Patterns of near-crash events in a naturalistic driving dataset: Applying rules mining

Abstract

This study aims to explore the associations between near-crash events and road geometry and trip features by investigating a naturalistic driving dataset and a corresponding roadway inventory dataset using an association rule mining method - the Apriori algorithm. To provide more insights into near-crash behavior, this study classified near-crash events into two severity levels: trivial near-crash events (-7.5 g ≤ deceleration rate ≤ -4.5 g) and non-trivial near-crash events (≤-7.5 g). From the perspective of descriptive statistics, the frequency of the itemsets, a set of categories of various variables, generated by the Apriori algorithm suggests that near-crash events are highly associated with several factors, including roadways without access control, driving during non-peak hours, roadways without a shoulder or a median, roadways with the minor arterial functional class, and roadways with a speed limit between 30 and 60 mph. By comparing the frequency of the occurrence of the itemset during trivial and non-trivial near-crash events, the results indicate that the length of the trip is a strong indicator of the near-crash event type. The results show that non-trivial near-crash events are more likely to occur if the trip is longer than 2 h. After applying the association rule mining algorithm, more interesting patterns for the two near-crash events were generated through the rules. The main findings include: 1) trivial near-crash events are more likely to occur on roadways without a median and shoulder that have a relatively lower functional class; 2) relatively higher functional roadways with relatively wide medians and shoulders could be an intriguing combination for non-trivial near-crash events; 3) non-trivial near-crash events often occur on long trips (more than 2 h); 4) congestion on roadways that have a lower functional class is a dominant rule associating with the high frequency of non-trivial near-crash events. This study associates near-crash events and the corresponding road geometry and trip features to provide a unique understanding of near-crash events.

Keywords: Association rule; Geometric feature; HPMS; Naturalistic driving data; Near-crash; SPMD.