Evaluation of Design Method for Highway Adjacent Tunnel and Exit Connection Section Length Based on Entropy Method

Abstract

With the continuous construction of transportation infrastructure, intersection nodes have been increasing rapidly, bringing growing numbers of tunnel- and exit-adjacent sections (TEAS) in mountain expressways in China. With the complex variation in the surrounding environment, drivers always face congestion and confusion on tunnel and the exit connecting sections (TECS) without adequate length, meanwhile excessively long TECS create detours. To better provide a sustainable design strategy for TEAS, based on a certain section of expressway in Shaanxi, China, this paper establishes a theoretical calculation model through analysis. The characteristics of traffic flow and drivers' light adaptation at tunnel exit are obtained through data collection and driving tests, and the length requirements of the tunnel and exit connecting sections (TECS) are discussed. A VISSIM microscopic simulation model is also built under various design schemes and entropy-based multi-attribute decision making (EBMADM) is used to objectively calculate the weights of the four selected evaluation indexes. Then, the design schemes of the TECS with different lengths have been comprehensively evaluated. The results show the match between the evaluation results of EBMADM with theoretical calculations under existing traffic conditions, which proves the rationality of EBMADM in such problems. For more cases, the results of the EBMADM evaluation show a positive correlation between the length of TECS for the best performing design scheme with traffic volume and diverging ratio.

Keywords: MADM; TEAS; VISSIM; entropy method; theoretical calculations.

Figure 1

Diagram of TECS, in which complete signs typically cannot be set.

Figure 2

TEAS on an expressway under construction in a mountainous area, taken with an unmanned aerial vehicle (UAV) by the author, in which the town is built on the mountain.

Figure 3

The location of Expressway A and data collection points.

Figure 4

Trend of traffic volume on Expressway A, where X denotes the hour and Y denotes traffic volume on the two-lane TECS. For example, the data of “Hour 8” indicate traffic volume of both lanes on the section between 8:00 a.m. and 9:00 a.m.

Figure 5

The process of data collection at the site.

Figure 6

The detailed position of the data collection point on TECS. Radar 1 is set at the tunnel exit to collect vehicle speed after the tunnel, and Radar 2 is set at the start of the taper to collect vehicle speed before the taper, while the roadside radar is set 100 to 200 m before the taper to collect the time headway.

Figure 7

Vehicle speed after the tunnel on TECS, where X denotes the type of vehicle and distance from the tunnel exit and Y denotes vehicle speed. (a) Fast lane; (b) Curb lane.

Figure 8

Vehicle speed before the taper on TECS, where X denotes the type of vehicle and distance from the tunnel exit and Y denotes vehicles speed. (a) Fast lane; (b) Curb lane.

Figure 9

Time headway distribution in the curb lane on the TECS. Sections A to D of the X axis represent the data collection points on the four TECS in Figure 6, respectively, in the order from south to north, and Y denotes time headway on the curb lane.

Figure 10

Vehicle trajectory after Kalman filter processing; the black shows the original data while the red shows the processed. (a) Horizontal position, where X denotes time series of lane changing and Y denotes horizontal position of the vehicle with 0 marked as the vehicle centerline coinciding with the boundary of fast and curb lane; (b) Lateral speed, where X denotes time series of lane changing and Y denotes the vehicle’s lateral speed, with positive values indicating the vehicle moves to the right.

Figure 11

Distribution of diverging vehicle trajectory for rightward lane change. X denotes time series of lane changing with 0 marked as the moment when vehicle centerline coincides with the boundary of fast and curb lane, and Y denotes width of lane changing with 0 marked as the same means. Each line denotes a trajectory of a lane-changing vehicle.

Figure 12

The length and width distribution of lane changing, where X denotes the width and distance of lane changing, respectively, and Y denotes the number of samples within the corresponding group. (a) Width of lane changing; (b) Distance of lane changing.

Figure 13

Correlation between lane-changing distance and speed. X and Y denote average speed and travel distance of the vehicle during lane changing, respectively. Each point denotes the data of a lane-changing vehicle.

Figure 14

Driving experiments on the section with drivers wearing SMI ETGTM.

Figure 15

Normal and abnormal data of driver pupil diameter., where X denotes the distance from a point to tunnel exit, and Y denotes driver’s pupil diameter at this position.

Figure 16

The distribution of the time of light adaptation. (a) Light adaptation results distribution, where X denotes speed of the vehicle at tunnel exit, and Y denotes light adaptation time for the driver, each point denotes the data of a driver; (b) Correlation of vehicle speed with light adaptation time, where the value denotes the correlation between factors corresponding to X and Y.

Figure 17

Drivers’ gaze position to identify the exit ramps in the simulation driving test.

Figure 18

Components of the connection section. (a). Case A and Case B; (b). Case C.

Figure 19

Improvement ratio of Scheme 2 (L = 290 m) compared with Scheme 1 (L = 250 m). (a) Capacity; (b) Travel time; (c) Delay; (d) CO emissions. X and Y denote the total traffic volume and diverging ratio on the two-lane TECS, respectively, while Z shows the improvement ratio of Scheme 2 compared with Scheme 1 based on simulation results for the two schemes in each case. The label on the right side shows the values represented by each color to help with reading.

Figure 20

Improvement ratio of Scheme 3 compared with Scheme 1. (a) Capacity; (b) Travel time; (c) Delay; (d) CO emissions. X and Y denote the total traffic volume and diverging ratio on the two-lane TECS, respectively, while Z shows the improvement ratio of Scheme 3 compared with Scheme 1 based on simulation results for the two schemes in each case. The label on the right side shows the values represented by each color to help with reading.

Figure 21

Improvement ratio of Scheme 4 compared with Scheme 1. (a) Capacity; (b) Travel time; (c) Delay; (d) CO emissions. X and Y denote the total traffic volume and diverging ratio on the two-lane TECS, respectively, while Z shows the improvement ratio of Scheme 4 compared with Scheme 1 based on simulation results for the two schemes in each case. The label on the right side shows the values represented by each color to help with reading.

Figure 22

Improvement ratio of Scheme 5 compared with Scheme 1. (a) Capacity; (b) Travel time; (c) Delay; (d) CO emissions. X and Y denote the total traffic volume and diverging ratio on the two-lane TECS, respectively, while Z shows the improvement ratio of Scheme 5 compared with Scheme 1 based on simulation results for the two schemes in each case. The label on the right side shows the values represented by each color to help with reading.

Figure 23

Design schemes score for a two-lane TECS at 3000 veh/h traffic volume. X denotes traffic cases with different diverging ratios and Y denotes the comprehensive score of the scheme in the corresponding case. Scheme 3 performs the best with a diverging ratio less than 20%, while Scheme 4 becomes the optimal scheme with that above 30%.

Figure 24

The optimal design scheme based on the results of EBMADM. X denotes traffic volume on the two-lane TECS and Y denotes diverging ratio. The optimal scheme for each of the 28 traffic cases has been shown.