Age and Gender Differences in Overtaking Maneuvers on Two-Lane ...

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Wasielewski (16) and Evans and. Wasielewski (17) measured drivers' propensity to take risks as indicated by their chosen driving speed and following headway ...
Age and Gender Differences in Overtaking Maneuvers on Two-Lane Rural Highways Haneen Farah own vehicle) or the speed of the oncoming vehicle, and insufficient clear sight distance. Each subtype of overtaking accident has its own associated causes and group of drivers. For example, a “return and lose control” accident is associated particularly with young drivers. However, Clarke et al. did not examine gender differences. A number of studies have found significant differences in the driving and behavioral characteristics of young and old drivers and of male and female drivers. For example, significant differences were found in drivers’ driving speeds (6), following gaps (7 ), and gap acceptance and overtaking frequencies (8–10). These differences also exist in accident involvement and traffic rule violations (11–15). Previous studies on driving and behavioral differences as a function of drivers’ age and gender show significant results, which strengthen the need for the current research. Wasielewski (16) and Evans and Wasielewski (17 ) measured drivers’ propensity to take risks as indicated by their chosen driving speed and following headway (gap between a vehicle and its leader) in a series of observational studies in which oncoming cars were photographed from freeway overpasses. The license plate number, read from the photograph, was used to extract from state files the driving record, sex, and age of the registered owner. The authors found a systematic decline in average speed and in the reciprocal headway with increasing age, which indicates a lower level of risk taking. However, the observational data were insufficient to permit plotting separate relations for males and females. Moreover, the method of data collection, which is based on using license plate numbers to identify drivers, is relatively unreliable. Yagil explored gender- and age-related differences in the propensity to commit traffic violations in Israel (18). A survey administered to 181 university students indicated females have a stronger sense of obligation to obey traffic laws and are more likely to evaluate traffic laws positively. The survey findings showed that the gender differences are even more pronounced among young drivers. Waylen and McKenna indicate that driver speed choice is generally accepted as a measurement of willingness to take risks and as a reliable predictor of accident involvement (6). The authors conducted an observational study where only speeds of vehicles that were unconstrained by other traffic were taken. Speed observations were collected for 850 drivers, 429 of whom were judged to be within the 17- to 25-year age group and 421 between 30 and 55 years of age. Main effects show that male drivers drive faster than female drivers and those drivers in the younger age group drive at faster speeds than those drivers in the older group. Elvik also indicates that speed has a major impact on the number of accidents and the severity of injuries and that the relationship between speed and road safety is causal, not just statistical (19). Farah et al. developed an overtaking gap acceptance model for two-lane rural highways based on data collected in a driving simulator experiment (9). The authors found that younger drivers and

For decades researchers have been pointing out significant differences in the driving behavior between young and old and between male and female drivers. There are many studies concerning age and gender differences in risk perception, traffic accident involvement, traffic violations, alcohol consumption, and risky driving. However, little effort has been focused on studying the behavioral differences in overtaking maneuvers on two-lane highways. A considerable percentage of the fatal accidents on two-lane highways is directly related to overtaking maneuvers. Therefore, the main focus of this study is to understand better the overtaking behavior of different drivers classified by their age and gender. Data on the overtaking behavior of 100 drivers were collected with an interactive driving simulator. Several scenarios of two-lane rural highways with different geometric and traffic conditions were developed. The results show interesting and significant differences in the overtaking behavior of drivers depending on their age and gender. These differences are mainly in the frequency of overtaking maneuvers, overtaking time duration, following distances, critical overtaking gaps, and desired driving speeds. Geometric and traffic conditions were also found to have a significant impact on drivers’ overtaking behavior. The findings of this study contribute to the understanding of the overtaking behavior of different groups of drivers and thus have implications for road safety intervention programs and the development of effective risk reduction strategies adapted and targeted for different age and gender groups.

Two-lane highways make up a substantial proportion of the road network in most countries around the world. About 60% of all fatal crashes in member countries of the Organisation for Economic Co-operation and Development occur on these roads (1). One of the most severe road accidents is connected to the overtaking maneuvers, to which about 35% to 50% of deaths on roads are directly related (2). Overtaking is among the most significant driving behaviors on two-lane highways. Overtaking affects highway capacity, safety, and level of service. Overtaking is also a mentally complicated task (3, 4) that substantially affects the highway performance. Clarke et al. sampled 973 police road accident files describing overtaking accidents from England, for the years 1989 to 1993 (5). The authors indicate that overtaking is a complex maneuver that can fail in a number of different ways, such as faulty judgment of the distance required to complete the overtaking maneuver, misjudgment of the speed of the leader (or possibly the acceleration of the driver’s Department of Transport Sciences, KTH–Royal Institute of Technology, Teknikringen 72, SE-100 44 Stockholm, Sweden. [email protected]. Transportation Research Record: Journal of the Transportation Research Board, No. 2248, Transportation Research Board of the National Academies, Washington, D.C., 2011, pp. 30–36. DOI: 10.3141/2248-04

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male drivers have lower critical overtaking gaps than older drivers and female drivers, respectively. As can be seen from the literature, little effort was focused on the behavioral differences in overtaking maneuvers on two-lane highways between the different gender and age groups. Therefore, the main focus of this study is to investigate if any differences exist in the overtaking behavior between different groups of drivers categorized by their gender and age. Moreover, this study breaks down the overtaking maneuver into submaneuvers and compares how different drivers, by age and gender, perform these submaneuvers and points out factors that would increase the risk of accidents (e.g., gap acceptance, following distances, driving speeds).

EXPERIMENTAL DESIGN AND DATA COLLECTION A laboratory experiment using a driving simulator was developed to collect data on drivers’ overtaking behavior. The simulator used in this experiment, STISIM (20), is a fixed-base interactive driving simulator, which has a 60° horizontal and 40° vertical display. Conducting this study in a driving simulator will allow for controlling the rate of exposure and environmental variables which were the cause for much of the discrepancies between different studies that aimed to estimate the effect of age and gender on accident involvement. Several previous studies have used driving simulators for studying overtaking behavior (9, 21–23). For example, Jenkins and Rilett used simulator data to develop a classification of overtaking maneuvers (22). Bar-Gera and Shinar evaluated the impact of the speed difference between the lead and subject vehicles on drivers’ desire to overtake in a simulated environment (23). They found that in half of the cases subjects passed lead vehicles that were faster than their own average speed. Farah et al. developed an overtaking gap acceptance model that takes into account the impact of the road geometry, traffic conditions, and driver characteristics (9). Lee conducted a study in order to validate the STISIM driving simulator in measuring on-road driving performance of older drivers (24). The simulated driving performance index was found to explain over two-thirds of the variability of the on-road driving performance index, after adjustment for age and gender of the drivers. The situations that participants encountered in this study were defined by the vehicles shown in Figure 1. The subject vehicle is following a front vehicle. In making the decision on whether to overtake the front vehicle, the subject vehicle needs to consider the available overtaking gaps and compare them to his or her critical overtaking gap. The available overtaking gaps are defined as the

FIGURE 1

time gap between the opposing vehicle and the vehicle in front of it, at the time that the subject vehicle encounters the lead vehicle. The critical overtaking gap is defined as the minimum acceptable gap. The following gap at the beginning of overtaking is defined as the time gap between the subject vehicle and the front vehicle, at the time that the subject vehicle’s left front wheel touches the center line. To capture the impact of various geometric design and traffic factors on the overtaking behavior, a number of different simulator scenarios were designed. The experiment design included four different factors: geometric design (A), traffic volume on the opposing lane (B), front vehicle speed (C), and opposing vehicle speed (D) (Table 1). These factors were chosen on the basis of previous studies that showed the factors’ impact on overtaking decisions (3, 22, 23, 25). Two levels were used for each factor (high and low). A full factorial design with these factors, which produces 16 (24) scenarios was used. Following Farah et al. (9, 26), it was decided that participants complete four scenarios out of the 16, which takes about 40 min, to allow the experiment to be completed within 1 h. The partial confounding method [e.g., Hicks and Turner (27)] was used to allocate the block of scenarios that each participant will complete. This method is designed for experiments in which the number of scenarios that can be run in a block is less than the total number of factor combinations, and so some effects have to be confounded. In the current experiment, third-level interactions (ABC, ABD, ACD, and BCD) were confounded. The order of scenarios presented to the participants was also changed to eliminate any effects of scenario ordering on the results. All scenarios in the experiment included a 7.5-km, two-lane highway section with no intersections, on a level terrain and with daytime and good weather conditions, which allowed good visibility. Drivers were instructed to drive as they would normally do in the real world and were given a 5- to 10-min familiarization scenario. The scenarios did not include any situations that would necessitate the driver to drive on the opposing lane. In other words, overtaking maneuvers are assumed to be dictated by “willingness to overtake” and not “necessity to overtake.” One hundred Israeli drivers (69 males, 31 females) who had a driving license for at least 5 years and drove on a regular basis participated in the experiment. The age of the participants ranged between 21 and 61 years, with a mean of 32.7 years and standard deviation of 9.8 years. Drivers were distributed into four groups according to their gender and age (under or above 30 years old). Eighteen female and 36 male drivers were under 30 years old (younger group) and 13 female and 33 male drivers were above 30 years old (older group). The reason for this categorization is to have a sufficient number of

Definition of overtaking gap acceptance situation.

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TABLE 1

Factors Included in Experimental Design Level

Factor

High (+)

Geometric design (A)

Lane width: 3.75 m, shoulder width: 2.25 m Curve radius: 1,500–2,500 m Curve radius: 300–400 m Drawn from truncated negative exponential distributions Mean: 10.3 s Mean: 18.0 s Min.: 5.0 s, max.: 25.0 s Min.: 9.0 s, max.: 31.0 s Drawn from uniform distributions 67% between 80 and 120 km/h 33% between 80 and 120 km/h 33% between 40 and 80 km/h 67% between 40 and 80 km/h Drawn from uniform distributions 67% between 80 and 120 km/h 33% between 80 and 120 km/h 33% between 40 and 80 km/h 67% between 40 and 80 km/h

Overtaking gaps in the opposing lane (B)

Speed of front vehicle (C)

Speed of opposing vehicle (D)

Low (−)

NOTE: Min. = minimum; max. = maximum.

female drivers in each category since the total number of female drivers participating in this study was only 31. Categorization to three or more age groups would result in an insufficient number of participants in each category, which could harm the statistical validity of the results. The average age of drivers and standard deviation (in parentheses) in each group are as follows: younger male drivers, 25.6 (±2.6) years old; younger female drivers, 27.3 (±1.3); older male drivers, 40.9 (±9.7); and older female drivers, 39.5 (±9.0). The simulator collected detailed data of the speed and position of vehicles at a resolution of 0.1 s. The resulting data set included a total of 14,654 overtaking gap observations. In 696 (4.7%) of these gaps, drivers completed overtaking maneuvers.

RESULTS OF AGE AND GENDER DIFFERENCES IN OVERTAKING MANEUVERS This section presents the results from analyzing the overtaking behavior of different drivers categorized by their age and gender. Several measures were taken into account in this comparison, including the average overtaking rates, overtaking time duration, following gap from the front vehicle, critical overtaking gaps, delay before overtaking, desired driving speed, and remaining gaps at the end of an overtaking maneuver from the overtaken and opposing vehicles.

Average Overtaking Rates A comparison of the average overtaking rates between the different driver groups in each scenario was conducted. The overtaking rate in a specific scenario for each group was calculated as the total number of overtaking maneuvers completed by drivers in that group divided by the total number of drivers in that group who drove that scenario. To test the significance of these differences, a 2 × 2 analysis of variance (ANOVA) was conducted. This analysis tool is useful when data can be classified along two different dimensions. The results of the two-way ANOVA show that there is a significant difference in the average overtaking rates between male and female drivers (p = .005). Male drivers on average overtake more than female drivers (overtaking rate is 2.55 for male versus 1.93 for female). No significant difference was found between the average overtaking

rates of the younger group and the older group of drivers ( p = .136). The interaction between gender and age has also no significant impact (p = .659). Another interesting comparison was of the percentages of drivers from each group who did not overtake and continued to follow the vehicle in front. These percentages were calculated for each scenario separately. The percentages were also normalized to the number of drivers in each group. The results show that 53% of the older female drivers prefer to keep following the front vehicle compared with 38% of the older male group. In the younger drivers’ groups, these percentages were lower, about 28% for female and 19% for male. The results of the two-way ANOVA analysis show significant differences between younger and older drivers (p = .0031) and between male and female drivers (p = .0404). However, the interaction is not significant at the 95% confidence level (p = .6713).

Overtaking Time Duration The ANOVA results showed that drivers’ gender, age, and the interaction between them have a significant impact on the average overtaking time duration (p = .0053, .0008, and .028, respectively). According to the results in Table 2, female drivers on average need more time to finish the overtaking maneuver than male drivers, and older drivers need more time than younger drivers. Values in parentheses represent the variances. Leung and Starmer found similar results: female drivers spent more time in the opposing lane than male drivers when overtaking (28).

TABLE 2 Average and Variance of Overtaking Time Duration by Age Group and Gender Overtaking Time Duration (s) Group Younger Older Total

Male (avg./var.)

Female (avg./var.)

Total (avg./var.)

6.933 (0.527) 7.315 (1.564) 7.124 (1.049)

7.125 (0.675) 8.864 (3.037) 7.995 (2.576)

7.029 (0.769) 8.089 (1.687) 7.559 (1.976)

NOTE: Avg. = average; var. = variance. Younger and older groups are defined in text.

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Delay Before Overtaking

Following Time Gap (sec)

1.50

The delay from the moment the subject vehicle was interrupted by the front vehicle until the starting moment of overtaking was calculated in this study. The average delay for each group of drivers in each scenario was calculated. The results showed that female older drivers experience the longest delay when compared with the other groups of drivers (on average 3.22 min), followed by the male older group (2.06 min) then the female younger group (1.73 min), and finally the male younger group (1.44 min). With the use of a two-way ANOVA analysis, it was found that gender and age have a statistically significant (p = .005 and 7.8E-05, respectively) impact on the average delay before overtaking; however, the interaction was not found to be statistically significant at the 95% confidence level (p = .083).

1.25

1.00

0.75

0.50

0.25 Female Female Male Male Older Group Younger Group Older Group Younger Group Age & Gender Groups FIGURE 2

Following time gap when beginning to overtake.

Following Time Gap from Front Vehicle When Beginning to Overtake Previous research studies found that one measure of drivers’ aggressiveness is expressed by close following from the front vehicle. This behavior was found to typically characterize young male drivers (28, 29). The results in Figure 2 show that younger male drivers keep shorter following time gaps than the other groups. Age and gender of drivers were found to have a significant impact on following time gaps ( p = .029 and .049, respectively). Younger male drivers keep shorter following time gaps from the front vehicle compared with older female drivers when beginning to overtake. The interaction between gender and age of drivers was not found to be significant ( p = .351). The results found in previous studies by Leung and Starmer (28) and Evans and Wasielewski (17 ) support these findings.

Critical Overtaking Gaps The critical overtaking gap of each driver in each scenario was calculated based on his or her accepted and rejected overtaking gaps using the maximum likelihood method. This method was proved to be the most accurate and reliable (30, 31). The maximum likelihood method of estimating critical gap considers that a driver’s critical gap is between the range of his or her largest rejected gap and his or her smallest accepted gap. A probabilistic distribution for the critical gaps must be assumed (32). A lognormal distribution was assumed in this study. The average critical time gap and variance (in parentheses) for each group is as follows: younger male group, 23.52 (2.87) s; younger female group, 28.86 (7.44) s; older male group, 27.28 (8.38) s; and older female group, 30.85 (12.98) s. According to the two-way ANOVA analysis, drivers’ age and gender significantly affect critical overtaking gaps ( p = .0001 and 3.35 × 10−8, respectively). The interaction was not found to be significant (p = .216). Critical overtaking gaps of male drivers are significantly lower than those of female drivers, and those of younger drivers are lower than those of older drivers.

Desired Driving Speed From the collected data by the driving simulator, the desired driving speed for each driver in each scenario was calculated as the mean speed of the vehicle in the sections at which it was not close to the vehicle in front. It was found that the average desired driving speed of younger male drivers is 92.2 (±3.13) km/h, higher than the average desired driving speed of younger female drivers, 87.0 (±4.91) km/h. Older drivers had lower average desired speeds. Average desired speed of older male drivers is 81.4 (±4.36) km/h and that of older female drivers is 82.9 (±5.03) km/h. Values in parentheses represent the standard deviations. Based on the two-way ANOVA analysis results, the impact of drivers’ age on desired speeds is significant ( p = .0009). However, the gender and the interaction were not found to be significant (p = .382 and .127, respectively). Similar results were found in the Waylen and McKenna study regarding the relationship between driving speeds and gender and age of drivers (6).

Gap from Opposing and Front Vehicles at End of Overtaking Maneuvers Several previous studies indicated that the time to collision (TTC) is considered one of the most widely used safety indicators and a measure of a crash risk (33, 34). The TTC is defined as the time required for two vehicles to collide if they continue at their present speed and on the same path (35). According to Svensson, TTC is an indicator for a traffic conflict and is, thus, inversely related to accident risk (36). In this study no significant differences with respect to the remaining gap from opposing vehicles were found between the different groups of drivers. According to the Israeli design applications, the recommended value for the minimum time to collision is 3 s. About 60% of these TTC values were less than 3 s, 40% less than 2 s, and about 20% were less than 1 s, which is associated with a high risk for a crash. However, a previous study by Farah et al. showed that TTC values in a virtual environment are approximately half of that in the field (26). The remaining gaps from the front vehicle at the end of the overtaking maneuvers were calculated. Figure 3 presents the results for the different drivers’ groups. As shown in Figure 3 and according to the results of the two-way ANOVA analysis, drivers’ age has a significant impact ( p = .036) on the remaining gap from the overtaken (front) vehicle at the end of overtaking maneuvers. However, the impact of drivers’ gender and the interaction are not statistically significant (p = .864 and .112,

Gap from Front Vehicle at End of Overtaking (sec)

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TABLE 3 Significant F-Values for Impact of Main Factors and Their Interactions on Different Overtaking Related Variables

7.0 6.0

Factor 5.0 4.0 3.0 2.0 1.0 Female Female Male Male Older Group Younger Group Older Group Younger Group Gender & Age Groups

FIGURE 3

Gap from front vehicle at end of overtaking maneuver.

respectively). Older drivers were found to have larger gaps from the overtaken vehicle at the end of the overtaking maneuver than younger drivers. The results in Figure 3 of the older drivers are much more dispersed than those of younger drivers. This outcome might be due to the distribution of the age of drivers in the studied groups. The older drivers’ age groups have larger standard deviation compared with the younger groups.

IMPACT OF ENVIRONMENTAL CHARACTERISTICS ON OVERTAKING BEHAVIOR The purpose of this section is to analyze the impact of the main factors [geometric design (A), traffic volume on the opposing lane (B), front vehicle speed (C), and opposing vehicle speed (D)] presented in Table 1 and their interactions on the overtaking behavior of drivers. This is done by using Type III sums of squares obtained with the general linear models procedure. Type III sums of squares tests the significance of the effect of one independent variable on each dependent variable given that all of the other independent variables are included in the model [for more details, see Hill and Lewicki (37)]. Table 3 summarizes the significant F-values for the impact of the main factors and their interactions on different measures of overtaking maneuvers (dependent variables). The values shown in Table 3 are the Pr > F of Type III sums of squares. Only significant F-values less than 0.1 are shown. According to the results illustrated in Table 3, it can be seen that the different overtaking measures are affected by some of the main factors and their interactions. For example, it was found that critical overtaking gaps are significantly affected by the traffic volume (B), the interaction between the traffic volume and the speed of the front vehicle (BC), the interaction between the geometric design and the speed of the opposing vehicle (AD), and the interaction between the traffic volume and the speed of the opposing vehicle (BD). According to further analysis of the direction of impact of the different factors, it was found, as expected, that on well-designed roads drivers conduct more overtaking maneuvers than on poorly

Model A B Interaction (A  B) C Interaction (A  C) Interaction (B  C) D Interaction (A  D) Interaction (B  D) Interaction (C  D)

No. of Overtaking Maneuvers

Critical Overtaking Gap (s)

Desired Speed (m/s)

Average Following Gap (m)