Optimal Design Alternatives of Guide Signs for Expressway Segments with Consecutive Dense Exits

Abstract

The guide signs at expressway play an important role in conveying road information to drivers. To study the reasonable setting of guide signs at expressway with consecutive dense exits, firstly, a bidirectional eight-lane expressway segment model were constructed with three exit ramp scenarios: “single exit”, “consecutive double exits”, and “consecutive triple exits”. Two groups of schemes (standard group and optimized group) for guide signs were set, resulting in 10 different experimental comparison schemes. Secondly, thirty-two licensed drivers were recruited to conduct experimental tests using a driving simulation platform. Then four types of experimental data related to driving operations are collected: participants’ subjective perception, vehicle operation safety, driving maneuvers smoothness, and cumulative eye movement comfort. These data were used to compare and analyze the 10 different design schemes of guide signs, determining the optimal design alternatives for expressway segment with three exit ramp scenarios. The comparative analysis of the experimental data across the three exit ramp scenarios revealed that factors such as ramp spacing, guide signs content, and placement distance of guide signs significantly impact vehicle safety and comfort. There is an inverse relationship between ramp spacing and both vehicle safety and driving comfort. The participants’ visual recognition efficiency is enhanced by optimizing the content and placement distance of the guide sign effectively, further ensuring the safe and smooth operation of vehicles. This study can effectively reduce traffic conflicts at expressway exit ramps and decrease the incidence of traffic accidents. Additionally, it provides solid theoretical support for the design and sustainable development of expressway traffic facilities.

1. Introduction

In traffic facilities of expressway, guide signs play a crucial role in conveying road information to drivers, effectively ensuring the safe and orderly operation of expressway traffic. However, as the scale of expressway construction continues to expand, issues related to unreasonable guide sign have begun to emerge. These issues include unclear sign information hierarchy, information overload, and unreasonable spacing of entrances/exits. The emergence of these problems has brought significant safety hazards to high-speed vehicles on expressway, and has also affected the sustainable development of expressway. A large number of accident statistics indicate that over one-third of expressway accidents occur at or near expressway entrances and exits, with exit-related accidents being particularly prevalent [1,2,3]. The issues, such as missing exits and subsequently reversing, compulsory lane-changing often result in serious accidents. Although current expressway traffic signs setting regulations include provisions for guide signs before ramp exits, the diverse nature of ramps in actual road networks, especially consecutive dense ramps, is not adequately addressed by existing standards [4]. Consequently, this study focuses on the setting of guide signs before exits on expressway sections with consecutive dense exits. This research is of great practical significance for enhancing traffic safety at expressway exits and promoting the efficient and sustainable development of expressway.

Each country has corresponding standards and regulations for the setting of expressway exit signs. For instance, the specifications for the design and setting of expressway guide signs in China stipulates that exit guide signs should be placed 2 km, 1 km, and 500 m before the beginning of the deceleration lane at expressway exits to indicate the distance to the exit [5]. Based on these existing standards, many researchers have conducted relevant studies considering practical situations, focusing on aspects such as the selection of panel information, placement and spacing of guide signs [6]. For example, Hawkins et al. [7] created a new table about advance placement distances for warning signs placed in advance of changes in horizontal alignment. Niu et al. [8] proposed a method of visualization of road guide sign panel based on the flexible combination of guiding information. Pan et al. [9] built the model for calculating the advance distance of expressway exit sign based on the characteristics of driving psychology principle and drivers’ response to the sign. Liu et al. [10] established a safety distance setting model based on the characteristics of diverging areas and driver behavior, investigated the safety performance of advance exit signs and provided the recommended positions for guide signs on six-lane highways. These studies indicate that reasonable setting of exit signs is crucial for maintaining traffic safety and efficiency on highways, particularly in areas with dense exit ramps.

With the construction of multi-lane highways, scholars have begun to conduct related research on multi-lane expressway guide signs. For example, Guo et al. [11] analyzed lane-changing trajectories to determine the reasonable setting distances for advance exit signs on bidirectional six-lane and eight-lane highways. Cui et al. [12] analyzed driver behavior characteristics to establish safety setting distance models for advance exit signs under different road conditions. Zhu et al. [13] considered factors such as driver psychophysiology, road conditions, and driving behavior to establish a model for the distance of advance guide signs on highways. Yang et al. [14] used driving simulation to study the information threshold for guide signs at ring expressway exits, determining that the threshold for cantilever signs is five items and for gantry signs is nine items. Shang et al. [15] compared 12 layout forms of exit advance guide signs in mountainous expressway tunnel based on information quantization theory, then suggested that exit advance guide signs in tunnels should be laid out as “Chinese/English place name” + “distance” + “guide arrow”. Zhao et al. [16] designed two types of exit signs for r-shaped and Y-shaped exits, resulting in four setting schemes. Through driving simulation and wayfinding experiments, the effectiveness of different exit sign forms under varying exit conditions was compared. Huang et al. [17] analyzed the effectiveness of existing typical diagrammatic guide signs (DGSs) on urban expressways in China by using driving simulator experiment, and found that DGSs can optimize driving behavior and improve road safety on expressways. The above researches on the setting of directional signs on multi-lane expressway mostly focuses on the spacing of guide signs, sign information threshold and information layout. There is no specific study on the setting of guide signs for expressway segments with consecutive dense exits, and there is a lack of research on the impact of the number of ramp exits and the spacing between adjacent ramps on the setting of guide signs.

To sum up, the above analysis of the literates underscore the importance of appropriate setting for exit signs on multi-lane highways to maintain traffic safety and efficiency. With the construction of highways featuring consecutive multiple exits, especially in densely packed exit sections, the rationality of advance guide sign setting becomes particularly crucial [18,19]. However, from the above literature analysis, although there are many studies on the setting of guide signs on multi-lane expressway in existing research, there is relatively little research on the expressway sections with consecutive dense exits. Moreover, the regulations in existing national standards are relatively macro and cannot provide detailed guidance for the setting of guide signs. Therefore, this study takes the continuous multi-exit expressway section as the research object, sets up multiple different experimental schemes based on the existing research results and national guide sign specifications, and studies the influence of the number of expressway ramp exits, the spacing between continuous ramp exits on the setting position and information and layout form of guide signs by utilizing the testing method of the simulated driving simulation platform. Finally, the optimal design alternatives of guide signs for expressway segments with consecutive dense exits are determined.

2. Experimental Design

Based on the simulation driving simulation platform, the simulation scenarios of the “dense exit” section of the bidirectional eight-lane expressway is constructed, the optimal design alternatives for advance guide signs is conducted by using driving simulation method. During the experiment, drivers are allowed to freely choose lane changes under free-flow conditions to explore the impact of guide signs on driving behavior under multifactor coupling conditions.

2.1. Experimental Environment

The driving simulation experiment platform used is a research-grade real vehicle driving simulator with 6-degree of freedom produced by Auto Sim Company based in Norway. The simulator includes several hardware systems such as the cockpit display system (CDS), main control unit, surround visual system, and network connectivity. It also comprises software systems for modeling, simulation, and data processing. The organizational structure of the system platform is shown in Figure 1 [20] (This figure is a schematic diagram of the organizational structure drawn based on the relationships between subsystems or devices within the simulation experiment platform).

The driving simulator features a layout and equipment identical to that of a real vehicle cockpit, including an electronic throttle, clutch pedal, brake pedal, steering mechanism, electronic dashboard, and driver’s seat. Virtual scenarios are projected onto three large screens, providing a 130° field of view, creating various sensory effects such as visual, auditory, and tactile experiences for the participants. Numerous studies [21,22] have demonstrated that the performance of this driving simulator closely resembles that of real-road environments, making it suitable for simulating vehicle driving behavior in different scenarios. The platform can collect real-time data on vehicle operation trajectories and performance, with a data acquisition frequency of 30 Hz.

2.2. Participants

The experiment recruited 32 licensed drivers as participants randomly. To ensure the smooth progress of the simulation experiment, 80% of the participants had expressway driving experience. The ratio of male to female drivers is 3:2, with ages ranging from 20 to 70 years (Mean = 32.66, Standard Deviation = 10.42), reflecting the overall actual distribution in China. All participants had a driving experience of at least 1.5 years (Mean = 8.47, Standard Deviation = 7.11). They were in good health, free from acute adverse illnesses, color blindness, or color weakness, and had a vision level above 0.5. None had previously participated in similar driving simulation experiments.

2.3. Experimental Scenarios

The driving simulation scenarios were set on a bidirectional eight-lane expressway. To study the impact of guide signs setting on expressway sections with different numbers of exits on driving behavior, three expressway scenarios were selected for the simulation experiments: “single exit ramp”, “double exit ramps”, and “triple exit ramps”, as illustrated in Figure 2. The road segments for each scenario were connected end-to-end, with the connection schematic shown in Figure 3 and the alignment diagram illustrated in Figure 4.

The basic parameters are primarily categorized into roadway scene parameters and sign scene parameters. The roadway scene parameters encompass an eight-lane bidirectional expressway with 3.75 m-wide carriageways and 3.5 m-wide emergency lanes, inclusive of a central median strip and roadside barriers. The horizontal alignment primarily consists of straight segments, with no elevation changes in the vertical profile. The sign scene parameters, on the other hand, specify the configuration of sign groups. The basic guide sign group solely comprises basic guide signs. The location guide sign group and multi-level advance sign group, in addition to the basic guide signs, incorporate interchange guide signs and multi-level advance signs. The deployment of these signs is adjusted based on the number of exits and the distance between adjacent exits. The design of guide signs from practical applications serves as “Standard group”, and that of our previous experiments in the literature [20] serves as “Optimized group”. The comparative simulation experiments are conducted under the identical conditions. In the “Standard group”, the color, size, font type and size, and layout design of the guide signs strictly adhere to the Chinese standard. The setting schemes of guide signs in the “Optimized group”, on the other hand, are based on the optimization schemes derived from our previous experiments [20]. The design schemes of “Optimized group” is mainly based on cognitive psychology and ergonomics theory, investigated the effects of the threshold, complexity, arrangement pattern, and number of information distribution pages of directional sign combinations on driver visual recognition by using eye movement visual recognition experiments. Ultimately, an optimization scheme for the combination form was determined.

2.4. Experimental Method

The driving simulation experiment studies the coupling effects between the number of exit ramps, the distance of exit ramps, and the combinations of the advance placement: “Location guide signs group” + “Multilevel advance signs group”. The setting schemes are shown in

Table 1. Setting schemes for guide signs in different scenarios.

No	Scheme	Exit Spacing L (km)	Sign Position D (km)		Sign Panel
1	1	L = 3	d1 = 1	D1 = y1	Standard group
	2				Optimized group
2	1	L1 = 3, L2 = 5	d2 = 2.5	D1 = y1, D2 = y2	Standard group
	2				Optimized group
	3	L1 = 3, L2 = 3	d1 = 1, d2′ = 1	D1 = D2 = y1	Optimized group
	4	L1 = 3, L2 = 2	d1 = 1, d2′ = 1	D1 = y1, D2 = y3	Optimized group
3	1	L1 = 3, L2 = 3, L3 = 5	d3 = 2.5	D1 = D2 = y1, D3 = y2	Standard group
	2				Optimized group
	3	L1 = 3, L2 = 3, L3 = 3	d1 = 1, d2′ = d3′ = 1	D1 = D2 = D3 = y1	Optimized group
	4	L1 = 3, L2 = 3, L3 = 2	d1 = 1, d2′ = d3′ = 1	D1 = D2 = y1, D3 = y3	Optimized group

. The variable “L” is the distance between two consecutive exits at expressway, the variable “d” is the setting position of the location guide sign, that is, for a single exit road section, d1 is the distance between the setting position of the location guide sign and the first advance guide sign of the interchange. The variable “y” is the setting position of the advance exit sign, which is the distance between the setting point and the starting point of the gradient section of the deceleration lane at the exit of the expressway. Based on our previous research results [20], the combination signs for the next exit and location guide signs are setting in both “Standard group” and “Optimized group” for comparative research, as shown in Figure 5 and Figure 6.

The experiment involves three exit ramp scenarios: single exit, double exits, and triple exits. Two independent variables are set based on the research requirements: (1) Exit ramp distance: This variable has three levels representing different distance between consecutive dense exit ramps (long spacing, medium spacing, and short spacing). (2) Sign design scheme: This variable has two levels representing the two types of guide sign design schemes (Standard group and Optimized group). Therefore, the experiment adopts a full factorial design: 3 (exit ramp distance) × 2 (sign design scheme). The study hypothesizes that both independent variables will have main effects and interaction effects on driving behavior. For instance, a high number of exit ramps with short distances between consecutive exits may negatively impact driving behavior, necessitating optimized guide sign schemes. Additionally, setting a high number of guide signs upstream of exits may positively influence driving behavior, but exceeding a certain threshold does not necessarily improve the effectiveness of guide signs [23,24]. Through experimental research, this study aims to determine the optimal design schemes for guide signs in scenarios with short, medium, and long exit ramp spacing, which ensure safe and efficient traffic flow on expressway.

(1)

Dense exit ramp spacing variable: This variable represents the distance between consecutive adjacent exits on the same side of the mainline. It is measured from the endpoint of the deceleration lane of one exit ramp to the starting point of the deceleration lane of the adjacent exit ramp. Based on relevant researches, the long, medium, and short spacing are defined as 5 km, 3 km, and 2 km, respectively.

(2)

Guide sign design scheme variable: This variable concerns the quantity, location, and combination of the “Location guide signs group + Multilevel advance signs group”. The specific considerations are as follows:

(i)

Location guide signs group: According to China’s sign setting standards, a location distance sign must be set more than 5 km before the next exit, indicating the names and distances of multiple upcoming exits. If the distance to the next exit is between 3 to 5 km, an advance exit sign can replace the location distance sign. For three-level exit guide signs, the forward interchange quantity sign is placed 5 to 7 km before the starting point of the deceleration lane of the interchange, and the location distance sign is placed 2 km thereafter. Therefore, the first advance guide sign is set at a sufficiently long distance d before the first interchange, with variable d1 = 1 to 3 km. Similarly, the location distance signs for the second and third interchanges are placed at a sufficiently long distance d before the first advance guide signs of the second and third interchanges, with variables d2 and d3 = 1 to 3 km. Additionally, the next exit advance sign should be positioned a certain distance d’ after the starting point of the deceleration lane of the interchange, with variables d1′, d2′, and d3′ = 1 to 3 km.

(ii)

Advance exit signs group: According to the standards, highways typically have three-level exit guide signs set 2 km, 1 km, and 500 m before the starting point of the deceleration lane of the exit, indicating the distance to the exit (y1 = 2 km, 1 km, 0.5 km). Due to the wide road width of bidirectional eight-lane highways and the need for early lane changing to leave the mainline, along with the dense exit sections requiring early warning, a four-level exit guide sign can be set (y2 = 2.5 km, 2 km, 1 km, 0.5 km). For sections with short ramp spacing in dense exit areas, two-level exit guide signs can be set (y3 = 1 km, 0.5 km). The positions of the advance signs for the three interchange exits are named D1, D2, and D3, respectively. The specific setting schemes are shown in Table 1.

3. Data Collection and Preprocessing

3.1. Experimental Data

(1)

Subjective data: The participants’ subjective perceptions of different setting schemes are collected through questionnaires. This includes the effectiveness of the driving simulator and the participants’ subjective feelings about the driving scenarios.

(2)

Driving data: During the experiment, the driving platform collects data every 0.01 s, including instantaneous acceleration, speed, position, and other motion parameters. It also records vehicle operation data such as turn signals, brake pedal usage, throttle position, steering wheel angle, etc. Additionally, road data such as the time of state changes, road numbers, lane numbers, lane types are recorded, and so on.

(3)

Eye movement data: Eye-tracking equipment collects data every 0.001 ms during the driving simulation. This includes eye-tracking data such as gaze points, gaze direction, pupil position, pupil diameter, etc. Eye movement event data include event types (e.g., fixation, saccade), event duration, and the area of interest (AOI) of the eye movement events.

3.2. Evaluation Metrics

This study evaluates the optimization schemes for expressway guide signs from four perspectives: subjective perception, driving safety, smoothness of driving operation, and eye movement comfort [25,26]. The selected evaluation metrics are as follows:

(1)

Subjective perception metrics: These indicators are mainly used to evaluate the real feelings of experimental participants towards simulated driving systems and driving environment scenes. The detailed evaluation content is shown in

Table 2. Subjective perception survey on driving simulator effectiveness.

Question 1	Is the test experience of accelerator and brake real?					Scores	Final scores 80.63
Degree	A. Very Realistic	B. Quite Realistic	C. Generally Realistic	D. Somewhat Unrealistic	E. Completely Unrealistic	76.25
Quantity	4	20	6	2	0
Percentage	12.5%	62.5%	18.75%	6.25%	0
Question 2	Is speed perception real in the experiment?					Scores
Degree	A. Very Realistic	B. Quite Realistic	C. Generally Realistic	D. Somewhat Unrealistic	E. Completely Unrealistic	78.75
Quantity	8	15	8	1	0
Percentage	25%	46.88%	25%	3.13%	0
Question 3	Is the experience of the test scenarios feel?					Scores
Degree	A. Very Realistic	B. Quite Realistic	C. Generally Realistic	D. Somewhat Unrealistic	E. Completely Unrealistic	83.13
Quantity	11	16	4	1	0
Percentage	34.38%	50.00%	12.50%	3.13%	0
Question 4	Is the experience of guide sign settings at interchange locations feel?					Scores
Degree	A. Very Realistic	B. Quite Realistic	C. Generally Realistic	D. Somewhat Unrealistic	E. Completely Unrealistic	81.25
Quantity	11	16	3	1	1
Percentage	34.38%	50.00%	9.38%	3.13%	3.13%
Question 5	Is the experience of multi-level exit advance guide sign settings feel?					Scores
Degree	A. Very Realistic	B. Quite Realistic	C. Generally Realistic	D. Somewhat Unrealistic	E. Completely Unrealistic	83.75
Quantity	13	14	3	2	0
Percentage	40.63%	43.75%	9.38%	6.25%	0

and

Table 3. Subjective perception survey on driving situation awareness.

Question 1	Have you ever unable to clearly read a sign in time?					Scores	Final Scores 77.88
Degree	A. Never	B. Rarely	C. Sometimes	D. Occasionally	E. Frequently	79.38
Quantity	13	11	2	6	0
Percentage	40.63%	34.38%	6.25%	18.75%	0
Question 2	Have you ever unable to react in time after seeing a sign?					Scores
Degree	A. Never	B. Rarely	C. Sometimes	D. Occasionally	E. Frequently	80
Quantity	12	14	0	6	0
Percentage	37.50%	43.75%	0.00%	18.75%	0
Question 3	Have you ever noticed the guide sign information being discontinuous?					Scores
Degree	A. Never	B. Rarely	C. Sometimes	D. Occasionally	E. Frequently	78.75
Quantity	13	10	5	3	1
Percentage	40.63%	31.25%	15.63%	9.38%	3.13%
Question 4	Have you ever noticed interruptions in the destination information?					Scores
Degree	A. Never	B. Rarely	C. Sometimes	D. Occasionally	E. Frequently	80.63
Quantity	15	10	2	4	1
Percentage	46.88%	31.25%	6.25%	12.50%	3.13%
Question 5	Have you ever felt confused about the direction of your destination after seeing a sign?					Scores
Degree	A. Never	B. Rarely	C. Sometimes	D. Occasionally	E. Frequently	70.63
Quantity	10	10	1	10	1
Percentage	31.25%	31.25%	3.13%	31.25%	3.13%

, and the evaluation data was obtained through questionnaires. In this study, the effective perception of the driving simulator (E_f) and subjective perception of the driving scenario (S_u) are selected.

(2)

Driving safety metrics: From the perspective of vehicle operation safety, four parameters are selected as evaluation indicators: speed, speed standard deviation, acceleration, and acceleration standard deviation, reflecting the safety state of vehicle operation. The closer the vehicle’s average speed ($\overline{\mathrm{V}}$) is to the speed limit, the higher the safety. A smaller the speed standard deviation (S_V) indicates smoother and safer vehicle operation. Influenced by road speed limits, the closer the acceleration (a) is to 0, the better the driving state. A smaller the acceleration standard deviation (S_a) indicates a more stable psychological state for the driver and higher safety.

(3)

Driving operation smoothness metrics: From the perspective of driving behavior smoothness, four indicators are considered: braking frequency (Q), braking effectiveness (P), lane-changing frequency (N), and the distance between lane-changing points and the exits (D). Braking effectiveness (P) is defined as the sum of the effects of releasing the throttle and applying the brake. A larger P indicates better speed control and smoother driving. It is calculated as:

$$P=\Sigma A\mathsf{\Delta }t+\Sigma B\mathsf{\Delta }t$$

(1)

where, A is the throttle release depth, Δt is the data collection interval, and B is the brake pedal depth.

Fewer braking frequency (Q) indicate better speed control and smoother driving. Fewer lane-changing frequency (N) within the critical influence range indicate safer and smoother driving behavior.

(4)

Eye movement comfort metrics: From the perspective of eye movement comfort, global eye movement data such as pupil diameter, fixation duration, fixation frequency, saccade duration, and saccade frequency are considered as cumulative comfort indicators for the driver’s information processing. Changes in pupil diameter (D) are mainly induced by variations in dense exit ramp guide signs. Ignoring other factors, the normal range of D is 1.3–4 mm, and the larger the deviation from this range, the poorer the comfort. Fixation refers to the activity of focusing the eyes on a sign, with fixation duration being the time the gaze remains fixed. The value of global average fixation duration (F_d) is normally between 100–20,000 ms, and the larger the deviation, the poorer the comfort. The global average fixation frequency (F_i) is the total number of fixations within the critical influence range. The global average saccade frequency (S_i) is the total number of saccades within the critical influence range, and a higher Si indicates poorer comfort. The global average saccade duration (S_d) is the average duration of all saccade events, where saccades refer to the process of the eyes searching for targets in the traffic environment. The larger the S_d, the poorer the comfort.

3.3. Data Preprocessing

(1)

Outlier removal

During the simulation experiment, collected data may be irregular, inaccurate, or even missing due to factors such as participants’ unfamiliarity with the test vehicle, equipment issues, or the experimental environment. Therefore, data must be preprocessed to remove significant outliers before analysis. The specific steps for preprocessing include:

(I) Equipment Calibration: Prior to the experiment, all instruments and equipment must undergo precision calibration. If any equipment fails to meet the required accuracy standards during calibration, the data generated by that equipment should be excluded. A rigorous calibration process minimizes the potential for data bias caused by equipment errors.

(II) Real-Time Monitoring and Recording: During the experiment, it is crucial to closely monitor the status of the instruments and the accuracy of the data. Any anomalies detected should be promptly corrected, with detailed records of specific incidents. Time stamps should be added to the data, facilitating the accurate identification and removal of biased data during preprocessing.

(III) Time Synchronization: To ensure the consistency of all data, it is necessary to synchronize the timing of all data collection devices. If synchronization is not feasible for certain devices, key time points should be manually recorded. During data processing, synchronized data should be retained, and redundant data resulting from desynchronization should be removed. This step is critical, as unsynchronized data could lead to biased analytical results.

(IV) Video Recording and Data Comparison: The entire experiment should be recorded using a high-definition camera, and during post-processing, the video footage should be compared with the experimental data. Special attention should be paid to key driving behaviors such as lane changes, sudden braking, and overtaking. Any abnormal driving behaviors not triggered by standard markers should be considered invalid data and excluded from the analysis. This method helps ensure that only valid driving behavior data are analyzed, reducing noise in the analysis.

(V) Elimination of Anomalous Speed Data: All recorded vehicle speed data should be examined, and data exceeding or falling short of the specified speed by 15%, as well as discontinuous data, should be considered invalid and removed. These anomalies may result from driver errors or equipment inaccuracies and must be excluded before data analysis.

(VI) Filtering of Eye Movement Data: During the experiment, vehicle vibrations or bumps may impair the effectiveness of eye-tracking equipment. Eye movement data affected by vehicle motion should be deemed invalid and removed to ensure the accuracy of the final analysis data.

In response to the possible data anomalies mentioned above, this study adopts the following processing methods:

(I) Data Filtering and Selection: Use Python programming tools to perform an initial filtering of the collected data, removing values that are clearly outside the normal range. Apply predefined threshold standards to further refine the data selection.

(II) Multi-Source Data Comparison: Cross-validate data from multiple sensors, such as vehicle speed sensors and eye-tracking equipment, to ensure the plausibility of each data point and remove potential anomalies.

(III) Time Synchronization Correction: Utilize time stamps within data processing software to correct the timing of data, ensuring that all data align on a consistent timeline. Manually adjust or exclude any data points that cannot be synchronized.

(IV) Video-Assisted Analysis: Use video data to assist in analyzing the experiment, conducting detailed checks on recorded driving behaviors. Review video footage to confirm and exclude anomalous driving data.

(2)

Video data extraction

Extract the experimental videos recorded by the driving simulator and eye tracker. Import these videos into driving behavior video analysis software (SCANeR Studio 1.9) and eye movement video analysis software (TOBII Glasses 2). The extraction process involves identifying the moments when: The driver shifts their gaze from the experimental equipment to the forward road task area (starting point). The driver shifts their gaze from the forward road to the target exit ramp area to execute the experimental task (ending point). The data between these two moments constitute the driving process data for a particular setting scheme.

(3)

Data synchronization

Different instruments collect data at different time intervals (e.g., driving data at 0.01 s intervals and eye movement data at 0.001 ms intervals). Synchronizing these data sets is essential for unified standard evaluation. Use the time information extracted from video analysis software to synchronize the corresponding time segments in the driving simulator and eye tracker data.

(4)

Segmentation of data units

The 2200-m upstream section of a expressway or expressway exit ramp is considered a critical impact range. Based on the evaluation criteria, extract data from the 2200-m point before the target exit to the exit point for analysis. Different positions correspond to different types of guide signs. As shown in Figure 7, data is extracted at 100-m intervals within the 2200-m influence range of the destination exit for long, medium, and short spacing schemes (negative values indicate distances before reaching the exit ramp). In the long spacing scenario, the critical influence range includes only the destination exit and some advance exit signs. In the medium spacing scenario, it includes the destination exit and advance exit signs. In the short spacing scenario, it encompasses the destination exit, an additional exit, advance exit signs, and location guide signs.

4. Experimental Results Analysis

The impact of guide signs on driving behavior can be evaluated in four aspects [20]: subjective perception, vehicle motion parameters, driving operation behavior, and cumulative eye movement parameters. During data processing, the indicators are categorized and analyzed according to these four aspects.

4.1. Analysis of Subjective Perception Impact

Subjective factors such as survey questionnaire responses are converted into quantitative data to more accurately assess the effective perception of the driving simulator and the subjective awareness of the driving scenario. The effectiveness evaluation of driving simulators including the realistic feel of the accelerator and brake, speed perception characteristics, and the experience of the scenes and guide signs, with a total of 5 questions set. The evaluation levels are: A—very real, B—relatively real, C—average, D—relatively unreal, and E—completely unreal, corresponding to scores of 100, 80, 60, 40, and 0, respectively. The calculation process of the score for a certain question is as follows: Firstly, the number of people who choose a certain evaluation level is multiplied by the score of this level, and the scores of the five evaluation levels are added together. Then, the total score of five evaluation levels is divided by the number of experimental participants, to obtain the average score, which is the questionnaire survey score for a question. Finally, the final score is the average score of all questions, and the higher the score, the more authentic the feelings of the experimenters. The specific results are shown in Table 2. The subjective awareness of the driving scenario is evaluated based on the visibility of signs, with a total of 5 questions set. The evaluation levels are: A—completely absent, B—almost absent, C—general, D—occasionally present, and E—frequently present, corresponding to scores of 100, 80, 60, 40, and 0, respectively. The calculation process is similar to Table 2, and the specific results are shown in Table 3.

Subjective perception can reflect the extent to which drivers’ information needs and cognitive patterns are met, as well as the alignment of guide signs with road alignment conditions and the traffic environment. From the data in Table 2 and Table 3, the final score for effective perception of the driving simulator is 80.63, and the final score for subjective awareness of the driving scenario is 77.88. These results demonstrate that the driving simulator has been effectively validated and provides reliable experimental data. The designed system of guide signs for dense exits under coupling conditions performs well, indicating that the experiment can lead to more rational guide sign system designs, thereby improving the service level and driving safety of expressway.

4.2. Analysis of the Impact of Guide Signs on Single Exit

Experiment 1: This experiment investigates the impact of the placement of guide signs on driving behavior on a expressway single exit ramp section. It aims to verify the readability, continuity, and standardization of the scenarios and sign setups. The scenarios are named Scenario 1-1 and Scenario 1-2, corresponding to the “Standard group” and “Optimized group” sign setting schemes in the two experimental setups, respectively. The variations of various parameters in driving simulation under two setting scenarios are shown in Figure 8.

(1)

Driving safety: As shown in Figure 8(1) to Figure 8(4), the “Optimized group” participants were more conscious of controlling their speed in advance. A noticeable deceleration begins 700 m before the exit, with speeds successfully reduced to 60 km/h before reaching the exit. Within the 700-500 m range before the exit, acceleration is lower. This range corresponds to the placement of multilevel advance exit signs at 500 and 1000 m before the exit, indicating that the optimized setting of sign effectively guides drivers in adjusting their speed. Additionally, the standard deviation of speed and acceleration in the optimized group is lower, suggesting more stable driving and higher overall safety.

(2)

Smoothness of driving operation: Figure 8(5) to Figure 8(7) show that the braking effectiveness in both schemes is within the 1.42–1.43 s% range. However, the “Optimized group” has slightly fewer instances of braking and lane changes compared to the “Standard group”. In “Optimized group”, lane changes occur earlier upstream of the exit ramp, demonstrating that the optimized signs better guide drivers to change lanes earlier and enter the deceleration lane sooner. This results in smoother operations and higher driving efficiency.

(3)

Eye movement comfort: Figure 8(8) to Figure 8(12) indicate that in the “Optimized group” participants’ pupil diameters remain within the normal range (1.3−4 mm), whereas the “Standard group” shows instances exceeding this range. This suggests an improved psychological state for participants in the “Optimized group” during the simulation. The fixation duration in the “Optimized group” is 1462.27 ms, which is within the normal range (100–2000 ms). Longer fixation durations occur around 1400 m and 300 m before the exit (near lane change and exit points). Longer saccade durations occur around 2200 m, 1200 m, and 700 m (all near multilevel advance exit signs), indicating that participants in the “Optimized group” predominantly maintain a forward gaze and quickly acquire necessary information from signs with brief glances, leading to a generally comfortable state.

4.3. Analysis of the Impact of Guide Signs on Consecutive Double Exits

Experiment 2: This experiment investigates the impact of guide signs on driving behavior across two consecutive interchange sections on a highway. The “Standard group” represents the existing “long spacing” scenario and serves as the control group. The “Optimized group” optimizes for three different spacing scenarios: the “long spacing”, “medium spacing”, and “short spacing” scenarios. This optimization involves changing the placement and type of location guide signs and optimizing the combination and placement of advance exit signs to suit different ramp spacings. The scenarios are named Scenario 2-1 to Scenario 2-4, corresponding to the four settings: “standard group”, “optimized group (long spacing)”, “optimized group (medium spacing)” and “optimized group (short spacing)”. The variations of various parameters in driving simulation under four setting scenarios are shown in Figure 9.

(1)

Driving safety: As illustrated in Figure 9(1) to Figure 9(4), the comparative results among the four schemes are as follows. Regarding average speed, the optimized group (long spacing) at 98.68 km/h is less than the standard group at 101.03 km/h, which in turn is less than the optimized group (medium spacing) at 102.08 km/h, and the shortest spacing in the optimized group (short spacing) achieves the highest speed of 105.91 km/h. In terms of acceleration, the standard group and optimized group (long spacing) both exhibit −0.16 m/s², followed by the optimized group (medium spacing) at −0.17 m/s² and the optimized group (short spacing) at −0.18 m/s². The standard deviation of speed is relatively similar across all four groups, but the standard group experiences greater fluctuations after the secondary exit advance sign. Regarding the standard deviation of acceleration, the three optimized schemes are close to each other and all smaller than the standard group, indicating a significant improvement in safety with the optimized schemes, where shorter exit distance necessitate higher safety requirements.

(2)

Smoothness of driving operation: As depicted in Figure 9(5) to Figure 9(7), the braking effectiveness of all four schemes falls within the range of 1.1–1.2 s%. In terms of braking frequency, the standard group exceeds the three optimized groups with varying exit spacings, and a shorter exit spacing results in fewer braking instances. Regarding lane-changing frequency, the standard group is higher than the optimized group (long spacing), but as the exit spacing decreases, the number of lane changes increases. Collectively, these findings suggest that participants experience better maneuver fluency upstream of exits with the optimized schemes.

(3)

Eye movement comfort: As shown in Figure 9(8) to Figure 9(12), the comparison among the four schemes reveals the following. With respect to pupil diameter, the standard group registers 3.64 mm, exceeding the three optimized groups with long (3.54 mm), medium (3.58 mm), and short (3.62 mm) spacings, indicating that participants feel more comfortable on roads with three “optimized groups”. In terms of fixation and saccade durations, the standard group is shorter than the three optimized groups with varying spacings. However, the standard group records more fixation and saccade counts than the optimized groups, suggesting that in complex situations, the optimized signs provide more comprehensive guide information, requiring participants to maintain longer fixation durations to process the information. The more accurate information, in turn, reduces the number of visual recognitions, facilitating better attention retention and cumulative eye movement comfort during driving.

4.4. Analysis of the Impact of Guide Signs on Consecutive Triple Exits

Experiment 3: This experiment investigates the impact of guide signs on driving behavior across three consecutive interchange sections on a highway. The “Standard group” represents the actual existing situation and serves as the control group. The “Optimized group” considers the “long spacing”, “medium spacing”, and “short spacing” scenarios, changing the placement and type of location guide signs and optimizing the combination and placement of advance exit signs to suit drivers’ needs for repeated guide sign setting. The scenarios are named Scenario 3-1 to Scenario 3-4, corresponding to the four settings: “standard group”, “optimized group (long spacing)”, “optimized group (medium spacing)” and “optimized group (short spacing)”. The variations of parameters in driving simulation under four setting scenarios are shown in Figure 10.

(1)

Driving safety: As depicted in Figure 10(1) to Figure 10(4), the comparative results among the four schemes are as follows. Regarding average speed, the standard group achieves 100.58 km/h in three-exit scenarios, slightly lower than 101.03 km/h in two-exit scenarios. The optimized group, on the other hand, exhibits an increasing trend in average speed across its three schemes in three-exit scenarios, with long spacing at 101.27 km/h < medium spacing at 102.38 km/h < short spacing at 108.75 km/h. Although the optimized schemes show a similar pattern in average speed, they all register an increase compared to two-exit scenarios. In terms of acceleration, all four schemes follow a consistent pattern across three-exit scenarios but exhibit lower values than in two-exit scenarios, indicating that while overall speed increases, it remains within a reasonable range with smoother speed variations, conducive to driving safety.

(2)

Smoothness of driving operation: As illustrated in Figure 10(5) to Figure 10(7), the comparison among the four schemes reveals the following. In terms of braking effectiveness, both the standard and optimized group (long spacing) show lower braking effectiveness in long-spacing exit scenarios under three consecutive exits compared to two exits, while the opposite is true for optimized groups (medium and short spacing). Higher braking effectiveness suggests better speed control awareness among drivers. Regarding braking frequency, under three consecutive exits, braking occurs less frequently in long and medium spacing exits compared to two exits, but more frequently in short spacing exits. In terms of lane-changing frequency, all four schemes exhibit higher lane-changing counts under three consecutive exits than in two-exit scenarios, with shorter exit distance leading to more frequent lane changes. This indicates that participants experience better maneuver fluency in optimized long-spacing exits.

(3)

Eye movement comfort: As shown in Figure 10(8) to Figure 10(12), the comparative results among the four schemes are as follows. In terms of pupil diameter, the three optimized schemes show smaller pupil diameters under three consecutive exits compared to two exits, suggesting that reasonable optimization of signs does not increase driving burden in three consecutive dense exits. Across all four schemes, fixation durations and fixation counts decrease, while saccade counts decrease and saccade durations increase under three consecutive exits. This indicates that as the number of consecutive dense exits increases, participants shift from a fixation state to a saccade state, seeking better comfort to accurately respond to travel demands.

5. Conclusions

With the continuous improvement and expansion of the expressway network, the number of expressway exit ramps is increasing, making the advance placement of guide signs crucial for guiding and diverting vehicles, especially on expressway sections with dense exits. This study investigated the design content, setting spacing, and placement of of guide signs for different numbers of exit ramps on a bidirectional eight-lane expressway and evaluated various setting schemes by using a driving simulation platform. The main findings of the study are as follows.

(1)

For a expressway section with a single exit, where the location guide sign placement (d1 = 1 km) and advance exit sign placements (D1 = 2 km, 1 km, 0.5 km) are the same, the optimized groups conveyed road information more efficiently through the optimization design of the combination signs for the next exit and location guide signs. The optimized schemes, which included an advance location guide sign, allowed participants to quickly acquire target information from the signs, ensuring good eye movement comfort while significantly improving driving safety, smoothness, and comfort within the critical influence range of the exit ramp.

(2)

For a expressway section with two consecutive exits, the experimental results showed that as the distance between exit ramps decreased, the vehicle speed fluctuation increased and lane change operations became more frequent within the critical influence range. The closer the interfering exit was to the target exit, the greater the impact on participants’ ability to find and correctly judge the exit. By optimizing the setting spacing of guide signs, the safety of vehicle operation and smoothness of driving behavior improved significantly in the simulation, allowing participants to proactively adjust their speed and successfully locate the target exit in advance.

(3)

For a expressway section with three consecutive exits, the results indicated that as the number of exit ramps increased and the spacing between them decreased, the interference with driving behavior grew, negatively affecting drivers’ ability to safely leave the mainline. Therefore, the placement of guide signs in special sections should consider the number and spacing of exits, with targeted advance placement of location guide signs and advance exit signs. Data analysis from the simulation experiments showed that in the optimized schemes, participants exhibited smoother driving behavior and greater comfort. More participants were able to quickly and accurately locate the target exit and take early lane-change actions after scanning the guide sign information.

In summary, this study focuses on the setting of guide signs for expressway sections with “single exit”, “two consecutive exits”, and “three consecutive exits”, and investigates the effects of guide signs information and layout, number of ramp exits, and distance between consecutive ramp exits on the setting of guide signs by using simulated driving. This study provides theoretical support for the setting of guide signs on expressway sections with dense exits. By optimizing the design schemes of guide signs, it effectively enhances driving safety and comfort, promoting efficient and sustainable development of expressway.