Effects of Visual Active Deceleration Devices on Controlling Vehicle Speeds in a Long Downhill Tunnel of an Expressway

Abstract

Because the inside of a road tunnel is a closed strip, the driving environment changes suddenly at the tunnel entrance and exit, which is why accidents occur more frequently in tunnels than elsewhere. Based on visual psychology, reverse-perspective-illusion deceleration lines (RPIDLs) and visual-intervention deceleration devices (VIDDs) were designed to control vehicle speeds near the entrance and in the middle of a tunnel, respectively. Then, to assess the speed-controlling performance of the RPIDLs and VIDDs for vehicles in a long downhill tunnel, laser velocimeters were used to measure the vehicle speeds at different observation points in the tunnel before and after implementing the RPIDLs and VIDDs. The results show that the RPIDLs and VIDDs decreased the average vehicle speed and controlled its dispersion effectively. The 60 m RPIDLs improved the traffic safety in the tunnel without lessening the passing ability therein. The slash-line VIDD had no obvious effect on the maximum vehicle speeds in the middle of the tunnel. Both the 90° fold-line VIDD and the four-yellow–four-white VIDD decreased the maximum vehicle speeds significantly. The 60 m RPIDLs and the 90° fold-line VIDD gave the best deceleration effect on vehicles near the entrance and in the middle of the tunnel, respectively, and controlled the average speed and its dispersion effectively.

Visual Recognition Analysis of Optically Long Tunnels: Interaction of Dynamic Vision and Visual Perception

“…The white-hole effect at the exit area of a tunnel is a typical manifestation of a driver’s visual response limitations ( 36 , 37 ), which result from the drastic changes in the visual environment. The white-hole effect in the exit area of a tunnel is a typical cause of drivers’ misperceptions of the driving environment and misjudgments of the length, width, and height of a tunnel, resulting in errors in the perception of the tunnels’ profile.…”

Analysis of the Causes of Traffic Accidents and Identification of Accident-Prone Points in Long Downhill Tunnel of Mountain Expressways Based on Data Mining

“…Rule (1): If the radius of the flat curve is (0), ( 1), ( 3), the week is ( 4), (7), the slope length is (0), (3), then the maximum injury degree is (1); Rule (1) contains 55 samples with 100% confidence; Rule (2): If the radius of the flat curve is (0), ( 1), ( 3), the week is (1), then the maximum injury degree is (1); Rule (2) contains 86 samples with 70.93% confidence; Rule (3): If the radius of the flat curve is (0), ( 1), ( 3), the week is ( 6), (3), the truck ratio is (0), (3), then the maximum injury degree is (0); Rule (3) contains 55 samples with 72.73% confidence; Rule (4): If the radius of the flat curve is (0), ( 1), (3), the week is (4), (3), then the maximum injury degree is (0); Rule (4) contains 51 samples with 92.16% confidence; Rule (5): If the radius of the flat curve is (4), then the maximum injury degree is (1); Rule (5) contains 75 samples with 73.33% confidence.…”

“…The accident probability of the long downhill sections of mountain expressway tunnels is closely related to the busyness of the tunnels [6], particularly in the long downhill tunnel sections of the mountain expressway with heavy traffic. If the treatment method and time are not correct, even less serious minor accidents will cause road congestion and potential regional traffic paralysis, resulting in the loss of personnel and public property [7,8]. The frequency distribution of traffic accidents in the long downhill tunnel sections of the mountain expressway is very dense [9].…”