the actual data from traditional manual driving and ATO (automatic train operation) system.

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“…The passenger flow of URT is increasing rapidly, and the problem of over saturation of passenger flow density during peak hours has become increasingly prominent 1 . The implementation of passenger flow prediction can effectively alleviate congestion in URT systems and enhance service quality 2 .…”

Section: Introductionmentioning

confidence: 99%

A short term passenger flow prediction method for urban rail transit considering station classification

Wang,

Xu,

Chen

2024

International Conference on Smart Transportation and City Engineering (STCE 2023)

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Accurately understanding the timing characteristics of urban rail transit (URT) passenger flows is helpful in improving the operational efficiency. A deep learning-based approach is used in prediction in URT systems, leveraging the timing characteristics of stations to classify them. Firstly, the dynamic time warping (DTW) is employed to quantify the dissimilarities, while the K-means algorithm is utilized to categorize stations based on the timing attributes of passenger flows. Secondly, in order to mitigate the impact of data noise, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) approach decomposes passenger flow data. Finally, an improved time series transformer model is proposed for deep learning prediction of passenger flows at different types of stations. The effectiveness of the approach is confirmed through the analysis of Xi'an Metro's passenger flow data, revealing the presence of four station classifications: occupation-residential balance type, business office type, leisure and entertainment type, and dense residential type. Compared with the Auto Regressive Integrated Moving Average (ARIMA) model, the Support Vector Regression (SVR) model, the Long Short-Term Memory (LSTM) model and Transformer, Mean Absolute Error (MAE) of prediction results in different types of stations is reduced by 8.94%~34.32%, Root Mean Square Error (RMSE) is reduced by 8.30%~36.50%, and Mean Absolute Percentage Error (MAPE) is reduced by 13.77%~38.84%. The accuracy of prediction for different stations using the proposed method is superior to the benchmark models.

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A two-layer modelling framework for predicting passenger flow on trains: A case study of London underground trains

Cited by 9 publications

References 89 publications

Prediction-based early warning system for overflow of people in metro stations

Prediction-based early warning system for overflow of people in metro stations

Rapid Algorithm for Generating and Selecting Optimal Metro Train Speed Curves Based on Alpha Zero and Expert Experience

A short term passenger flow prediction method for urban rail transit considering station classification

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