Predicting Work Zone Collision Probabilities via Clustering: Application in Optimal Deployment of Highway Response Teams

Sekuła, Przemysław; Laan, Zachary Vander; Sadabadi, Kaveh Farokhi; Skibniewski, Mirosław J.

doi:10.1155/2018/3179207

Cited by 3 publications

(1 citation statement)

References 70 publications

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“…Other studies employed clustering analysis for roadway crashes and safety projects [43][44][45]. Similarly, Sekuła et al [46] proposed a clustering approach to predict the probability of a collision occurring in the proximity of planned road maintenance operations (i.e., work zones). Different other studies also concluded data mining techniques are more advanced and better than traditional statistical techniques [47][48][49][50][51].…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluating the Effect of Roadway and Development Factors on the Rural Road Safety Risk Index

Zargari

Hassanpour

2020

Advances in Civil Engineering

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As roadway and development factors are identified as the most effective factors contributing to road traffic accidents, investigating these factors could lead to reducing the accident frequency rate. However, previous works focused on investigating the effect of roadway factors on the accident frequency rate using statistical analysis. The present study aimed to evaluate the effect of roadway and development factors on the accident frequency rate using ANOVA and Chi-square tests on a rural road. Secondly, it aimed to develop a rural road safety risk index based on K-means clustering and Gaussian models. The findings indicated that the operating speed and the differences between posted speed limits and the operating speed are the pivotal influencing factors on the accident frequency rate. Moreover, clustering analysis of the roadway and development factors on the two-lane, two-way road of Borujerd-Khorramabad indicated six clusters which were identified as highly, relatively highly, moderately, relatively lowly, lowly risky, and not risky (safe) clusters. Regarding clusters, the accident frequency rate increased by decreasing the difference between the posted speed limits and the operating speed from the safe cluster. In addition, the risky index model based on the Gaussian model showed that the average reducing factor of accident frequency rate reached 0.99 by increasing per km/hr in the difference between the posted speed limits and the operating speed among low risky and safe clusters, while it was equal to 1.17 in risky and unsafe clusters. The comparison of the clusters revealed that accident occurrence probability in risky clusters was more than the ones in low risky or safe clusters. Therefore, the maximum and minimum values of the safety risk index were observed in the sixth and the third clusters, respectively.

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Section: Literature Reviewmentioning

confidence: 99%

Evaluating the Effect of Roadway and Development Factors on the Rural Road Safety Risk Index

Zargari

Hassanpour

2020

Advances in Civil Engineering

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Unsupervised Learning-based Artificial Bee Colony for minimizing non-value-adding operations

Cheng

Pourhejazy

Ying

et al. 2021

Applied Soft Computing

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Using Deep Neural Networks and Big Data to Predict Microscopic Travel Time in Work Zones

Morshedzadeh,

Gargoum,

Gargoum

2024

Transportation Research Record: Journal of the Transportation R

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Traffic management in work zones is a challenging task that requires a balance between creating a safe environment for workers and minimizing traffic delays. Accurately predicting travel times through work zones is essential for dynamic traffic management and reducing congestion. However, complex interrupted traffic patterns in work zones (also known as construction zones) make this challenging compared with regular traffic congestion and free-flow conditions. To address this complexity, the paper develops a data-driven deep feed-forward artificial neural network to forecast under-construction travel times in work zones using an integrated data set of almost half a million observations. The variables considered in the neural network include the work zone characteristics, road design features, weather information, and traffic flow information. The raw data set comprised approximately 15 million travel time observations collected at 674 work zones. After cleaning and preprocessing the data and applying feature engineering techniques, the raw data set was reduced to 81 work zones spread along a 700 km corridor between the western borders of Alberta and Vancouver, British Columbia. The data were split into training and test data using a 90:10 ratio. Training data were used to develop a 27-input neural network with four hidden layers. After validating the test data, the neural network achieved a root mean squared error of 0.150 min and an R-squared score of 0.945, indicating a high accuracy level in estimating the under-construction travel time.

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Predicting Work Zone Collision Probabilities via Clustering: Application in Optimal Deployment of Highway Response Teams

Cited by 3 publications

References 70 publications

Evaluating the Effect of Roadway and Development Factors on the Rural Road Safety Risk Index

Evaluating the Effect of Roadway and Development Factors on the Rural Road Safety Risk Index

Unsupervised Learning-based Artificial Bee Colony for minimizing non-value-adding operations

Using Deep Neural Networks and Big Data to Predict Microscopic Travel Time in Work Zones

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