A Review of Data Analytic Applications in Road Traffic Safety. Part 2: Prescriptive Modeling

Hu, Qiong; Cai, Miao; Mohabbati-Kalejahi, Nasrin; Mehdizadeh, Amir; Yazdi, Mohammad Ali Alamdar; Vinel, Alexander; Rigdon, Steven E.; Davis, Karen; Megahed, Fadel M.

doi:10.3390/s20041096

Cited by 19 publications

(11 citation statements)

References 63 publications

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“…The paper also reviewed the statistical and machine learning models used for crash risk modelling. Hu et al [3] categorized the optimization and prescriptive analytic models that focus on minimizing crash risk. Ziakopoulos et al [4] critically reviewed the existing literature on different spatial approaches that include dimension of space in its various aspects in their analyses for road safety.…”

Section: Literature Reviewmentioning

confidence: 99%

Road Traffic Accidents Injury Data Analytics

Nour¹,

Naseer²,

Alkazemi³

et al. 2020

IJACSA

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Section: Literature Reviewmentioning

confidence: 99%

Road Traffic Accidents Injury Data Analytics

Nour¹,

Naseer²,

Alkazemi³

et al. 2020

IJACSA

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“…Gong [17,18] considered parameters such as materials, structure, traffic, and environment and successfully developed the Mechanistic-Empirical Pavement Design Guide (MEPDG) deep neural network model and GBM method for pavement rutting prediction, and the accuracy was improved. Hu [19] obtained risk indicators from a neural network prediction model and found that the application of data analysis improved the similarity of the predictive performance of the overall pavement safety level. Wang [20] et al proposed a training method to automatically determine the parameters of network structure for the deep belief network (DBN) with transfer learning (TL-GDBN).…”

Section: Introductionmentioning

confidence: 99%

Prediction of Highway Tunnel Pavement Performance Based on Digital Twin and Multiple Time Series Stacking

Zhang

et al. 2020

Advances in Civil Engineering

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The existing pavement performance prediction methods are limited to single-factor predictions, which often face the challenges of high cost, low efficiency, and poor accuracy. It is difficult to simultaneously solve the temporal, spatial, and exogenous dependencies between pavement performance data and maintenance, the service life of highways, the environment, and other factors. Digital twin technology based on the building information modeling (BIM) model, combined with machine learning, puts forward a new perspective and method for the accurate and timely prediction of pavement performance. In this paper, we propose a highway tunnel pavement performance prediction approach based on a digital twin and multiple time series stacking (MTSS). This paper (1) establishes an MTSS prediction model with heterogeneous stacking of eXtreme gradient boosting (XGBoost), the artificial neural network (ANN), random forest (RF), ridge regression, and support vector regression (SVR) component learners after exploratory data analysis (EDA); (2) proposes a method based on multiple time series feature extraction to accurately predict the pavement performance change trend, using the highway segment as the minimum computing unit and considering multiple factors; (3) uses grid search with the k-fold cross validation method to optimize hyperparameters to ensure the robustness, stability, and generalization ability of the prediction model; and (4) constructs a digital twin for pavement performance prediction to realize the real-time dynamic evolution of prediction. The method proposed in this study is applied in the life cycle management of the Dalian highway-crossing tunnel in Shanghai, China. A dataset covering 2010–2019 is collected for real-time prediction of the pavement performance. The prediction accuracy evaluation shows that the mean absolute error (MAE) is 0.1314, the root mean squared error (RMSE) is 0.0386, the mean absolute percentage error (MAPE) is 5.10%, and the accuracy is 94.90%. Its overall performance is better than a single model. The results verify that the prediction method based on digital twin and MTSS is feasible and effective in the highway tunnel pavement performance prediction.

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“…The paper likewise evaluated the factual and AI models utilized for crash risk displaying. [3] classified the enhancement and prescriptive logical models that attention on limiting accident risk. Ziakopoulos.…”

Section: IImentioning

confidence: 99%

Road Accident Analysis Using Machine Learning

Kanchi¹

2022

IJRASET

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Road accidents are one among the key concerns within the country. The economic impact of traffic accidents cost many billions of dollars for US citizens per annum. Reducing the traffic accidents has always been a challenge. The most important objective of the project is to investigate what are the foundations for mishaps like effect of precipitation or any natural elements and to predict the probability of road accidents supported the present accidents records. By performing data visualization, we are able to identify the car accidents zones, factors effecting accident severity, and locations etc. Identifying the key factors of how these accidents occur can help in implementing well informed actions. Road safety analysts following up on street accidents information have seen outcome in street auto collisions examination through the applying information logical methods, however, little headway was the expectation of street injury. This report says progressed information investigation strategies to anticipate injury seriousness levels and assesses their presentation. The review utilizes prescient demonstrating methods to recognize chance and key factors that adds to mishap seriousness. The review utilizes openly accessible information from US branch of transport that covers the sum from 2005 to 2019. The report presents a methodology which is adequately general so are frequently applied to various informational collections from different nations. The outcomes recognized that tree based methods like Extreme Gradient Boosting beat relapse covers, as Artificial Neural Network (ANN) also to the paper, recognizes fascinating connections were recognized concepts related with nature of information.

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A Review of Data Analytic Applications in Road Traffic Safety. Part 2: Prescriptive Modeling

Cited by 19 publications

References 63 publications

Road Traffic Accidents Injury Data Analytics

Road Traffic Accidents Injury Data Analytics

Prediction of Highway Tunnel Pavement Performance Based on Digital Twin and Multiple Time Series Stacking

Road Accident Analysis Using Machine Learning

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