Train operation conflict detection for high-speed railways: a naïve Bayes approach

Li, Jie; Li, Zhongcan; Wen, Chao; Peng, Qiyuan; Huang, Ping

doi:10.1080/23248378.2022.2071346

Cited by 4 publications

(3 citation statements)

References 38 publications

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“…For example, probability distributions were used to interpret the train running time, on which train arrival time can be estimated [18]. Besides, train operation conflict detection and conflict resolution [19] and control actions prediction [20] are typical ways to train operation modeling.…”

Section: Related Workmentioning

confidence: 99%

Dynamic train dwell time forecasting: a hybrid approach to address the influence of passenger flow fluctuations

Pang,

Wang,

Wang

et al. 2023

Rail. Eng. Science

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Train timetables and operations are defined by the train running time in sections, dwell time at stations, and headways between trains. Accurate estimation of these factors is essential to decision-making for train delay reduction, train dispatching, and station capacity estimation. In the present study, we aim to propose a train dwell time model based on an averaging mechanism and dynamic updating to address the challenges in the train dwell time prediction problem (e.g., dynamics over time, heavy-tailed distribution of data, and spatiotemporal relationships of factors) for real-time train dispatching. The averaging mechanism in the present study is based on multiple state-of-the-art base predictors, enabling the proposed model to integrate the advantages of the base predictors in addressing the challenges in terms of data attributes and data distributions. Then, considering the influence of passenger flow on train dwell time, we use a dynamic updating method based on exponential smoothing to improve the performance of the proposed method by considering the real-time passenger amount fluctuations (e.g., passenger soars in peak hours or passenger plunges during regular periods). We conduct experiments with the train operation data and passenger flow data from the Chinese high-speed railway line. The results show that due to the advantages over the base predictors, the averaging mechanism can more accurately predict the dwell time at stations than its counterparts for different prediction horizons regarding predictive errors and variances. Further, the experimental results show that dynamic smoothing can significantly improve the accuracy of the proposed model during passenger amount changes, i.e., 15.4% and 15.5% corresponding to the mean absolute error and root mean square error, respectively. Based on the proposed predictor, a feature importance analysis shows that the planned dwell time and arrival delay are the two most important factors to dwell time. However, planned time has positive influences, whereas arrival delay has negative influences.

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Section: Related Workmentioning

confidence: 99%

Dynamic train dwell time forecasting: a hybrid approach to address the influence of passenger flow fluctuations

Pang,

Wang,

Wang

et al. 2023

Rail. Eng. Science

Self Cite

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“…Researchers in [62] used machine learning [63] in risk score identification. ML algorithms employed on data from routine clinical care of HIV patients such as Logistic Regression (LR) [64], Gaussian Naive Bayes (GNB) [65], Decision Trees (DT) [66], K-nearest Neighbor (KNN) [67], eXtreme Gradient Boosting (XGB) [68], Random Forest (RF), and others were effective to model viral load and CD4-related outcomes [42].…”

Section: Machine Learning For Prep and Hiv Predictionmentioning

confidence: 99%

Machine learning and pre-exposure prophylaxis: A survey

Judith N. Nyakanga

2023

GSC Biol. Pharm. Sci.

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The goal of this paper was to study how machine learning techniques have been applied in PrEP (Pre-exposure prophylaxis) and HIV prediction to identify individuals who are at a higher risk of acquiring HIV infection and to optimize the used of PrEP, which is an effective method of preventing HIV transmission. The results indicate that machine learning has been used to HIV risk, optimizing PrEP use, developing personalized PrEP regimens, and identifying PrEP candidates. In predicting HIV risk, machine learning algorithms have been developed to predict HIV risk based on various factors such as demographic information, sexual behavior and drug use. The models can identify individuals who are at a higher risk of acquiring HIV infection and can be used to target interventions such as PrEP to those who are most in need. Regarding optimizing PrEP use, machine learning has been utilized to optimize PrEP usage by identifying the factors that are associated with adherence to PrEP. These models can help healthcare providers to tailor their interventions to promote PrEP adherence and improve its effectiveness. In addition, machine learning techniques have been used to develop personalized PrEP regimens based on an individual’s HIV risk profile. These models can help healthcare providers to optimize PrEP use and reduce the risk of HIV transmission. It was also established that machine learning models have been used to identify individuals who are most likely to benefit from PrEP. These models can help healthcare providers to target PrEP interventions to those who are likely to benefit from them.

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“…Gaussian Naive Bayes (GNB) is a probabilistic machine learning algorithm commonly used for classification tasks [30]. It is based on Bayes' theorem, which allows for estimating the probability of a particular class given a set of features.…”

Section: G Gaussian Naive Bayesmentioning

confidence: 99%

Preventing Road Accidents Through Early Detection of Driver Behavior Using Smartphone Motion Sensor Data: An Ensemble Feature Engineering Approach

Raza,

Akhtar,

Abualigah

et al. 2023

IEEE Access

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Driver behavior refers to the actions and attitudes of individuals behind the wheel of a vehicle. Poor driving behavior can have serious consequences, including accidents, injuries, and fatalities. One of the main disadvantages of poor driving behavior is the increased risk of road accidents, higher insurance premiums, fines, and even criminal charges. The primary aim of our study is to detect driver behavior early with high-performance scores. The publicly available smartphone motion sensor data is utilized to conduct our study experiments. A novel LR-RFC (Logistic Regression Random Forest Classifier) method is proposed for feature engineering. The proposed LR-RFC method combines the logistic regression and random forest classifier for feature engineering from the motion sensor data. The original smartphone motion sensor data is input into the LR-RFC method, generating new probabilistic features. The newly extracted probabilistic features are then input to the applied machine learning methods for predicting driver behavior. The study results show that the proposed LR-RFC approach achieves the highest performance score. Extensive study experiments demonstrate that the random forest achieved the highest performance score of 99% using the proposed LR-RFC method. The performance is validated using k-fold cross-validation and hyperparameter optimization. Our novel proposed study has the potential to revolutionize the early detection of driver behavior to avoid road accidents.INDEX TERMS Machine learning, driver behavior, sensor data, feature engineering, ensemble learning.

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Train operation conflict detection for high-speed railways: a naïve Bayes approach

Cited by 4 publications

References 38 publications

Dynamic train dwell time forecasting: a hybrid approach to address the influence of passenger flow fluctuations

Dynamic train dwell time forecasting: a hybrid approach to address the influence of passenger flow fluctuations

Machine learning and pre-exposure prophylaxis: A survey

Preventing Road Accidents Through Early Detection of Driver Behavior Using Smartphone Motion Sensor Data: An Ensemble Feature Engineering Approach

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