An Empirical Analysis of Driver Behavioral Changes Following Urban Speed Limit Adjustments

Roads have speed limits to ensure safety by restricting the maximum speed at which vehicles can travel. These speed limits are established based on road environmental conditions to reduce speed disparities between vehicles and decrease the risk of accidents caused by speeding. This study aims to comprehensively analyze how changes in speed limits on urban roads affect driver behavior. In particular, we evaluated the effects of speed limit policies from multiple perspectives by integrating compliance rate analysis from a macroscopic viewpoint with driver behavior analysis from a microscopic perspective. For analysis, we utilized individual operational data from Digital Tachograph (DTG) commercial vehicles across 20,881 road sections in Seoul's standard node-link network. We examined overall policy impacts by analyzing compliance rates by speed limit change type (6,954 sections with decreased limits, 1,620 sections with increased limits, and 8,288 sections with maintained limits), and conducted in-depth analysis of driver behavior changes primarily in sections with decreased speed limits through anomaly detection using the Isolation Forest algorithm, allowing for detailed examination of changes in drivers' speed selection and driving patterns. The results revealed that the larger the downward adjustment of the speed limit, the lower the driver compliance rate, and confirmed a significant relationship between overall compliance rate changes and individual driver behavior changes. These findings suggest that future speed limit policies should adopt a gradual approach that considers the magnitude of change and road characteristics. However, a limitation of this research is that the DTG data used for analysis is limited to commercial vehicles, which may exhibit driving behavior differences compared to general drivers.