Investigating the intra-driver heterogeneity in car following behaviour based on large-scale naturalistic driving study

Intra-driver heterogeneity is defined as transition of driver's behavior between usual and unusual, which is an intrinsic feature of drivers while yet to be extensively explored. This study used a large-scale naturalistic driving data set to investigate intra-driver heterogeneity in car-following. We constructed an IDM-based baseline model to represent a driver's usual behavior; by measuring difference between observed behavior with baseline, unusual behavior was identified. Then, multi-level logit model with random effects was fitted to uncover contributing factors. Among 41 drivers' 1356 trips, intra-driver heterogeneity was identified in 3194 episodes, which accounts for 15% of the time. Within investigated 24 factors, we found that intra-driver heterogeneity was statistically related with vehicle kinematic features, then traffic flow and surrounding environment, but not driver sociodemographics. Being cut in is the most prominent trigger for intra-driver heterogeneity. These findings garner some remarkable insights into improvement of car-following modeling and many other engineering practices.

Automated summary

This study used a large-scale naturalistic driving data set to investigate the occurrence of driver's intra-driver heterogeneity in car-following. The researchers identified unusual behavior by comparing observed behavior with a baseline model and found that intra-driver heterogeneity was statistically related to vehicle kinematic features, traffic flow, and surrounding environment, but not driver sociodemographics. Being cut in was the most prominent trigger for intra-driver heterogeneity. These findings provide valuable insights for improving car-following modeling and other engineering practices.