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The research work reported here investigates driving behavior under mixed traffic conditions on high-speed, multilane highways. With the involvement of multiple vehicle classes, high-resolution trajectory data is necessary for exploring vehicle-following, lateral movement, and seeping behavior under varying traffic flow states. An access-controlled, mid-block road section was selected for video data collection under varying traffic flow conditions. Using a semi-automated image processing tool, vehicular trajectory data was developed for three different traffic states. Micro-level behavior such as lateral placement of vehicles as a function of speed, instant responses, vehicle-following behavior, and hysteresis phenomenon were evaluated under different traffic flow states. It was found that lane-wise behavior degraded with increase in traffic volume and vehicles showed a propensity to move towards the median at low flow and towards the curb-side at moderate and heavy flows. Further, vehicle-following behavior was also investigated and it was found that with increase in flow level, vehicles are more inclined to mimic the leader vehicle’s behavior. In addition to following time, perceiving time of subject vehicle for different leading vehicles was also evaluated for different vehicle classes. From the analysis, it was inferred that smaller vehicles are switching their leader vehicles more often to escape from delay, resulting in less following and perceiving time and aggressive gap acceptance. The present research work reveals the need for high-quality, micro-level data for calibrating driving behavior models under mixed traffic conditions.

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