Urban expressway maintenance road section safety facility arrangement comprehensive evaluation method

Abstract

The invention discloses an urban expressway maintenance road section safety facility arrangement comprehensive evaluation method, which comprises the steps of S1, designing an arrangement mode of traffic safety facilities in an urban expressway working area, and constructing a driving simulation experiment scene based on the arrangement mode of the traffic safety facilities; s2, carrying out a driving simulation experiment based on the driving simulation experiment scene, and obtaining feature index data and subjective questionnaire data in the driving process of a driver; and S3, based on the obtained characteristic index data, evaluating the arrangement mode of the traffic safety facilities by adopting a non-integer rank RSR method based on an entropy weight method. According to the method, the traffic safety facility arrangement scheme of the urban expressway section working area is subjected to experimental evaluation through a driving simulation method, and the effectiveness of the safety facility arrangement mode is evaluated by using collected driving, eye movement and electroencephalogram data and adopting a non-integer rank RSR method based on an entropy weight method. The evaluation result can provide reference for determination of a traffic safety facility arrangement scheme in an urban expressway construction operation area.

Description

Technical Field

[0001] The present invention relates to the technical field of highway traffic safety, and particularly to a comprehensive evaluation method for the layout of safety facilities in the maintenance section of an urban expressway. Background Art

[0002] As an important part of the modern transportation system, urban expressways undertake huge traffic flows and transportation tasks. However, with the continuous increase in traffic volume, the proportion of large vehicles, and the operation time, diseases such as pavement wear and cracks inevitably occur on urban expressways. In recent years, the number of work areas on urban expressways has increased significantly. Research and statistical data show that due to the complex traffic environment in the work area, it is more difficult for drivers to drive on the sections near the work area than on normal sections, and the accident rate is also higher than that of standard sections. Therefore, work area safety has become a popular research direction in the field of traffic safety. Among them, warning drivers before they enter the work area of an urban expressway is an important step to ensure traffic safety in the work area. Reminding drivers before they are in danger can effectively reduce their running speed, so that they can better respond and prepare, and reduce the incidence of traffic accidents.

[0003] The setting of traffic safety facilities can effectively remind drivers of the alertness and recognition of the maintenance work area, and can effectively guide vehicle diversion and reduce traffic conflict points. At present, the setting of traffic safety facilities in the maintenance area of urban expressways in China mainly refers to the "Highway Maintenance Safety Operation Regulations" and "Road Traffic Signs and Markings Part 4: Work Area". However, there are certain differences in the settings for the same situation between different standards. The setting position and information notification range of traditional physical signs have certain limitations. Drivers are prone to miss warning information and it is difficult to obtain long-distance road condition information in a timely manner. Therefore, evaluating the effectiveness of the traffic safety facility setting plan in the work area of urban expressway sections has important guiding significance for ensuring traffic safety in urban expressway sections. Summary of the Invention

[0004] In view of the above problems, the present invention provides a comprehensive evaluation method for the layout of safety facilities in the maintenance section of an urban expressway, which can comprehensively evaluate the effectiveness of the traffic safety facility layout plan in the work area of urban expressway sections, and can provide a reference for the layout of safety facilities in the work area.

[0005] In order to achieve the above technical objectives, the technical solution adopted by the present invention is: a comprehensive evaluation method for the layout of safety facilities in the maintenance section of an urban expressway, including:

[0006] S1. Design the layout method of traffic safety facilities in the work area of urban expressway sections, and construct a driving simulation experimental scenario based on the layout method of traffic safety facilities;

[0007] S2. Conduct a driving simulation experiment based on the constructed driving quasi-experiment scenario to obtain the characteristic index data and subjective questionnaire data of the driver during the driving process;

[0008] S3. Based on the obtained characteristic index data, evaluate the layout method of traffic safety facilities by using the non-integer rank RSR method based on the entropy weight method.

[0009] In some embodiments, in S2, the number of drivers participating in the experiment is estimated by using the G*Power 3.1.9 software based on the α error and the effect size.

[0010] In some embodiments, the characteristic index data in S2 includes driving behavior index data, eye movement behavior index data, and electroencephalogram index data;

[0011] The driving behavior index data includes the driving speed, the eye movement behavior index data includes the pupil area and the average fixation time, and the electroencephalogram index data includes the β value and the (α + θ) / β value.

[0012] In some embodiments, the acquisition of the eye movement behavior index data starts at 113 m in front of the road construction sign, and the original eye movement data is exported by using the D-Lab software after the acquisition is completed;

[0013] The driving behavior index data is recorded every 0.03 s, and the driving behavior index data is recorded throughout the experiment.

[0014] In some embodiments, after each experiment is completed, a questionnaire is provided for the drivers participating in the experiment to answer, and the subjective questionnaire data is obtained; the subjective questionnaire data includes subjective safety, subjective alertness, and subjective comfort.

[0015] In some embodiments, step S3 includes:

[0016] S3.1. Obtain the weights of each evaluation index by using the entropy weight method;

[0017] S3.2. Establish an evaluation model by using the non-integer RSR method to evaluate the traffic facility layout plan.

[0018] In some embodiments, S3.1 specifically includes the following steps:

[0019] S3.11. Determine the evaluation object, establish an evaluation system, and construct an initial matrix A = (r ij ) m*n , r ij represents the value of the jth index of the ith evaluation object, m is the number of evaluation objects, and n is the number of evaluation indexes;

[0020]

[0021] S3.12. Standardize the evaluation indicators and normalize the data to [0, 1];

[0022] When j is a positive indicator:

[0023]

[0024] When j is a negative indicator:

[0025]

[0026] S3.13. Calculate the entropy value of each indicator, and the calculation formula is as follows:

[0027]

[0028]

[0029] Among them, f ij is the index value weight of the i-th item under the j-th indicator;

[0030] Correct f ij and shift the standardized r ij as shown in the following formula (6):

[0031]

[0032] Among them, a is the shift amplitude;

[0033] S3.14. Calculate the entropy weight w j of the j-th indicator:

[0034]

[0035] Among them, w j represents the weight of the j-th indicator.

[0036] In some embodiments, the shift amplitude a is taken as 0.0001.

[0037] In some embodiments, S3.2 specifically includes the following steps:

[0038] S3.21. List the original data matrix:

[0039] According to the evaluation purpose, select the evaluation indicators, distinguish the high-quality indicators from the low-quality indicators, and the original matrix is as follows:

[0040]

[0041] Among them, x ij is the j-th evaluation indicator of the i-th sample;

[0042] S3.22. Calculate the rank value:

[0043] Rank of high - priority indicators:

[0044]

[0045] Rank of low - priority indicators:

[0046]

[0047] Among them, n is the number of evaluation objects, X max and X min are the maximum and minimum values in the original sequence;

[0048] S3.23. Calculate the rank - sum ratio (RSR) value:

[0049]

[0050] The distribution of RSR refers to the specific cumulative frequency expressed by the probit value of probability;

[0051] Taking the probit value corresponding to the cumulative frequency as the independent variable and the RSR value as the dependent variable, calculate the regression equation;

[0052] S3.26. Evaluate the effectiveness of the traffic facility layout plan according to the RSR method.

[0053] Adopting the above - mentioned technical solution, compared with the prior art, the beneficial effects of the present invention are as follows:

[0054] The present invention conducts experimental evaluation on the traffic safety facility layout plan in the work area of the urban expressway section through the driving simulation and emulation method, utilizes the collected driving, eye movement, and electroencephalogram data, and comprehensively evaluates the effectiveness and rationality of the traffic safety facility layout method by using the non - integer rank RSR method based on the entropy weight method. The evaluation results can provide a reference for determining the traffic safety facility layout plan in the construction operation area of the urban expressway. Brief Description of the Drawings

[0055] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following - described drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0056] Figure 1 It is the traffic safety facility layout method in Scenario 1;

[0057] Figure 2It is the layout method of safety facilities in Scenario 2;

[0058] Figure 3 It is the layout method of safety facilities in Scenario 3;

[0059] Figure 4 It is the layout method of safety facilities in Scenario 4;

[0060] Figure 5 It is the layout method of safety facilities in Scenario 5;

[0061] Figure 6 It is the speed change of the driver driving in the warning area under different scenarios;

[0062] Figure 7 It is the change of the driver's pupil area under different scenarios;

[0063] Figure 8 It is the measurement result of the driver's average fixation time under different scenarios;

[0064] Figure 9 It is the measurement result of the driver's β value under different scenarios;

[0065] Figure 10 It is the measurement result of the driver's (α + θ) / β value under different scenarios;

[0066] Figure 11 It is the scoring result of the driver's subjective questionnaire;

[0067] Figure 12 It is the schematic flow diagram of the present invention. Specific implementation manner

[0068] The present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be particularly noted that the following embodiments are only used to illustrate the present invention, but do not limit the scope of the present invention. Similarly, the following embodiments are only partial embodiments of the present invention rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0069] Referring to the attached Figure 12 As shown, the present invention provides a comprehensive evaluation method for the layout of safety facilities in the maintenance section of an urban expressway, including:

[0070] S1. Design the layout method of traffic safety facilities in the work area of the urban expressway section, and construct a driving simulation experimental scenario based on the layout method of traffic safety facilities;

[0071] S2. Conduct driving simulation experiments based on the constructed driving quasi-experimental scenarios, obtain the characteristic index data and subjective questionnaire data during the driving process of the drivers, and the number of drivers participating in the experiment is estimated using G*Power 3.1.9 software based on the α error and effect size.

[0072] Among them, the characteristic index data includes:

[0073] Driving behavior indicators

[0074] Driving speed (km / h): Take the cross-sectional vehicle speeds of multiple subjects on the work area section. The average value of the cross-sectional vehicle speeds can present the driving state of the vehicle at a specific position and reflect the speed change of the vehicle on the research section.

[0075] Eye movement behavior indicators

[0076] Pupil area (pixel): The pupil area represents the visual adaptability and load level of the driver. The larger the value, the greater the mental load of the driver.

[0077] Average fixation time (ms): It refers to the ratio of the cumulative fixation time of fixation behavior to the number of fixations. The longer the value, the more attention resources the driver allocates to the target information.

[0078] EEG indicators

[0079] β value: β waves tend to be generated when people are mentally tense and emotionally excited. As the driver's attention and alertness decrease, the β wave activity also decreases.

[0080] (α + θ) / β value: This indicator is the ratio of the sum of the driver's EEG α value and θ value to the β value. The higher the value, the greater the driver's load and the higher the mental fatigue level.

[0081] Among them, the data collection of eye movement behavior indicators starts at 113 m in front of the road construction sign. After the collection is completed, the original eye movement data is exported using D-Lab software; the vehicle dynamics module of the driving simulator records the driving behavior indicator data every 0.03 s, and the driving behavior indicator data is recorded throughout the experiment. The collected EEG data is exported to MATLAB software for preprocessing and analysis.

[0082] After each experiment is completed, a questionnaire is provided for the drivers participating in the experiment to answer, and subjective questionnaire data is obtained; the subjective questionnaire data includes subjective safety, subjective alertness, and subjective comfort.

[0083] S3. Based on the obtained characteristic index data, and using the non-integer rank RSR method based on the entropy weight method to evaluate the layout method of traffic safety facilities, specifically including:

[0084] S3.1. Obtain the weights of each evaluation index by using the entropy weight method, which specifically includes the following steps:

[0085] S3.11. Determine the evaluation objects, establish an evaluation system, and construct an initial matrix A = (r ij ) m*n , r ij represents the value of the j-th index of the i-th evaluation object, m is the number of evaluation objects, and n is the number of evaluation indexes;

[0086]

[0087] S3.12. Standardize the evaluation indexes and standardize the data to [0, 1];

[0088] When j is a positive index:

[0089]

[0090] When j is a negative index:

[0091]

[0092] S3.13. Calculate the entropy value of each index, and the calculation formula is as follows:

[0093]

[0094]

[0095] Among them, f ij is the index value weight of the i-th item under the j-th index; in order to make the calculation result of lnf ij meaningful, it is necessary to correct f ij , that is, translate the standardized r ij , as shown in the following formula (6):

[0096]

[0097] Among them, a is the translation amplitude, taking 0.0001.

[0098] S3.14. Calculate the entropy weight w j of the j-th index:

[0099]

[0100] Among them, w j represents the weight of the j-th index.

[0101] S3.2. Establish an evaluation model by using the non-integer order RSR method to evaluate the traffic facility layout plan, which specifically includes the following steps:

[0102] S3.21. List the original data matrix:

[0103] According to the evaluation purpose, select evaluation indicators, distinguish high - priority indicators from low - priority indicators, and the original matrix is as follows:

[0104]

[0105] where x ij is the j - th evaluation indicator of the i - th sample;

[0106] S3.22. Calculate the rank value:

[0107] Rank of high - priority indicators:

[0108]

[0109] Rank of low - priority indicators:

[0110]

[0111] where n is the number of evaluation objects, X max and X min are the maximum and minimum values in the original sequence;

[0112] S3.23. Calculate the rank - sum ratio (RSR) value:

[0113]

[0114] S3.24. The distribution of RSR refers to the specific cumulative frequency expressed by the probit value of probability;

[0115] S3.25. Taking the probit value corresponding to the cumulative frequency as the independent variable and the RSR value as the dependent variable, calculate the regression equation;

[0116] S3.26. Evaluate the effectiveness of the traffic facility layout plan according to the RSR method, and conduct an analysis of variance on the results to determine whether each ranking result has statistical significance.

[0117] Example 1

[0118] This example provides a specific implementation case based on the solution of the present invention, as follows:

[0119] 1. Experimental scenario

[0120] Based on the safety facility setting methods in domestic and international highway maintenance areas, different safety facility combination schemes were set. Based on the driving simulation system platform, an experimental scenario of an urban expressway maintenance operation area with multiple combination schemes was constructed, and the most reasonable infrastructure combination design scheme was evaluated using the collected eye movement, electroencephalogram, and driving data.

[0121] This experiment mainly analyzed the impact of the safety facility design in the maintenance area of an eight-lane urban expressway on drivers' driving behaviors, evaluated different safety facility design schemes, and thus selected the ones to guide the actual application. The driving simulation experiment was adopted, and the scenario was set as a two-way eight-lane urban expressway, flat and without slopes, with a warning area length of 1000 meters, an upstream transition area and a buffer zone of 200m, a work area of 200m, and a downstream transition area and a termination area of 50m. The speed limit signs adopted secondary speed limits, with speed limit values of 100 km / h and 80 km / h respectively, and the distance between the two signs was set at 250m. The settings of the rest of the safety facilities were placed according to the requirements in the "Highway Maintenance Safety Operation Regulations".

[0122] In the actual work area of an urban expressway, there is a phenomenon that some drivers ignore the safety facilities and traffic cones in the warning area, resulting in vehicles straying into the closed lane and causing harm to construction workers and other traffic participants (Abdallah et al., 2024). To avoid this phenomenon, it is necessary to enhance the warning effect of the safety facilities in the warning area on drivers. Therefore, the following optimization schemes were proposed in this experiment. The basic scheme was to deploy safety facilities in accordance with the "Highway Maintenance Safety Operation Regulations", and the enhanced scheme was to add safety facilities on the basis of the basic scheme.

[0123] Scenario 1 (Scheme1): Basic scheme, set according to the standard

[0124] According to the "Highway Maintenance Safety Operation Regulations", the existing layout scheme of the maintenance area of an eight-lane urban expressway is as follows Figure 1 As shown, the improvable schemes for the current safety facility layout are as follows:

[0125] (1) Further improve the visibility of traffic signs in the maintenance area, enhance drivers' alertness, pay attention to the construction area ahead, and reduce the occurrence of traffic accidents.

[0126] (2) During the driving process in the work area, drivers have a single source of driving information, and the driving information they obtain comes from road signs. Multiple sources of driving information can be added to further guide drivers in the work area.

[0127] (3) The environment in the work area of an urban expressway is complex and changeable. Relevant equipment can be added to prompt the work area situation in real time to further remind drivers to drive safely.

[0128] Scenario 2 (Scheme2): Safety facility signs in the maintenance area plus red flags

[0129] To further improve the visibility of road traffic signs in the work area and attract the attention of drivers, in Scenario 2, red flags are placed on safety facilities to better remind drivers to pay attention to driving safety and avoid traffic accidents caused by failure to receive information in a timely manner. The relevant layout scenarios are as follows Figure 2 .

[0130] Scenario 3 (Scheme 3): Warning strobe lights are added to the road surface

[0131] The road surface dynamic light effect induction facility can transmit the warning information that there is a work area ahead. The road surface selects flashing red lights that can improve the driver's alertness and is longitudinally continuously arranged at equal intervals on both sides of the lane lines on the side where the work area is located. The section with the road surface dynamic light effect induction facility includes the entire warning area of the work area. Considering the running speed of the vehicle and the visual recognition effect comprehensively, the layout interval of the road surface dynamic light effect induction facility is 10 meters / group. The relevant layout plan is as follows Figure 3 as shown

[0132] Scenario 4 (Scheme 4): Voice navigation

[0133] Relevant research shows that voice navigation broadcasts can improve the driver's information perception level when driving on complex roads. Based on this, this plan adds short voice navigation. The voice navigation is played throughout the journey to guide the driver to drive safely in the work area and reach the destination smoothly. The relevant layout plan is as follows Figure 4 as shown

[0134] Scenario 5 (Scheme 5): Roadside VMS signs

[0135] As an important form of release in ITS, VMS can display road conditions, traffic control, meteorological and other information in real time at key traffic positions. It plays a significant role in improving road utilization rate, ensuring driving safety, etc. and is widely used in the management and control of the traffic system. In this plan, VMS signs are set in front of the work area to remind the driver of the traffic status in the work area in real time. The relevant layout plan is as follows Figure 5 .

[0136] 2. Experimental equipment

[0137] In this study, vehicle motion state data, driver's electroencephalogram and eye movement data during simulated driving are collected. The experimental equipment is as follows

[0138] (1) Driving simulation system

[0139] The DRS-10002.0 automotive driving simulation system is a cockpit with a moving vehicle and can complete automotive driving simulation experiments. The automotive driving simulation system is divided into a cockpit, a console, and a display, and can collect the behavioral parameters of the driver in real time. The cockpit includes various components required for operating the vehicle, such as a steering wheel, pedals, a gear shifter, and an instrument panel. The viewing angle of the display screen is 120°, which consists of three 60-inch 4K LCDs. The auxiliary audio system can simulate environmental and vehicle sounds, and the control system realizes the control of the loaded experimental scenarios and the collection of experimental data.

[0140] (2) 32-channel NE wireless EEG system

[0141] The 32-channel NE wireless EEG system has a 24-bit high resolution and a sampling rate of 500 SPS, and is a portable wearable system that can be widely used in audience scenarios. The system can receive the brain waves of people in real time through electrodes to ensure accurate collection of EEG signals. The frequency band sampling rate is 500 sps. The resolution is 24-bit - 0.05 uv, the bandwidth is 0 - 250 Hz, and the noise is less than 1 uv rms (0 - 250 Hz).

[0142] (3) Dikablis eye tracker

[0143] The Dikablis eye tracker is used together with the analysis software D-Lab to collect the eye movement behavior characteristics of the subjects. This device has the characteristics of being light in weight and simple to operate. When collecting data, the frequency of the eye tracker is 60 Hz, and the accuracy range is from 0.1° to 0.3°.

[0144] 3. Number of participants

[0145] The G*Power 3.1.9 software was used to estimate the sample size. The α error was 0.05 and the effect size was 0.25. The results showed that the sample size required to achieve a statistical power of 0.95 was at least 22. A total of 32 participants were recruited for this experiment, including 22 males (68.75%) and 10 females (31.25%). The gender ratio distribution roughly conforms to the current statistical characteristics of Chinese drivers. The age range of the participants was from 19 to 34 years old (M = 25.81, SD = 4.36), and all had one year or more of driving experience (M = 2.22, SD = 1.38), with good physical and mental health and normal vision.

[0146] 4. Experimental procedures

[0147] (1) Before conducting the driving simulation experiment, a driving simulation pre-experiment was carried out on the participants to familiarize them with and understand the driving simulation instrument. The experimental procedures and precautions were explained to the drivers, and the participants were informed of the meanings of the relevant signs in the driving experimental scenarios to ensure that the experimenters had a clear understanding of the experimental process.

[0148] (2) The experimenters carried out simulated driving under different experimental scenarios as required. The order of the experimental scenarios was shuffled to avoid memory effects. After completing one scenario, the participants rested for 5 minutes to relieve fatigue and ensure the accuracy of subsequent experiments.

[0149] (3) After completing an experiment, a questionnaire was provided for the participants to answer in order to test the rationality of the settings of different safety facilities in the work area.

[0150] (4) After one participant completed all the experiments, another participant entered the simulation cabin to complete the next experiment. This process was repeated until all the participants had completed the experiments.

[0151] 5. Experimental indicators

[0152] The sign settings in the maintenance area of the eight-lane urban expressway should effectively improve the driver's alertness and driving safety. The driver's physiological reactions and driving behavior stability are closely related to driving safety. Therefore, the indicators were selected from the following aspects in this experiment:

[0153] 5.1 Driving behavior indicators

[0154] Driving speed (km / h): The cross-sectional vehicle speeds of multiple subjects on the work area section were taken. The average value of the cross-sectional vehicle speeds can present the driving state of the vehicle at a specific position and reflect the speed change of the vehicle on the research section.

[0155] 5.2 Eye movement behavior indicators

[0156] Pupil area (pixel): The pupil area represents the driver's visual adaptability and load level. The larger the value, the greater the mental load of the driver.

[0157] Average fixation time (ms): It refers to the ratio of the cumulative fixation time of fixation behaviors to the number of fixations. The longer the value, the more attention resources the driver allocates to the target information.

[0158] 5.3 EEG indicators

[0159] β value: β waves tend to be generated when people are mentally tense and emotionally excited. As the driver's attention and alertness decrease, the β wave activity also decreases.

[0160] (α + θ) / β value: This indicator is the ratio of the sum of the driver's EEG α value and θ value to the β value. The higher the value, the greater the driver's load and the higher the mental fatigue level.

[0161] 6. Data collection and processing

[0162] In the experiment, the data collection of eye movement behavior indicators started 113 m in front of the road construction sign. After the collection was completed, the original eye movement data was exported using D-Lab software. The vehicle dynamics module of the driving simulator recorded the driving behavior indicator data every 0.03 s, and the driving behavior indicators were recorded throughout the experiment. The collected EEG data was exported to MATLAB software for preprocessing and analysis.

[0163] 7. Experimental Results

[0164] The experimental results showed that compared with the basic scheme, in the optimized scheme, various indicators of the driver were different. To further verify this hypothesis, the research used one-way ANOVA for verification.

[0165] 7.1 Driving Speed

[0166] Figure 6 It shows the speed change of the driver when driving in the warning area. Table 1 shows the results of the analysis of variance. The results show that compared with the original scheme, the results of the speed change in the optimized scheme are different (F = 8.472, p < 0.001). It can be seen from the figure that the speed of the driver decreases first and then slowly increases when driving in the warning area. Due to the influence of the warning signs in the work area, the driver will gradually reduce the speed to ensure driving safety when entering the warning area, and will gradually recover some speed after adapting to the driving environment, and finally tend to be gradually stable or slightly decelerated. Among them, the ranking of the influence of each scheme on the driver's speed is: Scenario 5 > Scenario 3 > Scenario 4 > Scenario 2 > Scenario 1. VMS can provide real-time road information and has a strong warning effect. After receiving clear and timely messages, the driver can react in time to cope with the changes in road conditions. And the red dynamic flashing lights provide a strong visual stimulus. Therefore, compared with Scenario 2 and Scenario 3, the driver reduces the speed by a greater margin. Although the navigation voice can remind the driver to pay attention to the complex traffic conditions in the work area, it will also cause the driver to be distracted and thus unable to control the vehicle speed well.

[0167] Table 1 Calculation Results of Speed Importance

[0168]

[0169] 7.2 Eye Movement Behavior Indicators

[0170] 7.2.1 Pupil Area (PupilArea)

[0171] The pupil area changes of the drivers under different schemes are as follows Figure 7, the results of the analysis of variance are shown in Table 2 below. The results show that compared with the original traffic safety facility setting plan, the pupil areas of drivers in Scenario 2 and Scenario 3 are increased. This is because the red flags and red strobe lights can cause visual alertness reactions in drivers. It can be seen from the analysis of variance that the pavement with red strobe lights in Scenario 3 can cause drivers to be more nervous and concentrated, resulting in a further increase in the pupil area of drivers. While Scenario 4 and Scenario 5 both provide more effective driving information and reduce the visual burden on drivers.

[0172] Table 2 Calculation results of the importance of pupil area

[0173]

[0174] 7.2.2 Mean fixation time

[0175] The measurement results of the mean fixation time under different plans are as follows Figure 8 , the results of the analysis of variance are shown in Table 3 below. Compared with the original plan, different optimization plans have all increased the mean fixation time of drivers during driving, and there are significant differences (F = 24.169, p < 0.05). This shows that the improved plan can increase drivers' attention to target information. Specifically for each improved plan, adding navigation voice announcements has the best effect on increasing the mean fixation time of drivers. This is because voice prompts can effectively guide drivers' attention and do not increase their visual burden. While the pavement with red signal lights has the worst improvement effect. This is because drivers need to distract their attention to other areas of the road surface during driving, which will weaken its guiding effect.

[0176] Table 3 Calculation results of the importance of mean fixation time [[ID=...]]

[0177]

[0178] 7.3 EEG indicators

[0179] 7.3.1 β value

[0180] The measurement results of the β wave values of drivers under different traffic safety facility layout plans in the working area are as follows Figure 9 , the relevant analysis of variance is shown in Table 4 below. It can be seen from the table that there are significant differences in the β wave values of drivers under different plans (F = 19.17, p < 0.001). It can be obtained from the figure that adding VMS has the greatest impact on the β waves of drivers. This is because VMS can provide rich real-time effects, resulting in an increase in drivers' cognitive load and corresponding increase in alertness. While the red lights, as dynamic visual cues, can bring relatively high stimuli and attract continuous attention from drivers. Therefore, they also have a relatively large impact on improving drivers' alertness. While adding red flags only increases the visual stimuli of drivers, and there is no significant difference in improving drivers' alertness (p = 0.386).

[0181] Table 4 Calculation results of β value importance

[0182]

[0183]

[0184] 7.3.2 (α + θ) / β value

[0185] The measurement results of (α + θ) / β value are as follows Figure 10 , and the results of its analysis of variance are shown in Table 5 below. Except for the scenario 2 plan, the results of the other plans have all changed significantly compared with the basic plan (p < 0.05). Compared with the basic plan, the (α + θ) / β values of the drivers under the other plans have all increased except for the scenario 4 plan. This shows that voice navigation can reduce the fatigue state of drivers and effectively concentrate the attention of drivers. And the other plans such as setting up red flags, red dynamic lights and VMS bring in additional visual information, which is likely to cause an increase in the attention load of drivers.

[0186] Table 5 Calculation results of (α + θ) / β value importance

[0187]

[0188] 7.4 Subjective questionnaire analysis

[0189] The subjective questionnaire analysis is carried out in three parts, namely subjective security, subjective alertness and subjective comfort. The measurement results are shown in Figure 11 . As can be seen from the figure, compared with the original plan, the improved plans have all improved the subjective safety scores of drivers, indicating that the effects of each improved plan on improving the driving safety of drivers are effective under the subjective cognition of drivers. Among them, the experimental personnel gave the highest score to the scenario 5 plan, which verifies that the display of dynamic information based on vision (such as VMS) significantly improves the sense of security of drivers. And each improved plan has also improved the subjective alertness of drivers. Among them, the scenario 4 plan has the most significant improvement in the scores of drivers, because the auditory information has less interference with the driving task and the driver can more easily maintain driving alertness. In terms of driving comfort, the scenario 3 plan reduces the driving comfort of drivers because the increase of red dynamic flashlights on the road surface causes too much visual stimulation to drivers, which affects the driving experience of drivers to a certain extent. And adding voice navigation information will not increase the visual burden of drivers, nor does it require drivers to make quick responses, so their overall driving experience is more comfortable.

[0190] 8. Comprehensive impact analysis

[0191] 8.1 RSR Comprehensive Evaluation Method

[0192] In this embodiment, the non-integer rank RSR method based on the entropy weight method is adopted, and the driving behavior data, eye movement behavior data, electroencephalogram data and subjective questionnaire data obtained from the experiment are selected as indicators to evaluate the layout forms of different work area schemes and facilities.

[0193] 8.1.1 Determination of Index Weights

[0194] The entropy weight method is an objective weight assignment method. It calculates the entropy weight by referring to and combining the dispersion degree of each index data, and modifies it to obtain a scientific and objective index weight. The specific steps are as follows:

[0195] (1). Determine the evaluation object, establish an evaluation system, and construct an initial matrix A = (r ij ) m*n , r ij represents the value of the j-th index of the i-th evaluation object, m is the number of evaluation objects, and n is the number of evaluation indicators.

[0196] In this experiment, a total of 5 layout results of traffic safety facilities in highway construction areas were evaluated. Among them, m = 5 and n = 8.

[0197]

[0198] (2). Standardize the evaluation indicators and standardize the data to [0,1];

[0199] When j is a positive index:

[0200]

[0201] When j is a negative index:

[0202]

[0203] (3). Calculate the entropy value of each index. The calculation formula is as follows:

[0204]

[0205]

[0206] Among them, f ij is the index value weight of the i-th item under the j-th index; in order to make the calculation result of lnf ij meaningful, it is necessary to modify f ij , that is, translate the standardized r ij as shown in the following formula (6):

[0207]

[0208] Among them, a is the translation amplitude, taking 0.0001.

[0209] (4) Calculate the entropy weight w of the j-th index j :

[0210]

[0211] Among them, w j represents the weight of the j-th index.

[0212] Through the above steps, the weights of the selected indicators in this experiment are calculated as shown in Table 6 below:

[0213] Table 6 Weight distribution of indicators

[0214]

[0215] 8.1.2 Evaluation model

[0216] In this experiment, the non-integer RSR method is used to establish an evaluation model. This method ranks the index values in a way similar to linear interpolation to improve the deficiency of the RSR method's ranking method. There is a quantitative linear correspondence between the ranked order and the original index value, thus overcoming the disadvantage of the RSR method that quantitative information of the original index value is easily lost during ranking. The steps for establishing the model are as follows:

[0217] (1) List the original data matrix:

[0218] According to the evaluation purpose, select evaluation indicators, distinguish high-quality indicators from low-quality indicators, and the original matrix is as follows:

[0219]

[0220] Among them, x ij is the j-th evaluation indicator of the i-th sample;

[0221] (2) Calculate the rank value:

[0222] Rank of high-quality indicators:

[0223]

[0224] Rank of low-quality indicators:

[0225]

[0226] Among them, n is the number of evaluation objects, X max and X min are the maximum and minimum values in the original sequence;

[0227] (3) Calculate the rank sum ratio (RSR) value:

[0228]

[0229] (4) The distribution of RSR refers to the specific cumulative frequency expressed by the probit value.

[0230] (5) Using the probit value corresponding to the cumulative frequency as the independent variable and the RSR value as the dependent variable, calculate the regression equation.

[0231] (6) Evaluate and rank the effectiveness of traffic facility layout plans according to the RSR method, and conduct an analysis of variance on the results to determine whether each ranking result has statistical significance.

[0232] 8.1.3 Model Result Analysis

[0233] According to the above steps, use the RSR method to evaluate different traffic facility layout plans. First, calculate the RSR value and the probit value. Then, using the probit value as the independent variable and the RSR value as the dependent variable, derive the linear regression equation for fitting the RSR estimated value. The obtained fitting linear regression equation is as follows:

[0234] y = -0.553 + 0.221 * Probit (12)

[0235] Conduct an F-test on the model. The analysis result shows that p = 0.016, R 2 = 0.853, and the model is well constructed. According to the RSR estimated value calculated by the regression equation, divide the evaluation objects into three grades. The larger the grade number, the higher the grade level, that is, the better the effect. The ranking and comprehensive evaluation results are shown in Table 7 below.

[0236] Table 7 Comprehensive Evaluation Results Based on Indicators

[0237]

[0238] As can be seen from the above table, the comprehensive evaluation rankings of various traffic safety facility layout scenarios are as follows: Scenario 4 plan > Scenario 5 plan > Scenario 2 plan > Scenario 3 plan > Scenario 1 plan. Compared with the initial layout plan, the optimized plan improves the driving safety of drivers in the work area. Generally speaking, adding voice navigation to guide drivers in the work area has the best effect, followed by adding VMS warning signs.

[0239] Taking the maintenance operation area with the inner two lanes closed in a two-way eight-lane highway as an example, four optimization schemes for traffic safety facilities in highway maintenance areas are designed. Based on driving simulation experiments, the driving behavior data and subjective feeling results of drivers are measured. The driving speed, eye movement index, electroencephalogram index and subjective questionnaire score are selected to construct an RSR comprehensive evaluation to evaluate the optimization schemes. The evaluation results can provide reference for determining the layout scheme of traffic safety facilities in the construction operation area of urban expressways.

[0240] The above are only partial embodiments of the present invention, and thus do not limit the protection scope of the present invention. Any equivalent device or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be similarly included in the patent protection scope of the present invention.

Claims

1. A comprehensive evaluation method for the layout of safety facilities on the maintenance sections of urban expressways, characterized in that, Including: S1. Design the layout of traffic safety facilities in the work area of urban expressway sections, and construct a driving simulation experiment scenario based on the layout of traffic safety facilities; S2. Conduct a driving simulation experiment based on the constructed driving simulation experiment scenario to obtain characteristic index data and subjective questionnaire data during the driving process of drivers; S3. Based on the obtained characteristic index data, evaluate the layout method of traffic safety facilities by using the non-integer rank RSR method based on the entropy weight method.

2. The comprehensive evaluation method for the layout of safety facilities in the maintenance section of urban expressways according to claim 1, wherein, In S2, the number of drivers participating in the experiment is estimated by using G*Power 3.1.9 software based on α error and effect size.

3. The comprehensive evaluation method for the layout of safety facilities on the maintenance section of urban expressways according to claim 1, characterized in that, The characteristic index data in S2 includes driving behavior index data, eye movement behavior index data, and electroencephalogram index data; The driving behavior index data includes driving speed, the eye movement behavior index data includes pupil area and average fixation time, and the electroencephalogram index data includes β value and (α + θ) / β value.

4. The comprehensive evaluation method for the layout of safety facilities in the maintenance section of an urban expressway according to claim 3, wherein, The acquisition of the eye movement behavior index data starts at 113 m in front of the road construction sign, and the original eye movement data is exported by using D-Lab software after the acquisition is completed; The driving behavior index data is recorded every 0.03 s, and the driving behavior index data is recorded throughout the experiment.

5. The comprehensive evaluation method for the layout of safety facilities in the maintenance section of an urban expressway according to claim 1, characterized in that, After each experiment is completed, a questionnaire is provided for the drivers participating in the experiment to answer to obtain subjective questionnaire data; the subjective questionnaire data includes subjective safety, subjective alertness, and subjective comfort.

6. The comprehensive evaluation method for the layout of safety facilities in the maintenance section of the urban expressway according to claim 1, characterized in that, Step S3 includes: S3.

1. Obtain the weights of each evaluation index by using the entropy weight method; S3.

2. Establish an evaluation model by using the non-integer RSR method to evaluate the traffic facility layout plan.

7. The comprehensive evaluation method for the layout of safety facilities in the maintenance section of an urban expressway according to claim 6, wherein S3.1 specifically includes the following steps: S3.

11. Determine the evaluation object, establish an evaluation system, and construct an initial matrix A = (r ij ) m*n , r ij represents the value of the j-th index of the i-th evaluation object, m is the number of evaluation objects, and n is the number of evaluation indices; S3.

12. Standardize the evaluation index and standardize the data to [0, 1]; When j is a positive index: When j is a negative index: S3.

13. Calculate the entropy value of each index, and the calculation formula is as follows: Among them, f ij is the index value weight of the i-th item under the j-th index; Correct f ij Perform correction, and translate the standardized r ij as shown in the following formula (6): where a is the translation amplitude; S3.

14. Calculate the entropy weight w of the j-th indicator j :[[-END]] Among them, w j represents the weight of the j-th index.

8. The comprehensive evaluation method for the layout of safety facilities in the maintenance section of an urban expressway according to claim 7, characterized in that The translation amplitude a takes 0.0001.

9. The comprehensive evaluation method for the layout of safety facilities in the maintenance section of an urban expressway according to claim 7, characterized in that, S3.2 specifically includes the following steps: S3.

21. List the original data matrix: According to the evaluation purpose, select the evaluation index, distinguish the high-quality index and the low-quality index, and the original matrix is as follows: where x ij is the j-th evaluation index of the i-th sample; S3.

22. Calculate the rank value: Rank of high-quality index: Rank of low-quality index: where n is the number of evaluation objects, X max and X min are the maximum and minimum values in the original sequence; S3.

23. Calculate the rank sum ratio RSR value: The distribution of RSR refers to the value expressed by the probability unit Probit for the specific cumulative frequency; S3.

25. Take the probability unit value Probit corresponding to the cumulative frequency as the independent variable and the RSR value as the dependent variable to calculate the regression equation; S3.

26. Evaluate the effectiveness of the traffic facility layout plan according to the RSR method.

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