Understanding speeding behavior from naturalistic driving data: Applying classification based association rule mining.
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Speeding is considered as one of the most significant contributing factors to severe traffic crashes. Understanding the associations between trip/driving/roadways features and speeding behavior is crucial for both researchers and practitioners. This research utilized naturalistic driving data collected by the Safety Pilot Model Deployment (SPMD) program and roadway features from a road inventory dataset - Highway Performance Monitoring System (HPMS), provided by the United States Department of Transportation (USDOT), to investigate the hidden rules that associated trip/driving/roadway features with speeding behavior. A classification-based association (CBA) algorithm was adopted to explore the hidden rules from two perspectives of speeding: speeding duration and speeding pattern. Results indicate that the combinations of longer trips (more than 60min), driving on the roadways with a relatively higher functional class are highly associated with longer speeding events (speeding longer than 2min). The moderate speeding events (speeding longer than 2min and longer than 30s) are found highly associated with the combination of driving on roadways with lower functional class, absence of a median and relatively short trip time (less than 30min). The research also found the combinations of driving on roadways with relatively lower functional class, experienced congestion before a speeding event, and the presence of a median is a leading cause that triggers a higher speeding pattern (speeding more than 5mph above the speed limit). Furthermore, the moderate speeding pattern (speeding more than 1mph above the speed limit and less than 5mph of the speed limit) is associated with the combinations of factors like experiencing congestion before a speed event, driving on roadways with higher functional class and a relatively shorter trip (less than 30min). The findings can help practitioners understand the composite effect of these factors more comprehensively and provide corresponding countermeasures to mitigate the negative consequences of speeding wherever possible. These can also help in calibrating driver behavior parameters for transportation-related simulation tools.