TRB ID Number : 01-0535 Evaluation of Driver's Psychophysiological Load at Freeway Merging Area Submitted for Presentation and Publication for 2001 TRB Annual Meeting Dr. Myungsoon Chang, Professor Department of Transportation Engineering, Hanyang University 1271 Sa 1 Dong, Ansan, Kyunggido, Korea, 425-791 Tel : +82-31-400-5151 Fax : +82-31-406-6290 E-mail : [email protected] Mr. Juyoung Kim, Researcher Korea Highway Corporation 293-1, Kumtodong, Sujonggu, Sungnam, Kyunggido, Korea, 461-380 Tel : +82-2-2230-4652 Fax : +82-2-2230-4182 E-mail : [email protected] Dr. kyungwoo Kang, Professor Department of Transportation Engineering, Hanyang University 1271 Sa 1 Dong, Ansan, Kyunggido, Korea, 425-791 Tel : +82-31-400-5153 Fax : +82-31-406-6290 E-mail : [email protected] Mr. Jungho Yun, Researcher Department of Transportation Engineering, Hanyang University 1271 Sa 1 Dong, Ansan, Kyunggido, Korea, 425-791 Tel : +82-31-407-3540 Fax : +82-31-406-6290 E-mail : [email protected] 1 ABSTRACT This study evaluated the change of driver's psychophysiological load of occipital lobe at freeway merge area and compared with basic driving section. Ten persons of 8 men and 2 women were investigated at 3 basic sections and 3 merging areas on the Youngdong freeway. It is found that driver's load in acceleration lane before merging is 2.21 times higher than the basic driving section. Further, driver's load for merging was maintained for 4 seconds after merging. Particularly, driver's highest loading point in merging behavior was found to be from gore area to 80m. INTRODUCTION In seeking ways to prevent traffic accidents, it is important to pay particular attention to the causes associated with drivers, in addition to such preventive considerations as improving roadway facilities and vehicle performance. The objective of this study is to identify changes of drivers' work load by quantitative measurement of psychophysiological load as they drive in freeway merge area. Freeway merge area is specifically chosen for the study because in that area drivers encounter complicated traffic environments as they have to enter freeway through ramp, accelerate, and merge into freeway in poor sight distance while high degree of alert and concentration is required on their merging performance. REVIEW OF RELATED STUDIES Psychophysiological Load Measurement Workload measurement is widely used to identify factors in a quantitative form that give psychophysiological influence to drivers. With the development of measuring technology, researchers have been able to begin measuring and analyzing drivers' work load using physiological signals generated from drivers. Physiological measurement may be conducted for electroencephalogram, electromyogram, and electrocardiography to identify pupil size, changes in heartbeat, brain activity, etc. Physiological measurement should be conducted in a way and to the extent that drivers are not interrupted. It offers advantage of giving continuous output. Electroencephalogram Electroencephalogram is tiny rhythmical electrical activity of brain cell, derived using an electrode attached to head skin, amplified using electroencephalograph, shown on a vertical axis with time on a horizontal axis. It is a live signal form brain function that changes all the time. Electroencephalogram found from human brain is classified into four types in general: α, β, δ and θ, depending on their amplitude and frequency. Type β is detected when a person is in physical or mental activities, with 2∼ 20 ㎶ amplitude and 12∼ 40 Hz frequency. It was discovered that occipital lobe in human brain governs drivers' 2 visual function. Considerable reaction is detected when they drive on a curve or when they change the lane. Frontal lobe is also known to control some of the visual function. In recognition of the fact that physiological signal reflects the change of the driver situations, many researches have been conducted to identify the level of vigilance with detection of drivers' electroencephalogram. Some researchers(1) reported that drivers' performance varies depending on the vigilance level and conducted experiments to find ways to improve drivers' vigilance level. There was another research(2) related to electroencephalogram measurement reporting that the complication degree of external excitement has effect on the activation of electroencephalogram, which consequently influence human perception. They reported that, as the complication degree of excitement becomes higher, the activation of electroencephalogram related to tension relief is blocked, and the activation of electroencephalogram related to improvement of vigilance level increased. This finding may well be explained in connection with drivers' overload. When a driver is overloaded, his or her brain system demands alert and concentration required to process information received from drivers' traffic environment, consequently increasing the vigilance level. Helander(3) conducted a survey in 1975 to identify the correlation of drivers’ vehicle operation and physiological response. They measured physiological response including skin electricity response, electrocardiogram and electromyogram. The study showed that the driver responded most at the intersections with bridge, at the road bump, and at the merge area. Brookhuis and others(4,5) conducted a research in 1993 for the changes in physiological signal to identify the drivers’ conditions on freeways. For this research, 20 people drove on freeway under vigilance condition for 150 minutes to identify the effect on their manipulation of steering wheel and their driving performance to follow vehicle ahead going at varying speeds. Based on the measurement of energy factors (Theta+Alpha)/Beta), the standard deviation of handle movement increased, whereas the number of wheel returning decreased. The energy factor of the driver following vehicle ahead going at varying speed decreased with time. DATA COLLECTION AND REDUCTION Survey Area and Subjects After visiting feasible sites to determine survey locations on the Youngdong Expressway, three basic tangent sections without merging situation with 200m distance and three merging areas covering ramp, acceleration lane, and a section beyond the merging point to certain distance ahead were selected. The design speed of expressway and ramp was 80 and 50km/h respectively with grades between 1.4% and 1.0% The basic section and merging section are alternately located and the two sections were separated by the sufficient distance that is not affected by each other. The survey locations were determined to satisfy the conditions that drivers' vigilance level should not be influenced by large advertisement structure and construction zones except roadway geometry. Ten driving testees were selected with normal vision capacity, one year or more of driving experience, and clean record. 3 Survey Vehicles The survey vehicle was equipped with measuring devices to collect physiological signals and external environmental data, which enable quantification of changing testee’s physical and emotional data. Figure 1 shows the survey vehicle. CCTV Camera Notebook Physiological Signal Measurement Apparatus CCTV Camera Velocity/Distance Measurement Inertia force Measurement Tachometer Velocity/Distance Measurement FIGURE 1 Survey Vehicle Diagram The physiological signals that the measuring device could collect included electroencephalography (EEG), skin conductance response (SCR), electrocardiography (ECG), electromyogram (EMG), and electrooculomotorgraphy (EOG). The measuring device could also collect environmental data, including vehicle speed, drivers' behavioral changes, movement of adjacent vehicles, and their approaching speed and distance. The measuring equipment included interface module, simulator module, and amplifier with 16 channels that amplifies detected body signals. Data Collection The study aims to measure drivers’ electroencephalography signal to analyze the drivers’ vigilance level. The changes of the β signal from the occipital lobe is collected together with following data. Testee's personal records (age, driving records, gender, eyesight) EEG signal from occipital lobe at merging section Roadway geometry characteristics at merging section Distance from gore to the point of the vehicle merging Traffic volume on freeway at the time of vehicle merging 4 When testees arrived at the survey site, their personal records were identified. Before electrodes were attached to testee's body, the pertinent areas of skin were cleaned using oil removal skin cream. Appropriate electrodes and lead cables necessary to measure signals from body were installed at the predetermined locations on the body. The measuring system was checked to ensure that body signals are detected from each respective channel. For the stablization of subjects, they were allowed to drive for 10 to 20 minutes to relieve the uncomfortableness by the attached electrodes. When the testees drove on the merging area, the signals from their body were measured and recorded. We took the same measurement on the basic sections for comparison with that of merging area. The merging area used for the study was from the beginning of the ramp to the point where the driver has proceeded for 10 seconds after he or she completed merging into the freeway traffic flow. To analyze the relation between merging distance and the electroencephalography change in the acceleration lane, The distance in the acceleration lane is marked by 40m increment. The 40m span was chosen since the measuring equipment was unable to divide signal in less than one second increment. Video recorder installed in the survey vehicle recorded the front view of the vehicle along with the time so that analyzers could later trace the data of the desired time to conduct the body signal data division using appropriate analysis program. The body signal was converted to frequency band to collect the β signal of the occipital lobe. DATA ANALYSIS In consideration of the fact that drivers are most influenced by vision rather than the sense of hearing or smell, the study analyzed the electroencephalography data of occipital lobe where vision is controlled. The collected data were analyzed by fast Fourie transform to obtain relative power spectrum for each frequency band of electroencephalography, i.e., δ(0.5∼ 4 Hz), θ(4∼ 8Hz), α (8∼ 12 Hz) and β (12∼ 30 Hz). Finally, we used β data for this study. We obtained the value of β change in three different sections: the ramp section, the gore to the point of merging, and the section after merging. For the basic driving section, we collected the relative spectrum of the β value as well. In Figure 2, where the survey vehicle enters the ramp and drives up to the beginning point of the acceleration lane is section 1. The second section is where the vehicle passes gore, accelerating and merging to freeway. The third section is started when the merging vehicle had moved fully in the freeway through lane and continues for 10 seconds. The purpose of adding the third section in the survey was to find out how long the drivers' physiological change from previous section would last. 5 Median Barrier Section2 Section 3 Section 1 Merging Starting Point of Point Acceleration Lane 4 Seconds after merging FIGURE 2 Section Division of Merging Area Traffic volume data from the point the vehicle enters the acceleration lane to the point the vehicle was merged and proceeded in the freeway traffic was also collected on the outside lane of the freeway at the same time of collecting brain signals,. The traffic volume collected did not take account of the vehicle type, and was converted to the traffic volume per hour. The distance needed for the vehicle to merge was measured using the video recordings and the distance marking. The video recording was also utilized for the signal division. The β signal was calculated by the 40m distance interval. The β signal increase was then calculated using the following formula. β Signal Increase= β Signal from Merging Area β Signal from Basic Roadway (1) Effect of Section Division in Merging Area The β signal increase was obtained by averaging 3 sectional data collected by 10 testees, which means 3 different data were available for each section. In this process, the data that could not be analyzed due to its generation in less than one second, as described previously, was eliminated to give consistency in the data presentation. Figure 3 shows the β signal increase in the merging area divided into 3 sections as described. Comparing with the β signal of the basic driving section, the first ramp section showed 1.09 times greater β signal, 2.21 times greater β signal for the second section (accelerating and merging), and 1.22 times greater β signal for the third section (for 4 seconds after merging). Apparently, it was found that drivers' vigilance level considerably rose as they started accelerating and then merged to freeway. ANOVA analysis was conducted on these results. Table 1 indicates the difference between survey sections based on 5% significance level. To identify more accurate difference, Duncan's test was conducted with the result shown in Table 1. This table may be interpreted to show major difference between first and second sections, and between the second and third sections, indicating that the second section is the highest vigilance level section. As mentioned, the drivers showed highest vigilance level (2.21 times greater than on basic section) in the second section as they accelerated and merged into the freeway. It seems that this happened because this was the section where the two traffic flows from ramp and the freeway met, and the driver was demanded a great deal of visual 6 information to search the gap for merging. The β signal increase ratio of 1.22 found after merging indicates that the drivers vigilance level experienced in the second section would remain for some period of time even after merging was completed. 3 β Signal Increase 2.5 2. 21 2 1.5 1. 22 1. 09 1 0.5 0 1 2 3 Section FIGURE 3 β Value of Occipital Lobe at Merging Area TABLE 1 source model error total ANOVA and Duncan’s Test By Three Merging Sections sum of mean df F value Pr>F squares square 2 3.24012659 1.62006329 11.35 0.0003** 27 3.85408571 0.14274392 29 7.09421229 Duncan Grouping A B A Mean 1.09 2.21 1.22 n 10 10 10 Section 1 2 3 Acceleration Lane Analysis by Distance It was found that drivers’ vigilance level was at its highest from the beginning of acceleration to the time just before merging was made. In order to further identify the β signal increase in acceleration section, we divided the 240m acceleration section in 40m increment, which was the minimum distance that allowed the signal analysis. We collected 3 merging area data from each testee, totaling 180 data altogether. Then, after excluding those data collected from the subsections with distance corresponding to the time less than one second, a total of 132 data were collected for the analysis. As shown in Figure 4 and Table 2, the β signal increase found in this section ranged 1.24 to 2.88. It is noted that relatively lower rate (1.24) was found in the section between the 160m and 200m distance. This is believed to have happened due to an unidentifiable factor encountered. Duncan's test was conducted to further find out difference between each 40m section, 7 and confirmed that 160m-to-200m section was the only section that indicated the difference. Most of the testees showed continued increase starting gore to the 40m section and then to the 80m. 6 β Signal Increase 5 4 2.88 3 2.88 2.58 2.39 2.31 2 1.24 1 0 0- 40m 40- 80m 80- 120m 120- 160m 160- 200m 200- 240m Merging Distance FIGURE 4 β Signal Increase by 40m Distance Interval Before Merging TABLE 2 ANOVA and Duncan's Test for Distance Interval before Merging sum of squares mean square source df model 5 40.4134956 8.08269912 error 126 354.841478 2.81620221 total 131 395.254973 F value Pr>F 2.87 0.0173** Duncan Grouping A Mean 2.88 n 22 Gore(0m)∼ 40m A 2.88 22 40m∼ 80m A 2.31 22 80m∼ 120m A 2.39 22 120m∼ 160m A 2.58 22 200m∼ 240m B 1.24 22 160m∼ 200m 8 Section Acceleration Lane Analysis for Accumulated Distance In addition to the analysis for each separate distance sections, similar analysis was conducted for accumulated distance sections for varying total merging distances. Through this analysis, we could obtain the vigilance level for each merging distance. It was found as shown in Figure 5 and Table 3 that the drivers showed the β signal increase of 2.88 times when they merged within 0 to 40m and 0 to 80m distance as well, whereas 2.01 and 1.96 ratio were found when they merged within 0 to 200 m and 0 to 240m distance respectively. β Signal Increase 6 5 4 3 2. 88 2. 88 2. 63 2. 35 2. 01 2 1. 96 1 0 0- 40m 0- 80m 0- 120m 0- 160m 0- 200m 0- 240m Cum ulated Merging Distance (m ) FIGURE 5 β Signal Increase by Cumulated Distance Before Merging ANOVA analysis shown in Table 3 was made to check any difference between accumulated sections. Since differences were confirmed, we conducted a Duncan's test and found that there were difference between the ratios found in the sections of 0 to 40m and 0 to 80m and the ratios found in the sections of 0 to 200m and 0 to 240m. 9 TABLE 3 ANOVA and Duncan's Test for Cumulated Distance Before Merging sum of squares mean square source df model 5 18.6692011 3.73384022 error 126 152.832884 1.21295940 total 131 171.502085 Duncan Grouping F value Pr>F 3.08 0.0118** Mean n Section A 2.88 22 0m∼ 80m A 2.88 22 0m∼ 40m A B 2.63 22 0m∼ 120m A B 2.35 22 0m∼ 160m B 2.01 22 0m∼ 200m B 1.96 22 0m∼ 240m Since the highest vigilance level is found from the gore to 80m on the acceleration lane, a special marking treatment such as shown in Figure 6 is recommended for implementation. 80m 80m FIGURE 6 Suggested Special Marking Treatment on Acceleration Lane 10 Vigilance Increase Analysis on Acceleration Lane β Signal Increase This part of the study is intended to find a model for the vigilance level in relation to associated factors. Since we could believe through this study that the increase rate of β signal from drivers while they are on the acceleration lane, was a function of the merging distance and the traffic volume on freeway outside lane as shown on Figure 7 and 8, we looked for a model representing their relations. 4. 5 4 3. 5 3 2. 5 2 1. 5 1 0. 5 0 0 50 100 150 200 250 300 Merging Distance (m) β Signal Increase FIGURE 7 β Signal Variation by Merging Distance 4.5 4 3.5 3 2.5 2 1.5 1 0.5 0 0 500 1000 1500 2000 Freeway Outside Lane Volume (vph) 2500 FIGURE 8 β Signal Variation by Freeway Outside Lane Volume From each testee, 3 outputs were collected totaling 30 outputs. We excluded those data which could not be analyzed due to its corresponding time less than one second. Also excluded were the data we judged to be outlier. A total of 21 outputs therefore were used in finding the relation. Through a regression analysis, an equation was derived. ∆ β = 0.04565759D – 0.00447506V + 0.00000261V2 – 0.0001258D2 (R2=0.92) (2) Where, ∆ β = β signal increase compared to basic section V = freeway outside lane volume (vph) D = merging distance (m) 11 According to this equation and Figure 9, drivers' visual vigilance level increases with merging distance to certain point and then decreases, whereas it continues to increase with increasing traffic volume on the freeway outside lane. FIGURE 9 β Signal Increase by Merging Distance and Mainline Volume Analysis after Merging An analysis was conducted to further identify the subsequent influence after merging. We analyzed β signals in one second increment starting first signal in 2 seconds after merging. The signal data were collected and analyzed for each time increment after merging, taking 3 merging area data from each testee, totaling 120 data altogether for 4 time duration cases with 10 testees. As shown in Figure 10, β signal rate of 1.53 was found in two seconds after merging, which was then decreased to 1.49 in three seconds, down to 1.22 in four seconds, and to 0.62 in 5 seconds. It is found that the drivers' vigilance level after merging remained for the first four seconds considerably. As shown in Table 4 of ANOVA and Duncan's test, there is a considerable difference of the rate in the first 4 seconds from 5 seconds. 12 3 β Signal Increase 2. 5 2 1. 53 1. 5 1. 49 1. 22 1 0. 62 0. 5 0 2 Sec 3 Sec 4 Sec 5 Sec Time After Merging FIGURE 10 β Signal Increase by Time After Merging TABLE 4 source model error total ANOVA and Duncan's Test for Time Duration after Merging df sum of mean F value Pr>F squares square 3 16.0065898 5.33552995 5.82 0.0010** 116 106.419545 0.91740987 119 122.426135 Duncan Grouping A A A B Mean n 1.53 1.49 1.22 0.62 30 30 30 30 Time After Merging 2 3 4 5 CONCLUSION The study identified the changes in drivers' vigilance level at the freeway merge area in comparison with normal basic section, by quantifying associated β signal outputs collected in the survey. The results of the study may be summarized as follows. When the drivers drove on ramp leading to a freeway, accelerated and merge into the freeway, the drivers' vigilance level increased to a range of 1.09 to 2.21 times greater than that found with the same drivers driving on a basic roadway. Highest vigilance level increase as much as 2.21 times was found from where the drivers started accelerating to where they had completed merging to freeway. Drivers who have completed merging to freeway remained under the influence of the vigilance they had during their merging, for the duration of 4 seconds further. Drivers who merged within 80m acceleration distance showed much higher vigilance level than those who made the merging within 240m. In addition, Drivers visual vigilance level 13 increased with merging distance to certain point and then decreased, whereas the level continued to increase with increasing freeway outside lane volume. ACKNOWLEDGMENT This paper is supported by the fund from Brain Korea(BK) 21 Project administered by The Korea Research Foundation under the Ministry of Education, Republic of Korea. REFERENCE 1. Wilson, J. R. & Rutherford, A., "Mental Models : Theory and Application in Human Factors", Human Factors, 31(6) , 1989 2. Wierwille, W. W., J. C. Hicks, T. G. and Muto, "Secondary Task Measurement of Workload as a Function of Simulated Vehicle Dynamics Driving Conditions", Human Factors, 35(2),1977 3. Helander, M.G., "Physiological Reactions of Drivers as Indicators of Road Traffic Demand", TRB 530, 1975 4. Brookhuis, K. A., Waard, D. and Mulder, L. J. M., "Measuring Driving Performance by Car-following in Traffic", Ergonomics, 1993 5. Wierwille, W. W. & Eggemeier, F. T., "Recommendations for Mental Workload Measurement in a Test and Evaluation Environment", Human Factors, 35(2), 1993 14
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