A Traffic Crash Warning Model for BOT E-Tolling Operations Based on Predictions Using a Data Association Framework.

As a result of the increasing use of artificial intelligence technology in transportation, numerous real-time crash prediction techniques have been developed. In the context of highway traffic management, machine learning models and classifiers are used to analyze electronic toll collection (ETC) and vehicle detector (VD) data to predict crash occurrences. However, traffic accidents are influenced by multiple factors, such as traffic speed differences, traffic density, and weather conditions, and direct associations may not exist between sensor data and crash incidents. Therefore, data integration and association methods must be used to examine ETC and VD data through traffic flow theories, to extract key data from datasets and to facilitate model training. In this study, a data association method and framework combined with deep learning was proposed to construct a crash prediction and warning model for national highways in Taiwan. The results revealed a model accuracy of 94%, indicating that the model had a low error rate and was suitable for the prediction of traffic accidents. Overall, this study provides referential data for the Freeway Bureau of Taiwan to conduct comprehensive assessments and develop strategies for crash prevention. [ABSTRACT FROM AUTHOR]