Effects of configuration elements and traffic flow conditions on Lane-Changing rates at the weaving segments

The objective of this study is to evaluate the effects of configuration elements and traffic flow conditions on lane-changing rates at weaving segments. To achieve this, data collected from ten weaving segments, utilizing DJI drones and automatic trajectory extraction technology, was analyzed in the study. The procedure introduced in Highway Capacity Manual (2016) was initially employed to estimate the expected lane-changing rates and identify existing issues. Subsequently, multilevel generalized linear regression models were developed to investigate the relationships between lane-changing rates and the influencing factors. Restricted Maximum Likelihood along with Empirical Bayes was applied to estimate the model parameters. The model results indicate that the Major-Weave II weaving segment exhibits the least lane changes when compared to the other three types of weaving segments under the same traffic flow conditions. Additionally, the types of weaving segments influence how geometric factors and traffic flow conditions affect lane-changing rates. The length and width of a weaving segment have opposite influences on the lane-changing rate. Longer lengths increase traffic flow complexity while more lanes provide additional space for vehicles and reduce complexity. A wider auxiliary road connected to the weaving segment brings more ramp-through vehicles and results in more lane changes due to disturbances in traffic flows. Poor level of service (LOS) typically indicates high lane-changing rates. However, the lane-changing rate rises sharply with increasing volume under LOS B. This may be because the large gaps among vehicles under LOS B prompt vehicles to make more lane changes in search of a better driving environment. The findings of this study can provide valuable insights for designing weaving segments, implementing operational policies, and developing automated driving systems (ADS) for connected and automated vehicles (CAVs) to reduce lane changes, enhance traffic efficiency, and improve safety.