A Driving Simulation to Analysis and Quantitative Comparison of Driving Behavior of Guide Signs at Intersections
Guide signs are an important source for drivers to obtain road information. However, the evaluation methods for the effectiveness of guide signs are not unified. The quantitative model for evaluating guide signs needs to be constructed to unify the current system of guide signs. This study aims to take the commonly used guide signs in China as the research object to explore the evaluation method of guide signs at intersections. Eight kinds of guide signs were designed and made based on the common layout (layout 1 and layout 2) and the amount of information on signs (3–6). Thirty-four drivers were recruited to organize a driving simulation based on the visual cognitive tasks. Drivers’ legibility time and driver behavior were obtained by using the driving simulator and E-Prime program. A comprehensive quantitative evaluation model of guide signs was established based on the factor analysis method and grey correlation analysis method from the perspective of safe driving. The results show that there is no significant difference in the SD of speed and the SD of acceleration under the influence of various guide signs. The average vehicle speed and acceleration decrease, and the lateral offset distance of the vehicle increases with the amount of information on guide signs increasing. The quantitative evaluation results of guide signs show that the visual security decreases with the increase of the amount of information on guide signs. And layout 2 has better performance than layout 1 when the amount of information on guide signs is the same. This study not only explores the change rule of driving behavior under the influence of guide signs, but also provides a reference for the selection of guide signs.
Traffic guide signs are a universal language accepted by the traffic participants and are essential safety facilities on the road. Guide signs provide drivers with timely and relevant information about upcoming situations and enable drivers to adjust their driving behavior accordingly . At present, the design of guide signs in China generally follows the Road Traffic Signs and Markings . But there are still deficiencies in the guide signs design regulations. In order to adapt to the actual conditions of their own road network structure and local characteristics, some cities in China have successively introduced guide signs specifications that apply their own city characteristics. However, there are certain differences in the component elements of guide signs in various regions. And these differences may affect the process of drivers’ target information visual cognition and the safe driving of drivers [3, 4].
The visual cognition time of guide sign is usually an important index to evaluate the safety of a guide sign . In order to keep drivers’ visual cognition time of guide signs within a safe range, many scholars have conducted a lot of research from the angle of sign information threshold [6, 7]. On the one hand, Wang designed an indoor visual cognitive experiment to obtain the linear functional relationship between the amount of information and visual cognition time . Liu et al.  used Microsoft Power to present the guide signs and used a stopwatch to record the visual cognition time; the mathematical expressions of the number of Chinese characters, the amount of information, and the visual cognition time of the guide signs were obtained through data analysis. On the other hand, Yang et al.  organized three batches of drivers with different levels of English proficiency to conduct a driving simulation test, and the information threshold of the bilingual guide signs in both Chinese and English on the highway was obtained qualitatively. And the results show that the English road names on the bilingual guide signs do not have much practical effect on the drivers according to the visual accuracy and the driving behavior indicators. Yang et al.  found that there is no significant difference in driver response time between monolingual and bilingual signs on urban roads based on the static sign picture display experiment. It may be that the China driver could get the necessary information when they just read the Chinese part of the bilingual guide signs .
The influence of the layout of guide signs’ information on the visual perceptual characteristics of drivers has also attracted the attention of scholars. Cui  studied the different effects of the guide sign’s text layout on the drivers’ visual cognition and comprehension characteristics and found that the text layout affected the participants’ understanding of the signs. Li et al.  summarized the advantages and disadvantages of the layouts of guide signs in various places to design an improved layout. And the experiments were taken to compare the performance of the improved layout and the recommended layout of the Road Traffic Signs and Markings . It was found that there was no significant difference between the two layouts in terms of search cognition. Ya-chen et al.  quantified the impact of the amount of information and the matrix arrangement of the information on the legibility time and found that rationally optimizing the layout of the sign could increase the information threshold.
Most guide signs studies to measure drivers’ reaction characteristics were based on static visual recognition tests in the past. In addition to cognition and understanding, the influence of guide signs on drivers is also reflected in the driving behavior [15, 16]. The guide signs of not being well designed will have a bad influence on driving safety . In order to clarify the relationship between the amount of information of guide signs and dangerous driving behavior, Sun et al.  designed a dynamic simulation test and then analyzed the influence of the amount of information on driving behavior from the perspective of dangerous driving. The results show that drivers’ driving behavior will change greatly during the period of recognizing guide signs. Liu et al.  conducted different guide signs dynamic visual cognition tests using vehicle acceleration and lateral offset distance as research indicators and measured the behavior changes of drivers when recognizing guide signs with different amounts of information. The study showed that the amount of information on guide signs had a significant impact on driving behavior. Lyu et al.  quantified the different information amount signs into four levels of visual load and organized static tests and questionnaire surveys. It was found that the amount of information on guide signs was highly correlated with the driving load. Xu et al.  organized a driving simulation test, combined subjective evaluation of subjects with indicators such as driver’s legibility distance, and studied the information threshold of graphical variable message signs.
Previous research has focused on the evaluation of guide signs from the driver’s visual characteristics and driving behavior. The evaluation of guide signs is mainly based on the single index of the driver’s visual cognition time [22, 23]. Although the effectiveness of visual cognition time as a safety evaluation index of guide signs has been proved , the visual cognition time is static in mostly studies, and the visual cognition time is different between static and dynamic conditions , and the visual cognition time is influenced by many factors [26, 27]. At the same time, the research mostly focuses on the influence of the amount of information of guide signs on driving behavior. However, there is still no research to quantitatively evaluate the effectiveness and safety of guide signs by combining the driving behavior under the influence of guide signs with the visual cognition time of guide signs to construct a comprehensive quantitative evaluation system.
Establishing relevant evaluation standards for guide signs is an effective way to solve the numerous problems of current guide signs and unify the details of the guide signs from the perspective of visual safety. Therefore, this research used two variables, that is, the amount of information and the layout of the guide signs, to design the schemes of guide signs to be evaluated. Participants were asked to carry out the task of visual cognition of guide signs during the driving simulation test. The changes in legibility time and the driver’s behavior under the influence of each guide sign were analyzed. A quantitative evaluation model was constructed to comprehensively analyze the guide signs from the perspective of safe driving. It provided a reference for the reasonable and objective quantitative evaluation of guide signs.
A total of 34 drivers with a driver’s license were recruited for the test. Drivers with a driving experience of no more than 2 years were defined as novice drivers, and drivers with a driving experience of more than 2 years were defined as experienced drivers . 16 drivers were male drivers, and 18 drivers were female drivers. Novice drivers accounted for 47.06% of the total samples. The visual acuity of all the subjects was 1.0 or higher and all in health.
With the development of driving simulation technology, driving simulation systems are used widely in the evaluation of road safety facilities [29–31]. The driving simulation system used in the test includes a scene display system, a vehicle control system, and a sound source system. The scene display system is mainly composed of three LED, which form a 135° angle to each other. The vehicle control system mainly includes the steering wheel, brake pedal, accelerator pedal, and other components with force feedback. The sound source system is used to simulate the sound of the engine and the surrounding environment during the driving process, which can restore the real driving experience of the driver (see Figure 1). And the scene of this experiment was set up by the UC-win/Road driving simulation software produced by Japan’s FORUM8 company.
2.3. Experimental Scenarios
2.3.1. Design of Guide Signs
Based on the size specification of the guide signs in Road Traffic Signs and Marking , eight types of guide signs were designed including two variables—the amount of information and layout. In order to control the variables, there were no road names recurring in the experiment and no uncommon words of Chinese in the road names. In order to balance the influence of the target road name’s location on the guide signs, a total of 24 signs were made, and some sample graphs are shown in Figure 2. It is worth mentioning that the text on the guide sign in the experiment is Chinese.
2.3.2. Scenario Design
Scenarios were constructed according to the corresponding standard Urban Road Engineering Design Specification  and Urban Road Traffic Signs and Markings Setting Specification , such as road and signs constructions. In order to avoid driving fatigue, driving tests were divided into three scenarios, and each scenario involved 8 guide signs. In order to prevent the mutual influence between the intersections, the space between two intersections was 800 m. The driving routes and directions are shown in Figure 3. The target road name was reminded for drivers by the scene editing function of UC-win/road. In order to avoid the interference of the traffic flow on the drivers, no traffic flow was set in the scenarios.
2.4. Experimental Design and Procedure
2.4.1. Before the Formal Test
First, the participants were informed of the relevant requirements: the speed limit of the entire scenario was 60 km/h, and the driving choice at the intersection should be made according to the target road name prompted by the computer screen before the intersection. They were asked to tell the visible point of the guide sign and which position the target road name is on the guide sign by self-reporting (left, right, or up). And the participants did not know the purpose of the experiment. After the participants understood the driving tasks, they did some practice tests to familiarize driving operation. They would start the formal test after a 5-minute rest when the participants were proficient in driving operations and clearly recognized the tasks.
2.4.2. During the Formal Test
The sequence of the three scenarios for each participant was randomly assigned by the experimenter. The participants could drive according to their usual driving style and habits. The subjects were required to abide by the relevant traffic rules during the driving process. During the whole test, the experimenter controlled the E-prime program to record the visible point and the legibility point according to the feedback of the participants. For each guide sign, the participants were required to make two self-reports, once at the point of view of the guide sign, and once the driver recognized the position of the target road name on the guide sign. The participants need to complete the driving tasks of all driving scenarios and rest for 5 minutes after each scenario. Each scenario took about 10 minutes, and the total test process took about 40 minutes.
2.4.3. After the Formal Test
After the participants have completed the driving test in all scenarios, the participants were asked to check their basic information again and received a certain reward.
2.5. Data Processing
2.5.1. Data Preprocessing
34 participants all completed 3 scenarios. Sometimes, there were distractions and something that makes the data significantly deviate from the normal value. The statistical product and service solutions (Statistical Product and Service Solutions, SPSS) box plots were used to remove outliers.
2.5.2. Evaluation Indicators
(1) Legibility Time. The legibility time is an indicator to evaluate the driver’s cognition of the guide sign, and the legibility time was defined as the time taken by the driver from identifying the guide sign information to confirm it . Studies have shown that the more complex the signs are, the longer the legibility time it takes for drivers to cognition the signs . The E-prime software was used to collect the driver’s legibility time. And each test scenario corresponded to an E-prime program. Each test scenario recorded 8 groups of legibility time, and the order of appearance of stimuli was the same as the order of appearance of guide signs in simulated driving. When the participant started driving, the experimenter ran the corresponding E-prime program on another computer. The experimenter pushed the test button according to the participant’s two self-reports when the participant approached the guide sign. E-prime accurately recorded the time of pushing the two keystrokes. The time difference with millisecond precision was used as the participant’s legibility time.
(2) Driver Behavior Indicators. The driver behavior was selected based on the previous research on guide signs [35–39]. The selected indicators and their meanings are shown in Table 1. The data collected section was 200 m before the guide sign. The driver’s visual cognitive process and the operation reaction process before the guide sign are shown in Figure 4.
2.5.3. Data Analysis Methods
Four data analysis methods were adopted in sequence. First, descriptive statistics and multifactor analysis of variance were used to reveal the change of indicators under the influence of different guide signs, and then factor analysis and grey correlation were used to construct a quantitative evaluation model for guide signs to further evaluate the pros and cons.
3.1. Descriptive Statistics of Indicators
Figure 5 shows the changing trend of drivers’ legibility time and driver behavior with the change of layout and the amount of information on guide signs.
3.1.1. Legibility Time
The relationship between the legibility time and the key component elements of the guide sign is shown in Figure 5(a). The driver’s legibility time is positively correlated with the amount of information on the guide sign. As the amount of guide sign’s information becomes larger, the legibility time becomes longer. The fluctuation range of the average legibility time is 1649.448 ms–2929.562 ms. Multifactor analysis of variance was used to analyze the impact of each influencing factor on the legibility time and found that the amount of information has a significant impact on legibility time (F = 28.976, ), indicating that the time allocated to the guide signs by drivers will increase accordingly with the amount of information increasing.
Figure 5(b) shows the average speed of different amounts of information and layout. It can be seen that the larger the amount of information is, the lower the average longitudinal vehicle speed is when adopting the same layout of guide signs. When the amount of information is the same, the driver’s average longitudinal speed of layout 2 is lower than that of layout 1.
Multifactor analysis of variance was used to explore the influence of gender, the layout, and the amount of information of guide signs on the longitudinal speed of drivers. It is found that gender has a significant effect on the longitudinal speed of drivers (F = 67.420, ). The simple main effect analysis of gender shows that the average speed of male drivers is 6.333 km/h higher than that of female drivers (the 95% confidence interval is 4.815 km/h—7.851 km/h). Multifactor analysis of variance was used to explore the influence of driving experience, the layout of guide signs, and the amount of information on the longitudinal speed of the driver. The driving experience has a significant effect on the longitudinal speed (F = 11.598, ). Further analysis of the main effect of the driving experience: the average longitudinal speed of novice drivers is 3.001 km/h lower than that of experienced drivers (95% confidence interval is −4.736 km/h—1.266 km/h).
3.1.3. SD of Speed
The SD of the speed reflects the degree of dispersion of the vehicle speed during the driving process. The lower the value of the SD of the speed, the smaller the negative impact of the road sign on the driver. Figure 5(c) shows the SD of vehicle speed under the influence of the different amounts of information and layout on guide signs. It can be seen that when the amount of information is 4 and adopting the layout 1, the maximum SD of vehicle speed is 9.185. At the same time, when the amount of information is 4 and adopting the layout 2, the minimum SD of the vehicle speed is 8.314. It shows that when the amount of information is the same, the layout of the guide sign will have a certain impact on the driving stability of the driver. In general, layout 1 has a greater impact on the SD of vehicle speed than layout 2 based on the performance of the eight types of guide signs.
When the acceleration is positive, it means that the vehicle is accelerating. Similarly, when the longitudinal acceleration is negative, it means that the vehicle is decelerating. The greater the absolute value of the negative value is, the larger the deceleration is. Figure 5(d) shows the average longitudinal acceleration of the driver under the influence of the different amounts of information and layout. When two guide signs have the same layout, the average longitudinal acceleration will decrease with the amount of information increasing.
Multifactor analysis of variance was used to explore the influence of gender, the layout, and the amount of information of guide signs on the longitudinal acceleration of drivers. Only gender has a significant effect on the longitudinal acceleration of drivers (F = 6.553, ). A further simple main effect analysis of gender shows that the absolute value of the longitudinal acceleration of male drivers is 0.05 m/s2 greater than that of female drivers (95% confidence interval is 0.088 m/s2—0.012 m/s2).
3.1.5. SD of Acceleration
The SD of acceleration describes the degree of acceleration’s dispersion. Figure 5(e) shows the acceleration SD under the influence of the different amounts of information and layout. When the amount of information is the same, the acceleration SD of layout 2 is lower than that of layout 1.
3.1.6. Lateral Offset Distance
Figure 5(f) shows the lateral offset distance of different guide signs. The indicator’s mean value of is positive. As the amount of information increases, the average lateral offset distance increases. When the amount of information is the same, the driver’s lateral control ability of layout 1 is better than that of the layout 2.
Multifactor analysis of variance was used to explore the influence of gender, the layout, and the amount of information of guide signs on the drivers’ offset distance. It was found that only gender had a significant effect on the offset distance (F = 11.611, ). A further simple main effect analysis of gender shows that the offset distance of male drivers is 0.105 m larger than that of female drivers (95% confidence interval is 0.044 m–0.166 m).
3.2. Correlation Analysis of Evaluation Indicators
In order to explore the relationship between the driver’s legibility time and the driver’s behavior, the Pearson correlation coefficient was calculated as shown in Table 2, and the legibility time is significantly negatively correlated with the average vehicle speed (r = −0.818) and average acceleration (r = −0.836). The legibility time is significantly positively correlated with the lateral offset distance (r = 0.827). The Pearson correlation coefficient between legibility time and vehicle speed SD (r = 0.297) and acceleration SD (r = 0.443) is positive. When the vehicle is approaching the guide sign, the driver will break and decelerate to accurately identify the target road name on guide signs. If the guide sign is set reasonably, the drivers’ legibility time becomes shorter, and the average speed of the vehicle will be correspondingly higher. At the same time, if the acceleration SD, speed SD, absolute value of average acceleration, and lateral offset distance indicators are small, the driver drives smoothly.
3.3. Improved Grey Correlation Degree Method
3.3.1. Extraction Evaluation Factors
Based on the above analyses, we found that these indicators had certain differences for different guide signs. Evaluation relying on a single indicator is not comprehensive and accurate. There are often correlations among multiple indicators and the correlation between the indicators affects the results of the traditional grey correlation degree method evaluation to some extent. Factor analysis can recombine the information of the original variables, find out the common factors that affect the variables, and use a few common factors to explain most of the information of the original variables [40, 41]. In this paper, the factor analysis method and grey correlation degree method were combined to make up for each other’s shortcomings. The factor analysis method can reduce the dimensionality of the indicators. And the mathematical model of factor analysis is shown in as follows:where is the original variable; is the common factor; is the special factor; and is the constant.
The basic steps of factor analysis are shown in Figure 6. The specific steps of the factor analysis method refer to previous studies [42, 43].
First of all, the inverse indicators mean that the smaller the value of the indicators is, the better the evaluation object is. In order to ensure the correctness of the evaluation results, the inverse indicators were normalized by using opposite numbers. The z-score method was used to standardize the indicators. The standardized results are shown in Table 3. Before analysis, the correlation of the original data was tested. If the correlation of the original data is weak, it is impossible to extract common factors with common characteristics . Correlation analysis can generally use Bartlett’s test of sphericity and KMO (Kaiser-Meyer-Olkin) test . The results of the correlation analysis of variables are shown in Table 4. The KMO test value among the six indicators is 0.605, which is greater than the threshold of 0.5 . At the same time, the results of Bartlett’s test of sphericity show that the value is 0.003, which is less than 0.05. The analysis of the two indicators shows that there is a correlation between the variables, which meets the applicable requirements of factor analysis.
The calculation results of the variance explanation of the factors are shown in Table 5. The variance contribution rate of the first factor is 48.557%, and the cumulative variance contribution rate of the first two factors reaches 74.927%. Figure 7 is a gravel diagram. Factors 1 and 2 fall on the “steep slope,” so the first two factors can be used to evaluate guide signs.
The factor loading matrix that the value is greater than 0.3 is shown in Table 6. Factor 1 has a large load on the speed (x1) and the lateral offset distance (x5), which better reflects safety and traffic efficiency. Factor 2 has a large load on the SD of acceleration (x4) and acceleration (x3), which better reflects the stability of the vehicle operation.
Equations (2) and (3) are obtained from Table 7. The factor scores can be calculated through these equations, and the scores are shown in Table 8. It is found that the scores of the two factors have a tendency to decrease as the amount of information increases through comparison. Factor 1 score of layout 1 is higher than that of layout 2, indicating that the driver’s potential safety risk of layout 1 is less than that of layout 2; the overall score of factor 2 of layout 2 is higher than that of layout 1, indicating that when the guide sign adopts layout 2, the driver drives more stably compared with layout 1.where is Factor 1 score; ; .
3.3.2. Evaluation after Dimensionality Reduction of Factor Analysis
In order to conduct a comprehensive evaluation of the road signs, the grey correlation analysis method was used for quantitative evaluation, and we need not consider the importance of indicators . The procedure is as follows: Firstly, confirm the evaluation indicators set. Assume that there are n schemes to be evaluated, and we can set an evaluation set . There are m indicators for the evaluated schemes. Each scheme will have an indicator set . The indicator is defined as the value of No. j indicator in scheme i, and the indicators set is constructed. The factor 1 and factor 2 scores of the eight types of guide signs in Table 8 were used as the evaluation indicators set. Secondly, normalize the indicators. Equation (4) was used to obtain the normalized matrix and the normalized results are shown in Table 9. Thirdly, confirm the ideal scheme set. Assume ideal scheme set . The evaluation indicators are divided into cost-type indicators and benefit-type indicators. Since the indicators have been normalized, all the cost-type indicators in the ideal scheme set were set to 0, and the benefit-type indicators were set to 1. Therefore, the ideal scheme set was determined to be . Fourthly, comparison of evaluation scheme and ideal scheme. Equation (5) was used to compare the normalized matrix and the ideal scheme to obtain the matrix and the results are shown in Table 10. Fifthly, find the correlation coefficient. The two-level maximum difference , and the two-level minimum difference . We can find m = 0.788, and M = 1.260 in Table 10. The resolution coefficient ρ directly affects the calculation of the correlation coefficient, and ρ = 0.5 under normal circumstances . Equation (6) was used to calculate the correlation coefficient matrix and the results are shown in Table 11. Finally, the following equation was used to get the degree of correlation . The final results are shown in Table 12.
The final correlation coefficient of different guide signs is shown in Table 12. It can be seen that the correlation degree of sign 5 is the highest, and the correlation degree of sign 4 is the lowest. It shows that the time allocation of sign 5 for the driver to recognize the target road name is more reasonable, and the vehicle operating state is in better condition compared with sign 4. When adopting the same layout of guide signs, the correlation degree decreases with the amount of information increasing. Combined with the analysis in the 3.1 section, the increase of information amount does lead to the driver’s nervousness and unstable driving during visual cognition of the signs. The evaluation result of this evaluation method has a certain degree of rationality. In general, the grey correlation degree of layout 1 is lower than that of layout 2, indicating that the performance of layout 2 is better than that of layout 1.
This study has the following two contributions compared with previous studies. The first is to study the influence of different guide signs on the legibility of drivers and driving behavior. The second is to establish a general framework for the evaluation of guide signs, which can provide suggestions and references for the selection of guide sign schemes.
4.1. Analysis of Driver Behavior
The study analyzed the influence of the guide signs’ information amount on the legibility time. As the amount of information increases, the legibility time increases significantly . One possible explanation is that the increase of information amount leads to an increase in nontarget interference information. It will increase the time of drivers to search for the target road name on guide signs accordingly . When combined with the legibility time analysis, the driver behavior can be analyzed more reasonably. Drivers have limited gaze time when participating in traffic. The total time allocated to gaze at the guide signs and pay attention to driving operations is a constant value. The increase of legibility time will lead to the instability of the driver’s operation of the vehicle and the decrease of traffic efficiency.
As the amount of information increases, the average speed decreases, and the deceleration increases under the same guide sign layout. It may be due to the fact that when the information amount of the guide sign is small, the driving task of drivers to read the target road name is less psychologically pressured. So, the vehicle speed is higher, and the driving process is smoother. With the increase of information amount, it is more difficult for drivers to find the target road name on guide signs . So, the speed of the vehicle is reduced to ensure the completion of the target road name searching task and make the right driving operations at the intersection . When the information of amount is the same, the drivers’ speed is lower under the influence of layout 2 than that of layout 1. The driver’s deceleration of layout 1 is larger than that of layout 2. It indicates that the layout of the guide sign may disturb the driver’s perception to recognize the target road name on the guide sign . The higher the approaching speed, the less difficult the driver’s subjective perception to search the target road name on guide signs. Therefore, the driver’s subjective perception of the searching target road name pressure of layout 1 may be less than that of layout 2.
Analysis results of driving behavior among different driver types show that the lateral offset distance of male drivers is larger than that of female drivers, which may indicate that male drivers have a poor lane-keeping capability when approaching guide signs. Combining the analysis of the speed and acceleration of drivers, it can be seen that the speed of male drivers is higher than that of female drivers, and the deceleration of male drivers is larger than that of female drivers under the influence of the same guide sign. High speed and large deceleration may be the reason why it is difficult for male drivers to keep driving on the center of the road lane. For driving experience, the speed of novice drivers is lower than that of experienced drivers, which indicates that novice drivers drive more cautiously compared to experienced drivers when they are approaching guide signs.
4.2. Effectiveness of Evaluation Method
First of all, this study combined the legibility time and the objective indicators collected by the driving simulator, which expands the field of indicators selected for guide signs evaluation. At the same time, although previous studies have introduced the grey correlation analysis method in the evaluation of guide signs, the correlation between grey correlation indicators will affect the evaluation results to a certain extent. The paper combined the grey correlation analysis on the basis of the factor analysis to improve the accuracy of the evaluation results. The six evaluation indicators such as legibility time and vehicle speed were simplified into two unrelated indicators—factor 1 and factor 2. In addition, the factor analysis method could provide an improved direction for the perfection of guide signs. The final simplified indicator factor 1 reflects the safety and traffic efficiency and factor 2 reflects the stability of vehicle operation. Based on analyzing the score of each factor of layout 1 and layout 2, it is found that factor 1 scores of layout 1 are higher, and factor 2 scores of layout 2 are higher. This is consistent with the descriptive statistics of driver behavior, indicating that the factor analysis introduced can provide a certain degree of quantitative evaluation. The final evaluation result shows that the performance of layout 2 is better than that of layout 1 after comprehensive consideration of the influence of factor 1 and factor 2.
This study explored the influence of the guide signs’ information amount and layout on the legibility time and the driving behavior based on the driving simulation to carry out the task of visual cognition of guide signs. The comprehensive quantitative evaluation model was constructed based on factor analysis and grey correlation analysis method through analysis of five driver behavior indicators (speed, SD of speed, acceleration, SD of acceleration, and lateral offset distance). And the model could evaluate the effectiveness of guide signs from the perspective of safe driving.
The research results show that the legibility time of drivers increases with the increase of information amount on the guide signs, and there are differences in the driving behavior of drivers under the influence of the guide signs. There is no significant difference in the SD of speed and SD of acceleration. The speed, acceleration, and lateral offset distance of drivers show regular changes with the increase of guide signs’ information amount. As the amount of information on guide signs increases, the driver’s ability to control the vehicle deteriorates. Taking into account the correlation between the evaluation indicators, a comprehensive quantitative evaluation model was introduced based on factor analysis and the grey correlation analysis method to comprehensively evaluate the effectiveness of guide signs. The final result shows that when adopting the same layout on guide signs, the correlation of the evaluated scheme and the ideal scheme decreases with the information amount increasing. In general, the grey correlation degree of layout 1 is lower than that of layout 2, indicating that the performance of layout 2 is better than that of layout 1. The evaluation results are reasonable and are consistent with some conclusions, which could provide references for the selection and practical application of guide signs.
The data used to support the findings of this study are available from the corresponding author upon request.
Conflicts of Interest
The authors declare that they have no conflicts of interest.
This work was supported by National Key R&D Program of China (Grant no. 2019YFE0108000).
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