Utilizing support vector machine in real-time crash risk evaluation

Yu, Rongjie; Abdel-Aty, Mohamed

doi:10.1016/j.aap.2012.11.027

Cited by 259 publications

(118 citation statements)

References 23 publications

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“…As suggested by Yu et al [17], variable selection procedure is needed prior to the SVMs estimation. Meanwhile, by selecting variables it is able to solve the over-fitting issues.…”

Section: Random Forestmentioning

confidence: 99%

“…However, the NN models work as a black box, and this strategy may raise over-fitting and local extremum issues [17]. Furthermore, the traditional method to select samples in case-control studies often applies a crash/non-crash ratio as 1:4.…”

Section: Introductionmentioning

confidence: 99%

“…Mujalli et al [19] investigated that data mining algorithms tend to produce lower prediction accuracy over the minority class in an imbalanced dataset. Meanwhile, the SVMs approach works better than NN when dealing with small sample size [17]. The SVMs solve the over-fitting issues by introducing kernel function and try to get the global optimal solution by solving the convex optimization problems.…”

Section: Introductionmentioning

confidence: 99%

“…The SVMs solve the over-fitting issues by introducing kernel function and try to get the global optimal solution by solving the convex optimization problems. Despite the convincing results by utilizing SVMs to evaluate real-time crash risk [17], the SVMs have difficulty in dealing with the imbalanced dataset as well, and additional efforts should be made to optimize the function parameters of the model and preprocess the raw data to achieve a higher prediction accuracy. In general, with the requirement of high-quality traffic flow data, majority of existing models cannot be applied in other regions where limited detectors or surveillance devices are installed on the freeways, though transferability of the models has been validated [8].…”

Section: Introductionmentioning

confidence: 99%

“…Even though, some studies [14] find that a generalized linear model-based approach may lead to biased estimates when the independent variables demonstrate strong nonlinear features. The commonly used techniques in predicting real-time crash likelihood are Neural Network (NN) [3,15,16] and Support vector machines (SVMs) [17,18].…”

Section: Introductionmentioning

confidence: 99%

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Real-time crash prediction on freeways using data mining and emerging techniques

2017

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Recent advances in intelligent transportation system allow traffic safety studies to extend from historic data-based analyses to real-time applications. The study presents a new method to predict crash likelihood with traffic data collected by discrete loop detectors as well as the web-crawl weather data. Matched case-control method and support vector machines (SVMs) technique were employed to identify the risk status. The adaptive synthetic over-sampling technique was applied to solve the imbalanced dataset issues. Random forest technique was applied to select the contributing factors and avoid the over-fitting issues. The results indicate that the SVMs classifier could successfully classify 76.32% of the crashes on the test dataset and 87.52% of the crashes on the overall dataset, which were relatively satisfactory compared with the results of the previous studies. Compared with the SVMs classifier without the data, the SVMs classifier with the web-crawl weather data increased the crash prediction accuracy by 1.32% and decreased the false alarm rate by 1.72%, showing the potential the potential value of the massive web weather data. Mean impact value method was employed to evaluate the variable effects, and the results are identical with the results of most of previous studies. The emerging technique based on the discrete traffic data and web weather data proves to be more applicable on realtime safety management on freeways.

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“…As suggested by Yu et al [17], variable selection procedure is needed prior to the SVMs estimation. Meanwhile, by selecting variables it is able to solve the over-fitting issues.…”

Section: Random Forestmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%