雾天场景下高速公路通行能力分析及提升策略

This paper studies the freeway traffic capacity in foggy weather. An improvement strategy for tree way traffic capacity in foggy weather is proposed based on vehicle-to-vehicle (V2V) communications. Firstly, a Gipps model calibrated in foggy weather was selected to describe the car-following behavior, and its spacing-speed function was derived to construct the analysis method of freeway capacity. Secondly, the influence of traffic capacity was analyzed from the perspectives of different foggy scenes and speed limit conditions. Under the influence mechanism by foggy weather, we conducted sensitivity analyses on driver reaction time T_n the maximum brake deceleration b_n of the following vehicle, and the estimated maximum brake deceleration b_n-1 of the front vehicle by the follower. Finally, considering the influence of the driver reaction time and the braking deceleration on traffic capacity, a car-following control strategy for improving freeway capacity was proposed based on foggy V2V conditions. The results show that speed limit values of 80 km .h^-1and 100 km .h^-1will lead to the maximum tra ffic capacity under light fog (visibility of 150 meters) and heavy fog (visibility of 60 meters) conditions, respectively. Both conditions of light fog and he any fog have the minimum traffic capacity when 60 km.h^-1is selected as the speed limit value. Compared with the speed limit of 40 km-h^-1, the maximum traffic capacities corresponding to the speed limit of 80 km-h^-1in light fog and 100 km .h^-1in heavy fog increase by 21.83% and 9.68%, respectively. Additionally, the minimum traffic capacities corresponding to the speed Limit of 60 km .h^-1are reduced by 15.88% and 4.61% under light fog and heavy fog conditions, respectively. Traffic capacity improves with the decrease of the driver reaction time and it is positive for capacity improvement when !>,_l>b.l. The proposed control strategy can effectively improve the freeway capacity in foggy weather, and the control strategy has a significant effect on the improvement of traffic capacity with a confidence level of 95%. The average improvement percentage is 44.22% under different foggy scenes and speed limit conditions.