Abstract
Turbo roundabouts are emerging innovative intersection control designs in the US that enhance traffic safety and operational efficiency. This study investigated driver’s speed choice, acceleration profiles, and behavioral parameters (critical gap and follow-up time) at the first rotor turbo roundabout in the US. Speed profiles revealed that vehicles’ speed at the approach and circulating lanes was affected by approach traffic arrival rates and traffic flow within circulating lanes. In addition, slower speed contributed to no speed-related crashes compared with past speed-related severe crashes before the turbo roundabout installation. Observed critical gaps (4.4–5.8 s) were higher than traditional multi-lane roundabouts (4.5–5.3 s). Follow-up time (3.7–4.7 s) was also higher than traditional multi-lane roundabouts, as drivers merged more cautiously in the circulating lanes owing to the raised lane dividers within the circular lanes. Drivers on the turbo roundabout’s inner lane exhibited longer critical gaps than those on the outer lanes owing to relatively complex merging events on the inner lanes, such as crossing multiple lanes to merge into the inner circulating lane. Merging vehicles’ gap acceptance was affected by the speed of vehicles on circulating lanes, merging vehicle type (e.g., passenger car, truck), and traffic flow rate on circulating lanes. Merging vehicles tended to accept larger gaps owing to the higher speed of vehicles on circulating lanes. As expected, heavy vehicles consistently accepted larger gaps than light vehicles. Conversely, higher traffic flow on circulating lanes caused merging drivers to accept shorter gaps after a longer wait time. Circulating lanes’ flow had a statistically significant effect on critical gaps compared with traditional roundabouts, possibly owing to raised lane dividers and local driving behavior. This study’s findings contribute to understanding the turbo roundabout’s performance in the US and the potential impacts of the turbo roundabout design features on capacity estimation and microsimulation considering US driver behavior.
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