Title

Linking Crash Occurrence To Real-Time Weather Conditions And Avi Traffic Data: Ensemble Data-Mining Approach

Keywords

Active Traffic Management (ATM); Adverse Weather; Automatic Vehicle Identification (AVI); Ensemble data-mining; Mountainous Freeways; Real-time Crash Analysis

Abstract

As advances in traffic detection technology help to operate roads more efficiently and as the authorities are shifting toward these advanced non-intrusive systems, the interest in incorporating safety into traffic management systems has also grown accordingly. The data that are collected from such systems is one of the greatest assets that should be utilized appropriately to maximize the benefit for the roadway authority as well as for the road users. Buried within this vast amount of data is useful information that could make a significant difference in how these roads are managed and operated. Data mining techniques are known for their superior performance in classification and prediction. This paper examines the usefulness of traffic data collected from Automatic Vehicle Identification (AVI) system, weather data and roadway geometry in real-time crash analysis utilizing data mining methods. All data mining models were found to outperform the classical ones. Artificial Neural Network and decision trees were found to provide the best accuracy in terms of the area under the ROC curve, lift chart and misclassification error for both training and validation datasets. Moreover, using data mining ensemble technique to combine the results from the best models further enhanced the prediction accuracy.

Publication Date

1-1-2012

Publication Title

19th Intelligent Transport Systems World Congress, ITS 2012

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

Socpus ID

84896997957 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84896997957

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