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• W8967978 abstract "Underground loop detectors are one of the most common traffic surveillance apparatus on freeways. Traffic data obtained from these detectors are used for various ITS (Intelligent Transportation Systems) applications such as travel time estimation and incident detection. In the recent past, however, researchers have been interested in proactive applications of these data. These proactive applications primarily involve real-time crash risk assessment based on analysis of traffic surveillance data observed prior to historical crashes. In this study, we have analyzed the crash data from five freeway sections in the Utrecht region of the Netherlands to identify the traffic conditions significantly associated with crash occurrences. Random Forest, a data mining methodology employing multiple classification trees, would be used to analyze the data and identify traffic parameters significantly associated with the binary variable representing crash vs. non-crash. It was found that the turbulence in traffic speeds is related to real-time crash likelihood. The results are promising in that they show the potential of transferring the proactive traffic management approach proposed by Pande and Abdel-Aty (2007) for Interstate-4 in Orlando, FL to freeways in the Netherlands. INTRODUCTION Underground loop detectors are used to continuously collect real-time traffic information from highways (primarily uninterrupted flow facilities such as freeways) around the World. Data from these detectors are used for various ITS (Intelligent Transportation Systems) applications such as travel time estimation and incident detection. Recently, however, the focus has been shifting towards proactive applications of these data including the development of real-time crash risk assessment models. These models are developed based on analysis of traffic surveillance data observed prior to historical crashes. For example, Golob and Recker (2004) and Pande and Abdel-Aty (2006 a) related crash patterns to traffic c1ata from single loop detector stations in California and dual loop detector stations in Florida, respectively. These models can differentiate crash prone traffic conditions from non-crash (or 'normal') traffic conditions in real-time. They can potentially be used to issue warnings or implement traffic management strategies ahead in time for reducing measure(s) of crash risk and avoid an imminent crash. Despite the extensive analysis presented in these studies a critical issue that remains to be addressed is transferability of such an approach especially to locations with varying traffic surveillance systems. The archived freeway traffic data may vary in terms of collected traffic parameters, level of aggregation, and/or spacing of loop detectors. In this study we would explore a sample of crash as well as non-crash data from five freeways in the Utrecht region of the" @default.
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• W8967978 date "2008-01-01" @default.
• W8967978 modified "2023-09-27" @default.
• W8967978 title "Exploration of Crash Prone Traffic Conditions on Freeways in the Netherlands Using Random Forests" @default.
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