A dynamic speed guidance method at on-ramp merging areas of urban expressway considering driving styles

Dynamic speed guidance for vehicles in on-ramp merging zones is instrumental in alleviating traffic congestion on urban expressways. To enhance compliance with recommended speeds, the development of a dynamic speed-guidance mechanism that accounts for heterogeneity in human driving styles is pivotal. Utilizing intelligent connected technologies that provide real-time vehicular data in these merging locales, this study proposes such a guidance system. Initially, we integrate a multi-agent consensus algorithm into a multi-vehicle framework operating on both the mainline and the ramp, thereby facilitating harmonized speed and spacing strategies. Subsequently, we conduct an analysis of the behavioral traits inherent to drivers of varied styles to refine speed planning in a more efficient and reliable manner. Lastly, we investigate a closed-loop feedback approach for speed guidance that incorporates the driver's execution rate, thereby enabling dynamic recalibration of advised speeds and ensuring fluid vehicular integration into the mainline. Empirical results substantiate that a dynamic speed guidance system incorporating driving styles offers effective support for human drivers in seamless mainline merging.