Paper on Hazardous Behavior

Research Article Implementing Surrogate Safety Measures in Driving Simulator and Evaluating the Safety Effects of Simulator-Based Training on Risky Driving Behaviors

Eunhan Ka,1 Do-Gyeong Kim,2 Jooneui Hong,3 and Chungwon Lee 4

1Institute of Engineering Research, Seoul National University, Seoul 08826, Republic of Korea 2Department of Transportation Engineering, University of Seoul, Seoul 02504, Republic of Korea 3Korea Development Institute, Sejong 30149, Republic of Korea 4Department of Civil and Environmental Engineering, Seoul National University, Seoul 08826, Republic of Korea

Correspondence should be addressed to Chungwon Lee; [email protected]

Received 26 December 2019; Accepted 23 April 2020; Published 19 June 2020

Academic Editor: Inhi Kim

Copyright © 2020 Eunhan Ka et al. )is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Human errors cause approximately 90 percent of traffic accidents, and drivers with risky driving behaviors are involved in about 52 percent of severe traffic crashes. Driver education using driving simulators has been used extensively to obtain a quantitative evaluation of driving behaviors without causing drivers to be at risk for physical injuries. However, since many driver education programs that use simulators have limits on realistic interactions with surrounding vehicles, they are limited in reducing risky driving behaviors associated with surrounding vehicles. )is study introduces surrogate safety measures (SSMs) into simulator- based training in order to evaluate the potential for crashes and to reduce risky driving behaviors in driving situations that include surrounding vehicles. A preliminary experiment was conducted with 31 drivers to analyze whether the SSMs could identify risky driving behaviors. )e results showed that 15 SSMs were statistically significant measures to capture risky driving behaviors. )is study used simulator-based training with 21 novice drivers, 16 elderly drivers, and 21 commercial drivers to determine whether a simulator-based training program using the SSMs is effective in reducing risky driving behaviors. )e risky driving behaviors by novice drivers were reduced significantly with the exception of erratic lane-changing. In the case of elderly drivers, speeding was the only risky driving behavior that was reduced; the others were not reduced because of their difficulty with manipulating the pedals in the driving simulator and their defensive driving. Risky driving behaviors by commercial drivers were reduced overall. )e results of this study indicated that the SSMs can be used to enhance drivers’ safety, to evaluate the safety of traffic management strategies as well as to reduce risky driving behaviors in simulator-based training.

1. Introduction

)e worldwide number of annual fatalities in traffic crashes reached 1.35 million each year, and this number continues to increase steadily in the world [1]. Human errors cause about 90 percent of all road accidents [2], and the majority of human errors involve risky driving. Drivers with risky driving behaviors such as speeding, following other vehicles too closely (tailgating), erratic driving, and violation of traffic laws accounted for about 52% of severe traffic acci- dents [3]. Moderating risky driving behaviors have been achieved successfully using a variety of approaches that

combine education, engineering, and enforcement; this approach to safety is known as the 3E principle [4]. Driver education has been used extensively to reduce risky driving behaviors. It has been reported to be an effective way to reduce traffic accidents by detecting risky driving behaviors and providing appropriate feedback to reduce these be- haviors [5]. Risky driving behaviors should be measured and evaluated quantitatively to give appropriate feedback to drivers in order to reduce risky driving behaviors.

Current driver education programs have focused on educating drivers about the skills and attitudes necessary to become a safe driver. Videos and lectures about traffic

Hindawi Journal of Advanced Transportation Volume 2020, Article ID 7525721, 12 pages https://doi.org/10.1155/2020/7525721

regulations and automobile-related knowledge, on-road training, and simulator-based training generally have been used in driver education programs. Videos and lectures help drivers acquire knowledge about driving safely by providing information about traffic regulations and the appropriate operation of automobiles. However, these approaches to teaching drivers have limitations in that they do not help improve the practical skills that are required in on-road driving [6]. On-road training with a driving instructor is an effective method to educate drivers to drive more safely on the road. However, even professional instruction and on- road training cannot address all of the potential crashes of driving because they cannot expose drivers to the various potential collision situations that can occur on the road.

Driving simulators are used extensively as a tool to instruct drivers to drive in a common driving environment as well as in collision situations that would be too dan- gerous to create in actual on-road driving [7]. )e in- structor can design various driving scenarios including myriads of road and traffic environments, movements of surrounding vehicles, and collision scenarios. )erefore, driving simulators can be used to give risky drivers re- peated training with various collision situations. Driving simulators can be used to measure driving behaviors quantitatively as well as to acquire the trajectory data for surrounding vehicles [8]. However, there are issues con- cerning the validity of virtual simulations of real driving environments. Current studies have shown that they have similar patterns, but the driving behaviors in driving simulators and on-road driving may not be the same [7, 9]. In other words, driving simulators can be useful tools for educational purposes in driver education programs. In fact, the driving instructors involved in a previous study thought that one-hour simulator training was as effective as three hours of on-road training [10].

Most research on reducing risky driving behaviors based on driving simulators has been conducted with a focus on drivers’ eye movements and the movements of the subject vehicle, i.e., movements such as erratic acceleration and deceleration, speed variation, and lane deviation [11–14]. Since risky driving behaviors cause severe road crashes, it is necessary to evaluate the crash potential in the interactions between the subject vehicle and surrounding vehicles, such as following leading vehicles (car-following) and changing lanes (lane-changing). However, few studies have evaluated the crash potential between the subject vehicle and sur- rounding vehicles, which would address the interactions between vehicles [15]. )is study implemented realistic interactions between the subject vehicle and surrounding vehicles in a driving simulator by applying traffic flow models to the movements of surrounding vehicles. In ad- dition, we examined surrogate safety measures (SSMs), which are used extensively in the field of road safety as useful measures for assessing crash potential or severity even on roads where no actual collisions have occurred. )e SSMs can increase our understanding of the situations that cause collisions. In this study, the SSMs were used to evaluate risky driving behaviors in order to evaluate vehicles’ crash potentials.

)e aim of this study was to determine whether the SSMs can identify risky driving behaviors in driving simulators and whether the SSMs are effective in improving drivers’ behaviors when the SSMs are used as evaluation measures in the simulator-based training.

2. Literature Review

Risky driving behaviors are defined differently by many organizations and in many studies. Since the motivation of a driver is difficult to determine, risky driving behaviors can only be judged and evaluated based on the motions of ve- hicles [16, 17]. Risky driving behaviors mean taking risks that endanger the safety of both the driver and other road users [18]. Generally, risky driving behaviors include speeding, noncompliance with traffic laws, tailgating, reckless changing speeds, erratic lane-changing, and threats to other drivers (yelling and horn honking) [3, 16, 19].

Driving simulators have been used increasingly for driver education because of the advantages they provide, including the freedom to present drivers with a wide variety of scenarios without any threat to their safety or the safety of other people [20]. Studies on reducing or evaluating risky driving behaviors using driving simulators have investigated mainly risky driving behaviors in terms of the drivers’ re- actions and the movements of vehicles. Studies of drivers’ reactions have used the movements of the eyes, the focus of gazes, the duration of glances, and the number of fixations as measures to evaluate drivers’ physical responses to collision situations [11, 14, 21]. )ese studies have shown that drivers’ perceptions of conflict situations improve after they have had driver education in which eye-tracking systems to improve the ability of novice drivers and older drivers to recognize situations where collisions could occur. Various studies have used response time, pressure on the accelerator, and pressure on the brake pedal to measure drivers’ re- sponses to collision situations and red lights at intersections [12, 13, 22]. Measures related to drivers’ reactions were used mainly to evaluate risk perception rather than to reduce risky driving behaviors.

Most of the studies related to the movements of vehicles have focused on the movements of the subject vehicle, and they evaluated primarily the risky behaviors of the drivers of the subject vehicle, e.g., erratic steering control, speeding, and tailgating. Erratic steering control involves the driver’s sudden and unexpected changes in steering the vehicle or how far the driver allows the vehicle to deviate from the center of its lane. )e steering angle, steering reversal rate, lane deviation, and mean lane position have been used in assessing erratic steering control [13, 14, 22–24]. Velocity, mean speed, speed variation, speeding, and acceleration are speeding-related measures that can be used to determine the driver’s compliance with the speed limit and the reckless changing of speed [13, 14, 22, 25]. Evaluations of the gap distance between vehicles and time-to-collision (TTC) with a leading vehicle were made mainly in a car-following sit- uation [15, 22]. However, the gap distance between vehicles and the TTC between the subject vehicle and the leading vehicle cannot take into consideration the accelerations and

2 Journal of Advanced Transportation

decelerations of either vehicle. Also, few studies have con- sidered the potential for crashes between vehicles when they are changing lanes [26].

Most studies have attempted to evaluate the effects of simulator-based training on risky driving behaviors. )ese studies focused principally on risky driving behaviors related to the movements of the subject vehicle. Since most risky driving behaviors require consideration of the subject ve- hicle’s interactions with surrounding vehicles, it is essential to evaluate the crash potential with one or more of the surrounding vehicles. However, research considering the interactions between vehicles has rarely been conducted because the movements of surrounding vehicles would not be implemented realistically. )is study attempted to in- troduce the SSMs into a simulator-based training program to evaluate the crash potential between vehicles and to re- duce the risky driving behaviors associated with the sur- rounding vehicles.

3. Methodology

3.1. Framework. )is study consisted of three parts: a survey of SSMs and scenario design, a preliminary experiment, and a simulator-based training program (see Figure 1). )e SSMs can be classified into measures that consider only the subject vehicle and measures that consider both the subject vehicle and surrounding vehicles. Before the SSMs were used as measures of driving behaviors, it was necessary to test whether the SSMs could detect risky driving behaviors and conservative driving behaviors in a driving simulator. )is study conducted a preliminary experiment for the sensitivity analysis of SSMs. )e purpose of the sensitivity analysis of SSMs was to ensure that SSMs could detect extreme driving behaviors, i.e., normal, conservative, and risky driving. In a preliminary experiment, each driver was required to engage in one of the three types of driving behaviors (normal, conservative, and risky) in the driving simulator. Finally, this study used a quantitative evaluation based on the SSMs to analyze whether drivers reduced their risky driving be- haviors after engaging in the simulator-based training program.

3.2. Survey of Surrogate Safety Measures. Since a simulator- based training requires immediate feedback concerning which driving behaviors are risky in the various driving scenarios, it is crucial to be able to calculate the SSMs used in the simulator-based training within a short time after driving in the driving simulators. )is study reviewed nu- merous studies about road safety in order to investigate the SSMs that can be used in simulator-based training, and 31 SSMs were selected as alternatives. Since 11 of the SSMs were challenging to calculate instantaneously in driving simula- tors or unsuitable in evaluating driving behaviors, 20 out of the 31 SSMs were selected as implementable SSMs. For example, the Crash Index is a measure concerning the se- verity of a potential crash, and it is presented in the form of the kinetic energy of the crash [27]. It is challenging to translate kinetic energy values into an easily understandable

account of the risk associated with a given participant’s driving behaviors. )us, this study excluded the Crash Index from the implementable SSMs and selected implementable SSMs as measures that can be explained easily to the drivers in simulator-based training.

)e implementable SSMs were divided into “Relating to the subject vehicle” and “Relating to surrounding vehicles,” depending on whether or not the SSMs related to interac- tions with surrounding vehicles. )e SSMs relating to the subject vehicle can be calculated without any interactions with surrounding vehicles (nos. 1 to 9 in Table 1). )e SSMs relating to surrounding vehicles consider interactions with surrounding vehicles, such as car-following situations and lane-changing situations (nos. 10 to 20 in Table 1). )e gap distance in the car-following situation (no. 10) was used to confirm the validation of driving errors between the driving simulator and on-road driving [7]. )e time-to-collision (TTC, no. 12) was used for studies in which driving be- haviors in critical situations were compared [15, 22, 35]. However, the gap distance and the TTC between the subject vehicle and the leading vehicle have the limitation that the difference between the acceleration of the subject vehicle and the deceleration of the leading vehicle cannot be considered. )is study used modified TTC (no. 13) and deceleration rate to avoid crash (no. 14) to evaluate the crash potential by considering the difference between the acceleration of the subject vehicle and the deceleration of the leading vehicle in situations where the subject vehicle is following the leading vehicle. )e study in which the driving behaviors were analyzed in the lane-changing situation used the gap dis- tance (no. 16 and no. 17) with the surrounding vehicles in a target lane [26]. )is study adopted the SSMs (nos.13, 14, 19, and 20) that had not been used to reduce risky driving behaviors in existing driving education programs to assess the crash potential between the subject vehicle and sur- rounding vehicles properly. )e contribution of this study is to secure the effectiveness of driver education by capturing interactions between a subject vehicle and surrounding vehicles based on the simulator-based training using SSMs and then ultimately induce the prevention and reduction of road accidents.

)e SSMs consist of measures with a single outcome for the dataset and measures with continuous outcomes cal- culated at every time step of the dataset. Accumulated speeding (AS), speed uniformity (SU), speed variation (SV), acceleration noise (AN), and lane deviation were measured with a single outcome and calculated after completing the driving scenarios. Measures with continuous outcomes should be transformed into a single representative value in order to evaluate how risky the drivers’ driving behaviors were.

A single representative value of SSMs can be obtained either as the maximum (minimum) value of total outcomes, such as Max S, i.e., the maximum velocity in a conflict situation [33] or as the ratio of conflicts defined as exceeding the threshold value of each measure [27, 36]. )is study adopted the minimum value as the representative value for SSMs related to lane-changing situations (see nos.15 to 20 in Table 1). )e method to define the threshold value for

Journal of Advanced Transportation 3

3.2 Survey of surrogate safety measures

Subject vehicle Subject vehicle and surrounding vehicles

(i) (ii)

31 drivers (preliminary experiment) 58 drivers (simulator- based training)

(i)

(ii)

Specification of driving simulator Scenario design

(i)

(ii)

3.3 Driving simulator and scenario design

3.4 Preliminary experiment

Normal driving Conservative driving Risky driving

(i) (ii)

(iii)

Driving before intervention Intervention (feedback) Driving after intervention

(i)

(ii) (iii)

3.5 Simulator-based training

3.6 Participants

Figure 1: Framework of the study.

Table 1: Description of implementable surrogate safety measures.

Relation with surrounding vehicles

No. Surrogate safety measure Unit Description

Relating to the subject vehicle

1 Accumulated speeding (AS) kph )e normalized relative area (per unit length) bounded between the speed profile values higher than the speed limit and the speed limit

line [28]

2 Speed uniformity (SU) kph )e normalized relative area (per unit length) bounded between the

speed profile and the average speed line [28] 3 Speed variation (SV) kph )e standard deviation of the speed 4 Acceleration (%) m/s2 )e acceleration of the subject vehicle 5 Deceleration (%) m/s2 )e deceleration of the subject vehicle 6 Acceleration noise (AN) m/s2 )e root mean square deviation of the acceleration [29] 7 Lane deviation m )e standard deviation of lane position [30] 8 Yaw rate (%) °/s )e rotational velocity around the z-axis of the subject vehicle [31] 9 Lane change (%) — )e number of lane change manoeuvres completed

Relating to surrounding vehicles

10 Gap distance (%) (GD) m )e longitudinal distance along a travelled way between one vehicle’s

leading surface and another vehicle’s trailing surface [32]

11 Proportion of stopping distance (%) (PSD)

— )e ratio of the distance available for manoeuvring to that of the necessary stopping distance to a projected point of collision [33]

12 Time-to-collision (%) (TTC) sec )e time interval required for one vehicle to strike another object if both objects continue on their current paths at their current speed

[32]

13 Modified TTC (%) (MTTC) sec )e time interval required for one vehicle to strike another object if both objects continue on their current paths at their current speed

and acceleration [32]

14 Deceleration rate to avoid

crash (%) (DRAC) m/s2

)e deceleration required by the following vehicle to come to a timely stop or attain a matching lead vehicle speed to avoid a rear-end crash

[34]

15 Min_Front_GD m )e minimum value of gap distance (GD) with leading vehicle of

current lane in lane-changing situation

16 Min_Lag_GD m )e minimum value of gap distance (GD) with lag vehicle of target

lane in lane-changing situation

17 Min_Lead_GD m )e minimum value of gap distance (GD) with leading vehicle of

target lane in lane-changing situation

18 Min_Front_TTC sec )e minimum value of time-to-collision (TTC) with leading vehicle

of current lane in lane-changing situation

19 Min_Lag_TTC sec )e minimum value of time-to-collision (TTC) with lag vehicle of

target lane in lane-changing situation

20 Min_Lead_TTC sec )e minimum value of time-to-collision (TTC) with leading vehicle

of target lane in lane-changing situation

4 Journal of Advanced Transportation

counting the number of conflicts in each of the SSMs was derived from the existing literature [37]. )e 85th percentile value of total participants’ driving data distribution was used as the threshold for SSMs for which higher values indicated more risky driving behaviors (acceleration (%), yaw rate (%), lane change (%), gap distance (%), and proportion of stopping distance (%)). For SSMs for which lower values indicated more risky driving behaviors (deceleration (%), time-to-collision (%), modified TTC (%), and deceleration rate to avoid crash (%)), the 15th percentile value of total participants’ driving data distribution was used as the threshold.

3.3. Driving Simulator and Scenario Design. )e driving simulator used in this study was mounted on a six-degree- of-freedom motion system, with a size of 3500 × 3500 × 3500 mm. )e visual system for the driving simulator consisted of three 43-inch full HD LED monitors, providing a 150-degree field of view with a resolution of 5760 ×1080 pixels and a 60 Hz refresh rate. )e virtual environment with various driving conditions was repre- sented through the three monitors, with rear-view and side- view mirrors visible on the center monitor and side mon- itors, respectively (Figure 2(a)). )e vehicle dynamics were validated based on the real motion of the Hyundai Sonata.

A part of the street grid in Seoul was implemented in a virtual environment in order to enhance the reality of the driving environment. )e total length of the designed route in the scenario was 10.1 km, including freeway (2.3 km), urban roads (6.0 km), and rural roads (1.8 km) (Figure 2(b)). )e freeway consisted of the main freeway segment with a posted speed limit of 110 kph and an off-ramp. )e urban roads included ten signal intersections located every 200–400 m on a four-lane two-way road with a speed limit of 60 kph. )e rural roads were either two- or four-lane, two- way roads with a speed limit of 80 kph.

)e movements of surrounding vehicles significantly determine the mental load and ability to drive a vehicle. If the movements of the surrounding vehicles were not real enough, there is a possibility that drivers will drive a vehicle differently than they would in actual driving, meaning that the results and conclusions obtained from the simulation would not be applicable in actual driving. Many studies using driving simulators have been limited in expressing realistic movements because the movements of the vehicles were very strictly controlled to assess the drivers’ abilities in certain crash situations [38]. )erefore, if the movements of the surrounding vehicle are unrealistic and strictly con- trolled irrespective of the movements of the subject vehicle, it would be difficult to expect the reduction of risky driving behaviors in actual driving on the road through simulator- based training. In order to implement the realistic inter- actions with surrounding vehicles, traffic flow models (i.e., a car-following model, a lane-changing model, and a gap- acceptance model) were modeled based on video data and vehicle trajectory data, and then, they were applied to the movements of the surrounding vehicles. Using the traffic flow models had the additional benefit of showing different

movements in each trial, thereby increasing the sense of reality and preventing participants from adapting to the scenario [38]. )e generalized model of car-following was estimated with data obtained from random vehicles on the West-Hanam IC and the West-Icheon IC of the Jungbu Highway [39]. )e lane-changing model was implemented based on the vehicle trajectory data measured by nine video cameras in the upper 400 m section of the Middle East IC of the Seoul Ring Expressway for discretionary and mandatory lane-changing. )e parameters of a logit model were esti- mated with the gap distance and the speed of the subject vehicle as independent variables. )e logit model was es- timated for the gap-acceptance model at the intersection. Data were collected using video cameras at six intersections in Seoul to estimate the parameters of the gap-acceptance model, and the data included the time gap, type of vehicle, and traffic volume. )is study estimated the parameters of the logit models for an unprotected left turn, an unprotected right turn, and a roundabout using collected data.

3.4. Preliminary Experiment for Extreme Driving Behaviors with SSMs. )is study conducted a preliminary experiment to analyze the sensitivity of SSMs for extreme driving be- haviors. Before participating in the preliminary experiment, the participants were shown how to control a driving simulator and performed one to three minutes of practice driving to prevent simulator sickness and to adapt to the virtual environment of the driving simulator. To use the SSMs that could measure the crash potential of surrounding vehicles in a driving simulator, the sensitivity analysis of the SSMs was required to determine whether they could capture risky driving behaviors. In this study, the experimental methods that had been used in previous studies were used to analyze the sensitivity of the SSMs to extreme driving be- haviors. Past participants were involved in a study of ex- treme driving behaviors that compared the difference in fuel consumption depending on driving behaviors and com- pared the difference in the performance of an urban network on driving behaviors [40, 41].

Participants in the preliminary experiment were asked to drive “normally,” “conservatively,” or “riskily.” In normal driving, the participants drove the way they usually drive. In the conservative driving condition, the participants were asked to maintain a greater safe following distance, accel- erate and decelerate as gently as possible, and keep their speed under the speed limit. In risky driving, the participants were required to complete their driving route within 10 minutes rather than the typical 15 minutes, to follow the leading vehicle more closely than the recommended safe distance, and to change lanes and the speed of the vehicle erratically.

3.5. Simulator-Based Training to Improve Driving Behaviors. )is study used SSMs that statistically could capture risky and conservative driving within the simulator-based train- ing conducted by the Korea Transportation Safety Authority (KOTSA). )e simulator-based training consisted of three parts, i.e., driving before the intervention, intervention

Journal of Advanced Transportation 5

(feedback based on results of driving behaviors), and driving after the intervention.

Before the intervention, the participants drove an in- troduction drive for 1 to 3 minutes to become accustomed to the control of the driving simulator and the virtual envi- ronment. Subsequently, they drove the driving scenario as they usually would do, allowing the instructor to examine the extent of their risky driving behaviors.

)e intervention consisted of two parts: feedback with a video replay of the driver’s driving and a commentary video. )e instructors provided feedback to the participants con- cerning how risky they drove in terms of six risky driving behaviors, i.e., speeding, reckless changing speed, rapid acceleration and deceleration, erratic steering control, tail- gating, and erratic lane-changing. )e speeding, reckless changing speeds, rapid acceleration and deceleration, and erratic steering control only assessed the movements of the subject vehicle. In contrast, the tailgating and erratic lane- changing assessed the crash potential with the surrounding vehicles in the normal driving environment. In typical drivers’ education programs, the instructor evaluates the drivers’ driving behaviors based on the movements of the subject vehicle and the crash potential with the surrounding vehicles in specific situations (i.e., speeding, reckless changing speeds, rapid acceleration and deceleration, and erratic steering control). In the simulator-based training using the SSMs of this study, the instructor informed the drivers the risky driving behaviors, including situations in which they were following a vehicle and changing lanes in a common driving environment and were riskier than other drivers. In other words, the contribution of this study is to evaluate the movements of the subject vehicle as well as the interactions between vehicles by identifying the risky driving behaviors such as tailgating in car-following situations and erratic lane-changing in lane-changing situations. Also, the instructor educated the drivers about safe methods for driving on the road to reduce the crash potential between the vehicles. In the commentary video, videos of actual crashes

attributable to each of the six risky driving behaviors were shown to encourage safe driving.

After the feedback session at the end of the intervention, the drivers were asked to drive again so that their driving behaviors could be observed in order to determine whether their driving behaviors had been improved; i.e., whether their risky driving behaviors were reduced. )e instructor showed the participants how much their driving had im- proved in terms of the frequency of six risky driving be- haviors and to encourage safe driving.

3.6. Participants. For this study, we posted advertisements and recruited 43 participants for the preliminary study. Out of the 43 participants, 12 participants dropped out of the experiment due to simulator sickness, leaving 31 partici- pants, i.e., 21 males and 10 females. )e average age of the participants was about 36 years old, and the average driving experience of participants was almost 13 years.

Existing research on simulator-based training was mainly conducted with novice, elderly, and commercial drivers [13, 20, 23, 42, 43] because drivers in these three groups tend to have higher accident rates, making them the primary targets for improving risky driving [44–46]. Par- ticipants in the simulator-based training were recruited separately from the three driver groups by posted adver- tisements. Out of 69 participants, 11 participants dropped out of the experiment due to simulator sickness, leaving 58 participants, i.e., 21 novice drivers, 16 elderly drivers, and 21 commercial drivers. )e average age of the participants was approximately 46 years old, and the average driving expe- rience of participants was slightly more than 20 years. )e demographic statistics of the participants are provided in Table 2.

4. Results

4.1. Results of the Sensitivity Analysis for Extreme Driving Behaviors with SSMs. In this study, we conducted a

(a)

Freeway (2.3 km) Urban (6.0 km)

Rural (1.8 km) Toll plaza

Finish

Start

(b)

Figure 2: Driving simulator and scenario in this study: (a) the driving simulator set-up; (b) the designed route in the scenario.

6 Journal of Advanced Transportation

sensitivity analysis for extreme driving behaviors to test whether SSMs could significantly distinguish between normal, conservative, and risky driving. Twenty SSMs were analyzed for extreme driving behaviors for 31 drivers. )e SSMs were analyzed for the entire road section and the freeway section because the lane-changing in the urban road section was forced due to the direction of the designed travel route, in contrast to the discre- tionary lane changes in the freeway section. In this study, driving behaviors except lane-changing were evaluated over the entire road section, but lane-changing was evaluated only for the freeway section. Also, an ANOVA test and a post-hoc test (Tukey HSD) at the 95% signif- icance level were performed to evaluate whether the SSMs could detect significant differences in extreme driving behaviors across the different conditions.

Differences were not statistically significant for five SSMs, i.e., lane deviation, DRAC, Min_Front_GD, Min_Lead_GD, and Min_Lag_TTC (Table 3). Lane deviation and DRAC, which evaluate driving behavior over the entire road section, did not exhibit significant differences. Few studies have compared lane deviations for different degrees of extreme driving behaviors. However, one previous study found that there was no significant difference in the mean of lane de- viation before and after training [25]. In contrast to GD, TTC, and DRAC, the measure of minimum deceleration to avoid a collision has been known to be limited to reflect a conflict situation, and drivers may fail to adjust DRAC to avoid a conflict situation [47].

When a driver changes lanes on the freeway, the subject vehicle enters the target lane at a higher speed than

the front vehicle in the current lane and the lead vehicle in the target lane. Min_Front_GD and Min_Lead_GD be- come shortened when changing lanes, and the driver changed lanes in a situation in which the driver main- tained the distance between the surrounding vehicles necessary for changing lanes. Regardless of the driver’s extreme driving behaviors, Min_Front_GD and Min_- Lead_GD were not significantly different. However, Min_Front_TTC and Min_Lead_TTC, which reflect the relative speed differences between vehicles, showed sta- tistically significant differences because the entry speed in risky driving is higher than that in conservative driving. Min_Lag_GD showed a statistically significant difference because drivers changed lanes at a shorter gap distance in risky driving and a longer gap distance in conservative driving than in normal driving. In the scenario of this study, the lag vehicle in the target lane decelerated when the subject vehicle entered the target lane because the car- following model was applied to the movements of sur- rounding vehicles. )erefore, there was no statistically significant difference between the extreme driving be- haviors of Min_Lag_TTC due to the decrease in the speed of the lag vehicle even though the distance between the subject vehicle and the lag vehicle in the target lane was shorter in risky driving.

4.2. Results of Improvement for Risky Driving Behaviors with SSMs. In this study, we classified 15 SSMs into six types of risky driving behaviors to improve the driver’s under- standing of SSMs in an intervention of simulator-based

Table 2: Demographics of the participants in this study.

Type Preliminary experiment (n � 31) Simulator-based training (n � 58)

N Percent (%) N Percent (%) Age 20–29 13 41.94 20 34.48 30–39 7 22.58 2 3.44 40–49 6 19.35 4 6.90 50–59 2 6.45 16 27.59 ≥60 3 9.68 16 27.59

Gender Male 21 67.74 43 74.14 Female 10 32.36 15 25.86

Driving years ≤2 years 6 19.35 21 36.21 3–20 years 16 51.61 5 8.62 21–39 years 8 25.81 25 43.10 ≥40 years 1 3.23 7 12.07

Crash experience None 26 83.87 42 72.41 1 2 6.45 4 6.90 2 3 9.68 11 18.97 ≥3 0 0.00 1 1.72

Traffic violation None 24 77.41 37 63.79 1 5 16.13 13 22.41 2 1 3.23 4 6.90 ≥3 1 3.23 4 6.90

Journal of Advanced Transportation 7

training. Fifty-eight drivers were provided with feedback on their driving behaviors based on 15 SSMs in simulator-based training, and they were taught how to reduce their risky driving behaviors.

4.2.1. Comparison among Novice, Elderly, Commercial, and Typical Drivers. Before analyzing the effect of simulator- based training using SSMs on the improvement of driving behaviors, it was necessary to identify how risky the drivers participating in the simulator-based training were. )is is because drivers who had safe driving behaviors were less likely to improve their driving behaviors, even though they were trained. In this study, 31 drivers who participated in the sensitivity analysis experiment were selected as typical drivers to compare with the three driver groups, i.e., novice, elderly, and commercial drivers. Because the 31 drivers were recruited randomly irrespective of gender, age, and driving experience, the risky driving behaviors of the three driver groups before the intervention were analyzed and compared based on the normal driving of typical drivers. )e larger the values of Min_Lag_GD, Min_Front_TTC, and Min_Lead_TTC were, the safer the drivers’ driving behaviors drivers were. )e smaller the values of the other SSMs were, the safer the driving behaviors of the drivers were (see Figure 3).

Novice drivers were found to be riskier than typical drivers except for Min_Lag_GD and Min_Lead_TTC (see Figure 3). Novice drivers have poor driving skills because of low driving experience [48]. Since novice drivers have diffi- culty in maintaining a safe distance from a leading vehicle because of their lack of driving experience, they were inclined to tailgate leading vehicles to a greater extent than typical drivers. Also, novice drivers had limited in-advance per- ception of dangerous situations, and therefore, it also was difficult for them to maintain a constant speed [43].

Elderly drivers were found to be riskier than typical drivers in the cases of deceleration, AN, and yaw rate. In contrast, other measures of elderly drivers were similar to or safer than that of typical drivers (see Figure 3). )ese results were consistent with the results that elderly drivers do not perceive the brake pedal pressure in the driving simulator as accurately as young drivers [43].

Commercial drivers were found to be riskier than typical drivers except for lane change and four SSMs related to tailgating (see Figure 3). )is suggests that commercial drivers tend to be more risky drivers than typical drivers to save time, but maintaining a safe dis- tance from a leading vehicle is essential for drivers of heavy trucks and buses [42].

4.2.2. Comparison of before and after Intervention by Driver Group. )e SSMs used as evaluation measures were sta- tistically tested at the 95% significance level with paired t- tests to determine whether drivers’ behaviors improved before and after the training in simulator-based training (see Table 4). In the case of novice drivers, Min_Lag_GD and Min_Front_TTC in lane-changing situations showed that the improvement before and after the training was not statistically significant. With the exception of AS, acceler- ation, deceleration, and AN, elderly drivers did not show statistically significant improvements after the training. In the case of commercial drivers, there were statistically sig- nificant differences for all measures except for Min_- Front_TTC in lane-changing situations. )e improvement in driving behaviors after the training was the greatest in commercial drivers and the least in elderly drivers.

In the case of novice drivers, the improvements in speeding, rapid acceleration and deceleration, and tailgating

Table 3: Summary of statistics for surrogate safety measures in sensitivity analysis.

No. SSMs Entire road section Freeway section

ANOVA Post-hoc ANOVA Post-hoc F-value p-value p-value F-value p-value p-value

1 AS F(2, 90) � 33.12 0.00 0.00 2 SU F(2, 90) � 35.73 0.00 0.00 3 SV F(2, 90) � 37.69 0.00 0.00 4 Acceleration F(2, 90) � 16.80 0.00 0.00 5 Deceleration F(2, 90) � 39.24 0.00 0.00 6 AN F(2, 90) � 28.41 0.00 0.00 7 Lane deviation F(2, 90) � 0.41 0.66 — 8 Yaw rate F(2, 90) � 30.77 0.00 0.00 9 Lane change F(2, 90) � 7.71 0.00 0.00 10 GD F(2, 90) � 29.05 0.00 0.00 11 PSD F(2, 90) � 29.58 0.00 0.00 12 TTC F(2, 90) � 16.22 0.00 0.00 13 MTTC F(2, 90) � 14.28 0.00 0.00 14 DRAC F(2, 90) � 2.91 0.06 — 15 Min_Front_GD F(2, 90) � 2.95 0.06 — 16 Min_Lag_GD F(2, 90) � 11.74 0.00 0.00 17 Min_Lead_GD F(2, 90) � 2.76 0.07 — 18 Min_Front_TTC F(2, 90) � 6.33 0.00 0.04 19 Min_Lag_TTC F(2, 90) � 0.16 0.85 — 20 Min_Lead_TTC F(2, 90) � 8.87 0.00 0.01

8 Journal of Advanced Transportation

0

2

4

A S

6

Typical Novice Elderly Driver group

Commercial

(a)

Typical Novice Elderly Driver group

Commercial

12

10

14

16

18

SU

(b)

Typical Novice Elderly Driver group

Commercial

35

30

25

20

SV

(c)

Typical Novice Elderly Driver group

Commercial

0.10

0.15

0.20

0.25

0.30

0.35

A cc

el er

at io

n

(d)

Typical Novice Elderly Driver group

Commercial 0.05

0.10

0.15

0.20

0.25

D ec

el er

at io

n

(e)

Typical Novice Elderly Driver group

Commercial 1.5

2.0

2.5

3.0

3.5

A N

(f)

Typical Novice Elderly Driver group

Commercial

0.100

0.125

0.150

0.175

Ya w

ra te

(g)

Typical Novice Elderly Driver group

Commercial

La ne

c ha

ng e

30

35

40

45

(h)

Typical Novice Elderly Driver group

Commercial

0.05

0.00

0.10

0.15

0.20

G D

(i)

Typical Novice Elderly Driver group

Commercial

0.0

0.1

0.2

PS D

(j)

Typical Novice Elderly Driver group

Commercial

0.1

0.2

T T

C

(k)

Typical Novice Elderly Driver group

Commercial

0.10

0.15

0.20

0.25

0.30

0.05

M T

T C

(l)

Typical Novice Elderly Driver group

Commercial

M in

_L ag

_G D

0

100

200

300

400

500

(m)

Typical Novice Elderly Driver group

Commercial

M in

_F ro

nt _T

T C

0

25

50

75

100

(n)

Typical Novice Elderly Driver group

Commercial

M in

_L ea

d_ T

T C

0

25

50

75

100

(o)

Figure 3: Comparison of surrogate safety measures by driver group: (a) AS (speeding); (b) SU (reckless changing speeds); (c) SV (reckless changing speeds); (d) acceleration (rapid acc. and dec.); (e) deceleration (rapid acc. and dec.); (f) AN (rapid acc. and dec.); (g) yaw rate (erratic steering control); (h) lane change (erratic steering control); (i) GD (tailgating); (j) PSD (tailgating); (k) TTC (tailgating); (l) MTTC (tailgating); (m) Min_Lag_GD (erratic lane-changing); (n) Min_Front_TTC (erratic lane-changing); (o) Min_Lead_TTC (erratic lane-changing).

Journal of Advanced Transportation 9

were significant, but no significant improvements were observed in erratic lane-changing (see Table 4). )rough the simulator-based training, most novice drivers improved their ability to maintain a safe distance from the leading vehicle. However, they did not show improvement in keeping a safe distance from adjacent vehicles in lane- changing situations. )erefore, although novice drivers improved following the simulator training in the car-fol- lowing situation, additional training should be provided for maintaining a safe distance between vehicles when changing lanes.

Elderly drivers showed improvement in speeding, rapid acceleration, and deceleration (see Table 4). Since elderly drivers tend to be more defensive than other driver groups, they showed the least improvement in risky driving be- haviors of the three driver groups. Compared to the younger drivers, elderly drivers were less aware of the brake pedal pressure in a driving simulator during deceleration. )ey showed a tendency to decrease speed rapidly in the driving simulator before the intervention. After the in- tervention, the deceleration behavior of elderly drivers was improved because the instructors requested that they begin their deceleration earlier to prevent erratic deceleration. However, there is a limit to the conclusions that the simulator-based training improved the erratic decelerating behavior of elderly drivers since their erratic deceleration behavior may have resulted from their use of the driving simulator.

After the intervention, the driving behaviors of com- mercial drivers were improved in all 15 SSMs. )e im- provements in speeding, rapid acceleration, rapid deceleration, and tailgating were more significant than the other risky driving behaviors (see Table 4). Since the commercial drivers had shown more risky driving behaviors than other groups of drivers, their driving behaviors were improved to a greater extent by the simulator-based training than driving behaviors of drivers for other groups. )ese

results showed that the simulator-based training program is effective in reducing risky driving behaviors of various driver groups by providing feedback on how risky their driving behaviors are.

5. Conclusions

)is study implemented SSMs in a simulator-based training program to evaluate the crash potential with surrounding vehicles. )e movements of surrounding vehicles need to be realistic to consider the interactions with surrounding vehicles in driver education. Traffic flow models developed from data collected on real roads were implemented for the movements of surrounding vehicles (car-following, lane-changing, and gap-acceptance at intersection). Twenty SSMs were implemented in the driving simulator. )e preliminary experiment that was conducted with 31 participants verified that 15 SSMs could be used to capture risky driving behaviors. )e 15 selected SSMs were used as the measure for current simulator-based training in the Republic of Korea to evaluate the driving behaviors of novice, elderly, and commercial drivers.

After the intervention of the simulator-based training, the risky driving behaviors of novice drivers, elderly drivers, and commercial drivers were reduced in different ways. In the case of novice drivers, additional on-road training was required to reduce risky driving behaviors in lane-changing situations. For elderly drivers, the speeding and rapid acceleration behaviors were improved. However, other risky driving behaviors were not statistically reduced because elderly drivers already drive vehicles safely so that there was nothing to improve significantly except for speeding, rapid acceleration, and deceleration. )e training using the driving simulator reduced the risky driving be- haviors of commercial drivers. )e reason that simulator-

Table 4: Summary of statistics for surrogate safety measures in before and after intervention.

Types of risky driving behaviors

SSMs

Novice Elderly Commercial (n � 21) (n � 16) (n � 21)

μbefore μafter t-stat μbefore μafter t-stat μbefore μafter t-stat (p-value) (p-value) (p-value)

Speeding AS 3.54 1.48 5.41 (0.00) 0.71 0.29 2.46 (0.03) 2.83 0.42 5.54 (0.00)

Reckless changing speeds SU 15.03 12.97 4.10 (0.00) 13.51 12.73 1.29 (0.22) 15.50 13.37 5.20 (0.00) SV 30.62 25.53 6.34 (0.00) 23.00 21.86 1.92 (0.07) 28.70 23.50 9.19 (0.00)

Rapid acceleration and deceleration

Acceleration 0.24 0.15 6.94 (0.00) 0.21 0.17 2.28 (0.04) 0.24 0.14 8.70 (0.00) Deceleration 0.17 0.11 6.07 (0.00) 0.14 0.12 2.46 (0.03) 0.17 0.10 7.01 (0.00)

AN 2.97 2.27 6.20 (0.00) 2.80 2.49 3.19 (0.01) 2.95 2.04 8.54 (0.00)

Erratic steering control Yaw rate 0.14 0.12 2.85 (0.01) 0.13 0.12 1.95 (0.07) 0.14 0.12 3.57 (0.00)

Lane change 39.38 36.43 1.89 (0.07) 35.94 33.69 1.35 (0.20) 37.10 33.90 3.36 (0.00)

Tailgating

GD 0.14 0.07 3.84 (0.00) 0.02 0.02 0.08 (0.94) 0.09 0.04 4.32 (0.00) PSD 0.19 0.10 4.42 (0.00) 0.04 0.03 0.56 (0.58) 0.12 0.06 5.07 (0.00) TTC 0.21 0.17 2.51 (0.02) 0.07 0.06 1.04 (0.31) 0.14 0.10 4.39 (0.00) MTTC 0.24 0.15 4.39 (0.00) 0.17 0.14 1.77 (0.10) 0.18 0.10 4.67 (0.00)

Erratic lane-changing Min_Lag_GD 27.75 49.99 −0.88 (0.39) 79.01 90.95 −0.29 (0.78) 5.42 84.24 −2.45 (0.02)

Min_Front_TTC 76.71 91.01 −0.30 (0.77) 117.18 76.87 0.89 (0.39) 27.97 127.39 −2.78 (0.05) Min_Lead_TTC 17.09 41.00 −2.66 (0.02) 58.17 60.47 −0.15 (0.88) 16.38 48.78 −2.78 (0.01)

10 Journal of Advanced Transportation

based training was most effective for reducing the risky driving behaviors for commercial drivers was that their behaviors were risky compared to the behaviors of other groups of drivers.

)e results of this study showed that SSMs could be used both for road safety and traffic management strategies and for the evaluation of individual drivers’ driving behaviors in driver education. However, there were two limitations in this study that should be addressed in future research. First, the possibility that adaptation to manipulating a driving sim- ulator after the intervention has a positive effect on reducing risky driving behaviors cannot be ruled out. )is study did not compare drivers trained in simulator-based training using SSMs with drivers trained in the previous simulator- based training. In this study, there are improvements in various driving behaviors by giving the drivers feedback using SSMs, but it is possible that intervention without SSMs also could contribute to reducing risky driving behaviors. )erefore, future research should determine the extent to which the intervention based on feedback using SSMs contributes to reducing risky driving behavior compared to existing simulator-based training. Second, it is unclear whether simulator-based training using SSMs will result in the reduction of risky driving behaviors in actual driving. )is study only analyzed instantaneous training effects in simulator driving. Future research should examine how simulator-based training using SSMs reduces risky driving behaviors in actual driving. )erefore, it is necessary to study whether the trends of SSMs are the same by comparing drivers in the existing simulator-based training with those in the simulator-based training proposed in this study by comparing actual driving data with data from the driving simulator.

Data Availability

)e data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

)e authors declare that there are no conflicts of interest regarding the publication of this paper.

Acknowledgments

)is research was supported by the Institute of Construction and Environmental Engineering and the Institute of Engi- neering Research at Seoul National University. )e authors wish to express their gratitude for their support.

References

[1] World Health Organization, Global Status Report on Road Safety 2018, World Health Organization, Geneva, Switzerland, 2018.

[2] NHTSA, National Motor Vehicle Crash Causation Survey: Report to Congress, NHTSA, U.S. Department of Trans- portation, Washington, DC, USA, 2008.

[3] NHTSA, Traffic Safety Facts Annual Report Tables, NHTSA, Washington, DC, USA, 2019.

[4] T. R. Neuman, R. Pfefer, K. L. Slack et al., “NCHRP report 500,” in Guidance for Implementation of the AASHTO Stra- tegic Highway Safety Plan: A Guide for Addressing Aggressive- Driving Collisions, vol. 23, Transportation Research Board, Washington, DC, USA, 2003.

[5] T. Toledo, O. Musicant, and T. Lotan, “In-vehicle data re- corders for monitoring and feedback on drivers’ behavior,” Transportation Research Part C: Emerging Technologies, vol. 16, no. 3, pp. 320–331, 2008.

[6] P. K. Alvaro, N. M. Burnett, G. A. Kennedy et al., “Driver education: enhancing knowledge of sleep, fatigue and risky behaviour to improve decision making in young drivers,” Accident Analysis & Prevention, vol. 112, pp. 77–83, 2018.

[7] L. Meuleners and M. Fraser, “A validation study of driving errors using a driving simulator,” Transportation Research Part F: Traffic Psychology and Behaviour, vol. 29, pp. 14–21, 2015.

[8] O. M. J. Carsten, J. A. Groeger, E. Blana, and A. H. Jamson, Driver Performance in the EPSRC Driving Simulator: A Validation Study. Report No. GR/K56162, University of Leeds, Leeds, UK, 1997.

[9] D. L. Fisher, M. Rizzo, J. Caird, and J. D. Lee, Handbook of Driving Simulation for Engineering, Medicine, and Psychology, CRC Press, Boca Raton, FL, USA, 2011.

[10] B. Kappé, M. van Emmerik, W. van Winsum, and A. Rozendom, Virtual Instruction in Driving Simulators, Presented at Driving Simulator Conference, Dearborn, MI, USA, 2003.

[11] D. L. Roenker, G. M. Cissell, K. K. Ball, V. G. Wadley, and J. D. Edwards, “Speed-of-Processing and driving simulator training result in improved driving performance,” Human Factors: >e Journal of the Human Factors and Ergonomics Society, vol. 45, no. 2, pp. 218–233, 2003.

[12] D. Crundall, B. Andrews, E. van Loon, and P. Chapman, “Commentary training improves responsiveness to hazards in a driving simulator,” Accident Analysis & Prevention, vol. 42, no. 6, pp. 2117–2124, 2010.

[13] E. M. M. Jongen, K. Brijs, M. Komlos, T. Brijs, and G. Wets, “Inhibitory control and reward predict risky driving in young novice drivers - a simulator study,” Procedia—Social and Behavioral Sciences, vol. 20, pp. 604–612, 2011.

[14] P. V. Leeuwen, R. Happee, and J. D. Winter, “Vertical field of view restriction in driver training: a simulator-based evalu- ation,” Transportation Research Part F: Traffic Psychology and Behaviour, vol. 24, pp. 169–182, 2014.

[15] F. Bella and G. D’Agostini, “Combined effect of traffic and geometrics on rear-end collision risk,” Transportation Re- search Record: Journal of the Transportation Research Board, vol. 2165, no. 1, pp. 96–103, 2010.

[16] AAA Foundation for Traffic Safety (AAAFTS), Aggressive Driving: Research Update, AAA Foundation for Traffic Safety, Washington, DC, USA, 2009.

[17] D. Wiesenthal, J. Roseborough, and C. Wickens, “Behavioral approaches for combating aggressive driving (Chap. 8),” in Routledge Handbook of Transportation, D. Teodorovic, Ed., Taylor & Francis, New York, NY, USA, 2015.

[18] I. Richer and J. Bergeron, “Differentiating risky and aggressive driving: further support of the internal validity of the Dula Dangerous Driving Index,” Accident Analysis & Prevention, vol. 45, pp. 620–627, 2012.

Journal of Advanced Transportation 11

[19] D. Lord and S. Washington, Safe Mobility: Challenges, Methodology and Solutions, Emerald Group Publishing, Bingley, UK, 2018.

[20] L. M. Mart́ın-delosReyes, E. Jiménez-Mej́ıas, V. Mart́ınez- Ruiz, E. Moreno-Roldán, D. Molina-Soberanes, and P. Lardelli-Claret, “Efficacy of training with driving simulators in improving safety in young novice or learner drivers: a systematic review,” Transportation Research Part F: Traffic Psychology and Behaviour, vol. 62, pp. 58–65, 2019.

[21] A. Pollatsek, V. Narayanaan, A. Pradhan, and D. L. Fisher, “Using eye movements to evaluate a PC-based risk awareness and perception training program on a driving simulator,” Human Factors: >e Journal of the Human Factors and Er- gonomics Society, vol. 48, no. 3, pp. 447–464, 2006.

[22] Y. Ba, W. Zhang, G. Salvendy, A. S. K. Cheng, and P. Ventsislavova, “Assessments of risky driving: a Go/No-Go simulator driving task to evaluate risky decision-making and associated behavioral patterns,” Applied Ergonomics, vol. 52, pp. 265–274, 2016.

[23] Y. Wang, W. Zhang, and G. Salvendy, “Effects of a simulation- based training intervention on novice drivers’ hazard han- dling performance,” Traffic Injury Prevention, vol. 11, no. 1, pp. 16–24, 2010.

[24] P. Freeman, D. M. Neyens, J. Wagner, F. Switzer, K. Alexander, and P. Pidgeon, “A video based run-off-road training program with practice and evaluation in a simulator,” Accident Analysis & Prevention, vol. 82, pp. 1–9, 2015.

[25] L. Dorn and D. Barker, “)e effects of driver training on simulated driving performance,” Accident Analysis & Pre- vention, vol. 37, no. 1, pp. 63–69, 2005.

[26] Y. Ali, Z. Zheng, and M. M. Haque, “Connectivity’s impact on mandatory lane-changing behaviour: evidences from a driving simulator study,” Transportation Research Part C: Emerging Technologies, vol. 93, pp. 292–309, 2018.

[27] K. Ozbay, H. Yang, B. Bartin, and S. Mudigonda, “Derivation and validation of new simulation-based surrogate safety measure,” Transportation Research Record: Journal of the Transportation Research Board, vol. 2083, no. 1, pp. 105–113, 2008.

[28] A. T. Moreno and A. Garćıa, “Use of speed profile as surrogate measure: effect of traffic calming devices on crosstown road safety performance,” Accident Analysis & Prevention, vol. 61, pp. 23–32, 2013.

[29] R. Herman, E. W. Montroll, R. B. Potts, and R. W. Rothery, “Traffic dynamics: analysis of stability in car following,” Operations Research, vol. 7, no. 1, pp. 86–106, 1959.

[30] M. R. Savino, “Standardized names and definitions for driving performance measures,” MS. thesis, Tufts University, Med- ford, MA, USA, 2009.

[31] H. Hagelin, “Detection of critical events using limited sen- sors,” MS. thesis, Linköping University, Linköping, Sweden, 2012.

[32] Society of Automotive Engineers, SAE J2944 201506. Oper- ational Definitions of Driving Performance Measures and Statistics, Society of Automotive Engineers, Detroit, MI, USA, 2015.

[33] D. Gettman and L. Head, “Surrogate safety measures from traffic simulation models,” Transportation Research Record: Journal of the Transportation Research Board, vol. 1840, no. 1, pp. 104–115, 2003.

[34] F. F. Saccomanno, F. Cunto, G. Guido, and A. Vitale, “Comparing safety at signalized intersections and round- abouts using simulated rear-end conflicts,” Transportation

Research Record: Journal of the Transportation Research Board, vol. 2078, no. 1, pp. 90–95, 2008.

[35] W. Wang, Q. Cheng, C. Li, D. André, and X. Jiang, “A cross- cultural analysis of driving behavior under critical situations: a driving simulator study,” Transportation Research Part F: Traffic Psychology and Behaviour, vol. 62, pp. 483–493, 2019.

[36] D. Gettman, L. Pu, T. Sayed, S. Shelby, and I. T. S. Siemens, Surrogate Safety Assessment Model and Validation, Publica- tion FHWA-HRT-08-05. FHWA, U.S. Department of Transportation, Washington, DC, USA, 2008.

[37] R. H. Wortman and J. S. Matthias, “An evaluation of driver behavior at signalized intersections,” Transportation Research Record, vol. 904, pp. 10–20, 1983.

[38] J. Olstam, Simulation of Surrounding Vehicles in Driving Simulators. PhD >esis, Linköping University, Linköping, Sweden, 2009.

[39] D. C. Gazis, R. Herman, and R. B. Potts, “Car-following theory of steady-state traffic flow,” Operations Research, vol. 7, no. 4, pp. 499–505, 1959.

[40] M. F. Chang and R. Herman, “Driver response to different driving instructions: effect on speed, acceleration and fuel consumption,” Traffic Engineering and Control, vol. 21, no. 11, pp. 545–550, 1980.

[41] R. Herman, L. A. Malakhoff, and S. A. Ardekani, “Trip time- stop time studies of extreme driver behaviors,” Transportation Research Part A: General, vol. 22, no. 6, pp. 427–433, 1988.

[42] T. Rosenbloom and A. Shahar, “Differences between taxi and nonprofessional male drivers in attitudes towards traffic-vi- olation penalties,” Transportation Research Part F: Traffic Psychology and Behaviour, vol. 10, no. 5, pp. 428–435, 2007.

[43] B. Keshavarz, R. Ramkhalawansingh, B. Haycock, S. Shahab, and J. L. Campos, “Comparing simulator sickness in younger and older adults during simulated driving under different multisensory conditions,” Transportation Research Part F: Traffic Psychology and Behaviour, vol. 54, pp. 47–62, 2018.

[44] B. Simons-Morton and J. Ehsani, “Learning to drive safely: reasonable expectations and future directions for the learner period,” Safety, vol. 2, no. 4, p. 20, 2016.

[45] J. B. Cicchino and A. T. McCartt, “Critical older driver errors in a national sample of serious U.S. crashes,” Accident Analysis & Prevention, vol. 80, pp. 211–219, 2015.

[46] X. Wang, Y. Xing, L. Luo, and R. Yu, “Evaluating the ef- fectiveness of Behavior-Based Safety education methods for commercial vehicle drivers,” Accident Analysis & Prevention, vol. 117, pp. 114–120, 2018.

[47] F. Cunto and F. F. Saccomanno, “Calibration and validation of simulated vehicle safety performance at signalized intersec- tions,” Accident Analysis & Prevention, vol. 40, no. 3, pp. 1171–1179, 2008.

[48] A. F. Williams, W. A. Leaf, B. G. Simons-Morton, and J. L. Hartos, “Vehicles driven by teenagers in their first year of licensure,” Traffic Injury Prevention, vol. 7, no. 1, pp. 23–30, 2006.

12 Journal of Advanced Transportation

Copyright of Journal of Advanced Transportation is the property of Hindawi Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.