Yellow light decision based on driving style: Day or night?

Drivers' driving decisions at yellow lights are an important cause of accidents at intersections. As proved by existing studies, driving style is an important basis for a driver to decide to pass a yellow light or not. This study, therefore, aims to investigate the effects of different driving styles on driving decisions at yellow lights under different lighting conditions. Specifically, 64 licensed drivers were recruited to comparative study the effects of different driving styles on the decision to pass through yellow lights under both daytime and nighttime lighting conditions using a driving simulator and a VR device. The results showed that maladjusted drivers more likely to pass the yellow light faster than adapted drivers (81.25% vs 43.75%) during both day and night. Male drivers had higher overall driving style scores than female drivers, and male drivers were faster and more likely to pass a yellow light than female drivers (56.25% vs 31.25%). This study also found that inexperienced drivers were faster and more likely to pass a yellow light than experienced drivers (50% vs 37.5%). Overall, maladjusted drivers are more likely to pass yellow lights, which can be improved and society properties by enhancing driving learning for maladjusted drivers.

Introduction

Nowadays, road traffic safety has become a major public concern of people safety. Because 56% of fatal road traffic accidents are usually caused by unsafe driving behaviors, which is often associated with aggressive driving style [1]. Due to the close relationship with road safety, driving style deserves deep investigation [2].

Driving style has an important influence on driver decision making. Driving style refers to a driver’s habits in terms of speed selection, following distance, tendency to overtake other vehicles and violation of traffic rules [3]. It plays an important role in predicting driving behaviors and reflecting the driver’s internal state [4]. Thus, it is meaningful to explore driving style. Today, there are many instruments that can be used to measure driving style, but in this study, we chose to use the Multidimensional Driving Style Inventory (MDSI). Because the MDSI has been shown to be a valid and reliable indicator for assessing driver styles [5-8]. Generally, MDSI can be divided into four driving styles through eight factors: danger, anger, anxiety, and caution [8]. In detail, dangerous driving style is the one in which the driver deliberately violates driving regulations and is driving in pursuit of excitement, speed, or illegal overtaking; anxious driving is driving in a manner where the driver develops feelings and emotions of alertness and nervousness, accompanied by distracting behaviors; an angry driving style is the one in which the driver displays irritating, angry, and hostile attitudes and behaviors; and cautious driving style refers to safe and cautious driving behaviors [8].

The four driving styles are divided into two main categories: maladaptive driving and adaptive driving styles. Maladaptive driving styles include dangerous, anxious, and angry driving styles, while cautious driving style are adaptive driving styles [8, 9]. Drivers with different driving styles make different decisions at signal crossings. A maladjusted driver usually exhibits aggressive driving. It shows that they decided to cross the intersection and try to run the red light even though they were away from the stop line. Adaptive drivers, on the other hand, usually drive conservatively. This means that they are close to the stop line and decide to stop, even though they can safely cross the intersection [10, 11]. Although there exist many studies on driving style, there are fewer studies on drivers’ driving style in different lighting conditions.

Additionally, light conditions also have an important influence on driver decisions. Firstly, drivers have different hazard perceptions under different lighting conditions. In the night scenario, the driver’s hazard perception sensitivity index all but drops, and even a complete lack of awareness of the hazard occurs, which leads to an increased rate of vehicle crashes and a significant increase in the severity of injuries that do not occur in the daytime scenario [12-14]. This may be related to the psychological needs of the driver. This is because the psychological needs of drivers are higher in night scenes than in day scenes [12]. Not only that, but the psychological needs of drivers differed between driving styles as well. Therefore, it is relevant to study the influence of lighting conditions on driving style. Although there have been many studies on driving performance under different lighting conditions, such as distracted driving [15, 16], sleep driving [17, 18], driving risk [19, 20], visibility [21, 22], and visual attention [21], there is a gap in the research on the driving decisions of drivers with different driving styles under different lighting conditions.

Lastly, traffic signals at intersections have the same impact on drivers’ driving decisions as stop signs do. Intersections are high-risk locations where traffic accidents occur frequently. As reported, approximately 30% of road accidents in China occur at intersections [4]. In particular, the decisions made by drivers at the start of yellow traffic lights are often critical, as inaccurate decisions win led to traffic conflicts and collisions [23]. Depending on the distance of the vehicle from the stop line, the driver is faced with two different scenarios. In the first case, the driver is in the indecision zone, while in the second case the driver is in the possible stopping zone [24]. At the onset of the yellow light signal, drivers need to decide immediately whether to stop or cross the intersection [11, 25]. However, immediate decision making by drivers is a challenge, especially in yellow light zones where drivers can neither stop safely nor cross the intersection [26]. Therefore, it is relevant to study drivers’ decision making at yellow signals. Although there are many studies on signalized intersections, there is still a research gap on the decision-making behaviors of drivers with differing driving styles under different lighting conditions at yellow lights.

Early research on driver decision-making at yellow lights has focused on modelling the propensity of drivers to run yellow lights as a function of constructive driving speed, distance from the stop line, and demographic variables such as driver age and gender [11, 24, 25]. Although early studies on yellow light decision making had significant findings, there were some shortcomings that could not be addressed [10]. Today, with the advancement of technology, many scholars study driving decisions at yellow lights through improved models or functions that better reduce the likelihood of traffic accident development. For example, the inclusion of a connected environment at signalized intersections helps drivers to make safer decisions at the onset of yellow lights [10]. In addition, distracted driving models in the form of mobile phone conversations have been constructed to investigate the impact of distracted driving on yellow light decisions [24]. While these and other related studies have confirmed the importance of drivers’ yellow light decisions, it is unclear how the affective information provided by driving style affects drivers’ decisions at the signal under different lighting conditions. This research gap prompted the present study.

Driving simulators plays an important role in assessing the human factors of road safety [27-29]. However, there are still limitations to the fidelity of driving simulators [21, 30]. Therefore, immersive virtual environments (IVEs) based on virtual reality (VR) technology offers an alternative approach to the study of driving behavior. Virtual reality is “a real or simulated environment in which the perceiver experiences a sense of remoteness” [31]. The behavior observed in a virtual reality environment is qualitatively comparable to that of the real world [32].

Today, IVE is an effective research tool with reasonable ecological validity to evoke realistic human driving behavior. It can control and manipulate key variables according to an experimental design and collect reliable qualitative and quantitative behavioral data [21, 33]. In this paper, we adopt a driving simulator and an IVE approach to address two questions.

In this study: Firstly, what driving decisions do drivers with different driving styles make when the traffic signal is yellow? Secondly, what driving decisions do drivers with differing driving styles make under different lighting conditions when the traffic lights are yellow?

Materials and methods

Participants

In this paper, 64 participants aged 19–23 years (mean age, 20.4 years (SD: 1.57 years)) were recruited for this experiment. Of these, 32 were male and 32 were female, randomly divided into four groups of 16 participants each, with equal proportions of males and females. They as have a valid driver’s license with at least one year of driving experience and at least 10,000 km of driving history per year. Participants had normal or corrected to normal vision and hearing. Participants will need to sign a paper consent form before the experiment begins, complete a pre-driving questionnaire that includes questions related to demographics, driving history, and driving behavior, and perform a familiarization drive that includes interaction with traffic signal changes. Participants in the experiment were asked to obey the speed limits on the signs and drive as close to the speed limit as possible. Participants in this study were evaluated for motion sickness using an adaptation of a standard motion sickness assessment tool [34], and the NASATLX questionnaire was administered after each drive. Upon completion of the experiment, each participant was paid 50 RMB. The study was approved by the Ethics Committee of Sichuan Normal University. All experimental procedures were carried out in accordance with the Declaration of Helsinki.

Design. A 2 × 2 between-group design focused on comparing driving styles (adaptive and non-adaptive) across test variables (half of the adaptive and non-adaptive drivers were assigned to the daytime scenario and the other half to the nighttime scenario) with respect to driving decisions at yellow lights at intersections.

Ethical approval and consent to participate

The Sichuan Normal University Committee approved the study protocol in accordance with the International Declaration of Helsinki. Oral consent was obtained from study participants and participation was entirely based on their willingness. Participants were also informed about the purpose of the study and its procedures. Moreover, participants were informed about their right to withdraw from the study.

Experimental equipment

Driving simulator

The experiment was conducted using the driving simulators. Which consists of a fully functional real car and is equipped with three projectors capable of displaying 180-degree scenes. The simulator can simulate realistic driving characteristics such as acceleration, deceleration, braking, and cornering. In addition, according to Chinese road standards the driving simulator car is designed to meet the needs of the study, traffic signs and road markings. Traffic signals are distinctly placed at an intersection on an urban route. Before meeting the traffic signal, drivers drove in a simulated urban environment to familiarize themselves with the environment. When approaching an intersection with a signal, the driver must respond to the change in the traffic signal from green to yellow. The driver interacts with the traffic signal in such a way that the traffic light changes from green to yellow when the driver is 5 seconds away from the stop line [10]. Since the yellow interval between the red and green lights was set at 3 seconds (see Fig 1), it meant that participants had 2 seconds to read and interpret the information [10].

Fig 1

Experimental scenario.

MDSI

Based on statements of feelings, thoughts, and behaviors while driving, drivers complete a Likert scale. The scale has a total score of 6, ranging from 1 (not at all) to 6 (very much). As the Cronbach’s alpha was reasonable for the four dimensions (0.82 for dangerous driving, 0.82 for anxious driving, 0.77 for angry driving, and 0.70 for cautious driving), each driver’s responses to the relevant scales were averaged to produce scores of each of the four driving styles, with higher scores indicating higher levels of that style.

Driver demographics

Driving gender and driving experience have a significant impact on driving behaviors. Drivers of different genders exhibit different driving behaviors. Male drivers tend to drive faster and are more prone to reckless driving, while female drivers tend to choose to drive more cautiously. Driving experience also has a varying degree of influence on driving behaviors. Experienced drivers tend to choose more dangerous driving styles, while inexperienced drivers tend to be overly cautious and cause anxiety. This paper therefore investigates the differences between the driving styles of drivers in different light conditions by selecting driving gender and driving experience as demographic variables to fill in the gaps in the research. To prevent confounding of demographic variables by driving style and light conditions, drivers of the same driving gender and number of driving experiences were selected for the study.

Experimental procedure

The experiment recruited 64 subjects and each subject was given two opportunities to experiment. The flow of the experiment was given as follows (as shown in Fig 2).

Fig 2

Experimental procedures.

Participants arrived at the laboratory to sign an informed consent form for the experiment and fill out relevant personal information (age, gender, driving experience, driving history, and questions related to driving behavior) and perform a familiarization drive that included interaction with traffic signal changes.

The drivers were differentiated by using the MDSI questionnaire, and subjects were divided into four groups: daytime non-adapted, daytime adapted, nighttime adapted and nighttime non-adapted. And subjects were assigned with individual tasks. Firstly, training in the operation of the driving simulator was carried out; secondly, after the realism of the driving experience in the simulator [35], the road environment was simulated as naturally and as detailed as possible. The scenario was consistent between the driving simulator and the VR system. Which training was completed a formal driving simulation was carried out by using the driving simulator.

After informing, subjects were placed on the driving simulator for training and performed a familiarization drive, including interactions with traffic signal changes, and 15 minutes later, a formal driving simulation.

Before starting the experimental driving, participants received the following instructions: “Drive as usual until you pass the intersection. You have the option to withdraw from the experiment at any time for any reason.” For the formal driving, the average participant was required to react to the traffic light ahead changing from green to yellow in a daytime scenario. The other half of the participants performed the same experiment in a nighttime scenario.

After the simulated driving is completed, an equal number of participants will be randomly selected from each of the 4 groups to be certified for the VR experiment.

After completing the test, each participant was asked to answer a post-test questionnaire. The questionnaire included feedback and thoughts on the driving experience and the driving simulator itself, as well as an assessment of the effect of the tool on the participant’s motion sickness using the NASATLX questionnaire.

Data processing

Driver decisions were derived from driving simulator data. The explanatory variables are divided into traffic operation variables, driver demographics, and driving conditions.

Traffic operation variables include driving speed, distance to the stop line at the start of the yellow light, acceleration noise (or change) before the start of the yellow light. Driver demographics include age, gender, driving experience, license type, and education. Driving condition variables had two categories: daytime and nighttime. The study used analysis of variance to test the experimental hypothesis. Namely, that driving style is significantly associated with yellow light driving decisions. Illumination conditions influence driving decisions on driving styles.

Sixty-four participants drove twice, resulting in 128 decisions at the start of the yellow interval at signalized intersections. Each driver interacted with the traffic signal twice in repeated driving, resulting in a data set. Summary statistics for the explanatory and response variables (Table 1). Of the 128 encounters with the yellow light, 18 drivers decided to cross the intersection in the daytime scenario, and 38 drivers crossed the intersection in the nighttime scenario. The average driving speeds for the daytime and nighttime light conditions were 11.97m/s (SD:2.26m/s) and 10.69m/s (SD:2.16m/s), respectively. Acceleration noise, as the standard deviation of roadway acceleration before the onset of yellow light, is considered an indicator of reckless driving [36], with values of 0.38 m/s2(SD (0.04) m/s2) and 0.37 m/s2(SD (0.04) m/s2) for daytime and nighttime ambient driving conditions, respectively.

Table 1

Summary statistics of operation and response data for each driving scenario.

variable	Variable Description	Average(SD)	Count(percentage)
		Day Night	Day Night
Driving conditions
Day	Driving in daytime scenarios	- -	32 100
Night	Driving at night scenario	- -	32 100
Traffic operation variables speed	Speed of driver at start of yellow light(m/s)	11.97(2.26) 10.69(2.16)	- -
Acceleration noise (or variation)	Standard deviation of driver’s acceleration and deceleration before the start of the red light(m/s⌃2)	0.38(0.04) 0.37(0.04)	- -
length	Distance from the stop line at the start of the yellow light	37.66(4.29) 36.75(4.20)	- -
Response Variables decision-making	The driver decided to proceed through a yellow light		10(47.75) 18

Results

Decision trees

A decision tree is constructed from the available data to classify the discrete outcomes of drivers’ stopping or passing at the start of a yellow indication at a signalized intersection. The dependent variable is the binary outcome in the decision tree (i.e., the driver’s decision to stop at a traffic signal intersection), and the input variables are driving conditions, driver’s gender, traffic operation variables, and driver demographics provided as possible explanatory variables in the decision tree. Fig 3 shows the decision tree diagram for the stop/pass decision model. Nodes are identified by selecting the option that provides the highest information gain. The decision tree classifies driver decisions by dividing the data into 52 smaller homogeneous groups, and their corresponding statistics are given within each node. The total number of nodes reached (N) and the number of stops or passes at the intersection for that node are presented in the Fig 3. For example, terminal node 3 indicates that about 12.5% of drivers choose to pass the yellow light at a speed of 11.53 m/s and an acceleration of 0.37 m/s for 2 the adapted drivers. Similarly, terminal node 15 implies that drivers with a distance greater than 38m are likely to stop in the daytime scenario. In terminal node 19, 57.14% of male drivers with an acceleration noise of 0.37 m/s2 and a speed greater than 10.44 m/s2 choose to pass the yellow light in the night scenario.

Fig 3

Decision trees.

Schematic diagram of the decision tree for the stop/pass decision model. Note that the numbers in the circles denote interaction terms; distance, AN, and speed denote the distance to the stop line at the start of the yellow light, acceleration noise (m/s2), respectively.

Driving style

In this paper, driving style is scored by the MDSI driving questionnaire, and the results of the MDSI questionnaire are divided according to gender. This paper examines the differences between driving style and gender by means of t-tests. The mean and SD scores are shown in Table 2. The results show that there is a significant difference between driving styles and gender. This result shows that adaptive driving style scores are overall higher than non-adaptive driving scores, and males score higher than females. Also, the results showed that males scored higher than females on anxious and cautious driving styles. Females scored higher than males on dangerous and angry driving styles.

Table 2

Driving style score according to gender.

Driving style	MDSI factors	Men	Women
Non-adaptive	Dissociative and Anxious
	Mean	2.31	2.23
	S.D.	0.77	0.25
	Risky and Angry
	Mean	2.04	2.05
	S.D.	0.77	0.74
	High-velocity and Distress reduction
	Mean	2.67	2.43
	S.D.	1.01	0.89
Adaptive	Patient and Careful
	Mean	4.65	4.57
	S.D.	0.78	0.69

Different light conditions

This paper conducts a 2 (driving styles: maladaptive, adaptive) X 2 (lighting conditions: day, night) simulation experiment on drivers’ driving decisions. An analysis of variance (ANOVA) was conducted on the results collected from the experiments. The results showed that light conditions had a significant effect on driving speed (F (1,62) = 10.33, p<0.05). Drivers in the daytime scenario were slower than those in the nighttime scenario (11.97m/s vs 10.69m/s). Light conditions also had a significant effect on acceleration noise (F (1,62) = 0.18, p<0.05) and distance (F (1,62) = 0.67, p<0.05). Driver acceleration noise was lower in the daytime scenario than in the nighttime scenario (0.374 vs 0.375) and driver distance from the yellow light was greater in the daytime scenario than in the nighttime scenario (37.66 vs 36.75). The results also revealed that the drivers in the night scenario had a higher probability of proceeding through a yellow light than the drivers in the day scenario (59.375% vs 28.125%).

Discussion

Driving decisions under different light conditions

Driver behavior at signalized intersections is considered critical because of its direct impact on traffic safety [37]. And different light conditions may lead to deviations in driving behavior. Such deviations can greatly increase the incidence of traffic accidents. For this reason, VR simulations of lighting scenarios provide realistic and important information for research and are expected to reduce the incidence of deviations.

Fig 4 shows that the probability of a driver choosing to pass a yellow light is lower in the daytime situation than in the nighttime situation (31.25% vs 56.25%). This increased probability can be explained by the fact that the driving behavior of the sampled drivers in the nighttime scenario may lead to an increased risk. This result can be attributed to the fact that drivers have different visibility of the road under different lighting conditions [38]. In low visibility conditions, accidents are more likely to occur due to difficulties in recognizing conditions on the road, especially in encounters with pedestrians [39, 40]. Conversely, in high visibility conditions, drivers can make the right decision in time to avoid an accident when encountering road conditions.

Fig 4

Probability of driver passing yellow light under interaction.

Fig 5 shows that different lighting conditions have a significant effect on driving speed (F (1,62) = 10.33, p<0.05). The driver’s speed in the daytime scenario was lower than the driver’s speed in the nighttime scenario. This result can be explained by the fact that drivers are more likely to control their emotions in the daytime scenario and tend to choose to follow driving safety and traffic regulations, so they choose to reduce their speed when encountering yellow lights. On contrasted the night scenario, drivers tend to disregard driving safety norms and perceive high speeds as safer due to changes in the environment and visibility, so they choose to overtake at high speeds in the yellow light scenario. Fig 5 also shows that different lighting conditions have a significant effect on driver driving acceleration (F (1,62) = 0.18, p<0.05). Driver driving acceleration at night was greater than the driver acceleration during the day, as different light conditions determined driver speed selection. In particular, the difference between the acceleration chosen by maladjusted drivers at night and during the day is greater, which may be related to changes in driver mood.

Fig 5

Speed and acceleration of driver through yellow light under interaction.

As can be seen in Fig 6, lighting conditions had a significant effect on driving distance (F (1,62) = 0.67, p<0.05). The distance between the driver and the stop line in the night scene is smaller than that in the daytime scene. This result can be explained by the fact that the driver’s distance from the stop line was negatively related to the driver’s driving decision to pass the yellow light. The shorter the distance between the driver and the stop line, the more likely the driver is to make the decision to pass the yellow light. In the nighttime environment, drivers tend to increase their speed, so the shorter the distance to the stop line, the higher the probability of passing the yellow light.

Fig 6

Distance(a).

Overall, the findings suggest that drivers tend to choose higher speeds to pass the yellow light in the night scenario than in the day scenario, while the distance to the stop line is shorter and acceleration is higher.

Driving decisions under different driving styles

The results of the study show that driving style has a significant influence on the driver’s driving decision to choose to pass a yellow light. This result suggests that driving style has an important basis for drivers to make driving decisions. Driving style predicts driver behavior on the road, which in turn reduces the incidence of traffic accidents.

From Fig 4, it can be concluded that non-adaptive drivers are more likely to choose to pass a yellow light than adaptive drivers (59.375% vs 28.125%). This can be attributed to the fact that non-adaptive drivers are more likely to make dangerous decisions and tend to violate safe driving norms by passing yellow lights quickly. Adaptive drivers are more likely to make rational decisions due to their emotional stability and tend to follow safe driving guidelines by stopping and waiting at the stop line. This result further emphasizes the importance of driving style in predicting driving behavior.

In the literature, drivers’ speed is repeatedly cited as a contributing factor in their decision to start at a yellow light. In general, drivers usually choose a faster speed to run a yellow light. To test whether our data would yield factually correct results, an ANOVA was chosen for testing. The results showed that driving style had a significant effect on drivers’ driving speeds (F (1,62) = 6.83, p<0.05). Drivers with different driving styles chose different driving speeds. The results also suggest that when non-adaptive drivers drive at higher speeds, they tend to choose a more dangerous route (frequent lane changes) to get through yellow lights. This result can be explained by the fact that non-adaptive drivers tend to perceive themselves as skilled drivers and that higher speeds produce a sense of excitement, which makes non-adaptive drivers feel happy emotions. In contrast, adaptive drivers are ruled by caution and prudence when driving and tend to drive within the prescribed speed limit. As a result, non-adaptive drivers had significantly higher speeds than adaptive drivers (11.53 m/s vs 10.13 m/s). Similarly, as acceleration noise increased (F = 0.27, p<0.05) and distance decreased (F = 0.25, p<0.05), the probability of passing a yellow light was significantly higher for non-adaptive drivers compared to adaptive drivers. A similar explanation can be made for the relationship with the probability of passing a yellow light, i.e., the driver’s driving style had a significant effect on the change in acceleration, with a positive correlation (F = 0.27, p<0.05). Therefore, to reduce the probability of yellow light jumping by maladjusted drivers, relevant driving regulations should be developed to regulate the behavior of this group of drivers and reduce the probability of traffic accidents.

The impact of interactions on driving decisions

The presence of interaction effects suggests a degree of complexity between driver decision-making at yellow lights and data on speed, distance to the stop line, acceleration, lighting conditions, and driving style. These relationships are further complicated by the effect of driving style on drivers’ speed choices at signalized intersections. To test for differential risk, the probability of a driver passing a yellow light was plotted as shown in Fig 4. The results show that the interaction between driving style and lighting conditions had a significant effect (p<0.05) on the driver’s driving decision to choose to pass the yellow light. In daytime conditions, maladjusted drivers were more likely to choose to pass the yellow light than adapted drivers (43.75% vs 12.5%) and in nighttime conditions (81.25% vs 43.75%).

The results also suggest that the interaction between lighting conditions and driving style has a negative impact on driving decisions. The effects of different lighting conditions were different for drivers with different driving styles. Firstly, for non-adaptive drivers, adequate lighting mitigated the behavior of passing yellow lights and reduced the likelihood of injury or death (43.75% vs 81.25%). Secondly, for adaptive drivers, the effect of light conditions on passing yellow lights was significant (F (1, 31) = 0.04, p<0.05), indicating that drivers were more likely to choose to pass yellow lights in nighttime conditions (12.5% vs 32.5%).

The impact of driver demographics on the probability of running yellow lights (Table 3)

Driver’s gender

Fig 7 shows the probability of passing the yellow light among drivers. The probability of passing a yellow light increases with speed for all drivers in both conditions, and males are shorter from the stop line than females (Fig 8). The results demonstrate that the probability of passing a yellow light is higher in night driving conditions and that males have a better chance of passing a yellow light than females. Because the distance is shorter, and the probability of passing is greater. For example, the probability of running a yellow light for male drivers in daytime driving conditions is 37.5%, while the probability of passing a yellow light for male drivers in nighttime conditions is 75%, which indicates that the probability of passing a yellow light is reduced by half. This reduction in probability can be attributed to the higher driver visibility in daytime scenarios [38], which is in line with studies that found that women are more hesitant than men to run red lights when drivers decide to do so [41].

Fig 7

Effect of driving demographics on the probability of passing a yellow light.

Fig 8

Distance(b).

The study found that the night environment provides better environmental conditions for male drivers to pass compared to female drivers. It is because males have more risk-taking behaviors than females and are more at risk of car accidents and are more likely to violate traffic rules [42]. For example, compared to the daytime scenario, the probability of passing for female drivers is about 18.75%, while the probability of passing for male drivers is about 37.5%, which means that male drivers seem to prefer to pass the yellow light, while female drivers prefer to wait at the stop line at night [37]. This is because males are more likely to make dangerous decisions than females and believe that they are less likely to be in a car accident. Females, on the other hand, are conservative and adhere to safe driving practices. And Fig 9 indicates that there is a gender difference in driving speed, with male drivers driving at a higher speed than female drivers. This result also confirms the influence of light conditions and driving style on driving decisions. That is, daytime adaptive drivers tend to drive at lower speeds and avoid running yellow lights.

Fig 9

Effect of demographics on speed.

Driver experience

Figs 7–9 show the probability of passing a yellow light for all driving experience groups. It can be observed that in both cases, the probability of passing the yellow light increases with increasing speed for all drivers, while in the night scenario driving conditions, the probability of passing the yellow light is higher for experienced drivers and the driving distance from the stop line is also greater than for inexperienced drivers. This is because they tend to cross the intersection by increasing their speed at the start of the yellow light. However, as found in this study, the occurrence of this risky behavior can be reduced by predictive information on driving style.

Inexperienced drivers appeared to have a lower propensity to pass through yellow lights. For example, the probability of passing the yellow light for inexperienced drivers in the daytime scenario condition is 31.25%, while that for experienced drivers in the same speed condition is 25%, which indicates a decrease in the probability of passing the yellow light. This decrease in probability can be attributed to the slower driving speed of inexperienced drivers in the daytime scenario. Inexperienced drivers are normally considered dangerous because they tend to cross the intersection at the beginning of the yellow light by increasing their speed or running the red-light violation, while drivers with more driving experience have more experience in processing information, deciding, and taking safe actions to avoid potentially dangerous events. Whereas, in the night scenario, the probability of passing the yellow light for inexperienced drivers is 50%, while that for experienced drivers under the same speed condition is 68.75%, which indicates an increase in the probability of passing the yellow light. This increase in probability can be attributed to the fact that experienced drivers choose to drive faster in the nighttime environment. More specifically, experienced drivers are more likely to choose to drive at high speeds compared to the speed of the daytime scenario. Because experienced drivers believe that they are proficient in driving operations, have good road perception, can make correct decisions, and trust their skills, no dangerous accidents will occur.

Conclusions and limitations

In this paper, we investigate the influence of a driver’s driving style on driving decisions at intersections with yellow lights by comparing different lighting conditions. Based on the results obtained, some remarks can be listed as follows:

The probability of a driver deciding to wait at the stop line in the daytime scenario is greater than the probability of waiting in the nighttime scenario. (71.875% greater than 40.625%)

Non-adaptive drivers are more likely to choose to pass a yellow light rather than to wait at the stop line. (62.5%)

During the day, adaptive drivers tended to wait at the stop line, while non-adaptive drivers tended to pass the yellow light; during the night, non-adaptive drivers had a much higher probability of passing the yellow light than during the day, and adaptive drivers had a much higher probability of passing the yellow light.

Male drivers had higher overall driving style scores than female drivers, and male drivers were more likely to pass a yellow light than female drivers with higher driving speeds, higher acceleration, and smaller distances (56.25% vs 31.25%).

Drivers with a lack of driving experience were more likely to pass a yellow light than experienced drivers with higher driving speeds, higher acceleration, and smaller distances (50% vs 37.5%).

The purpose of this study is to make recommendations by comparing daytime and nighttime drivers’ decisions to pass the yellow light. Since the probability of passing a yellow light at night is significantly higher than that during the day, it is recommended that:

Add education on traffic lights to the driving test, especially the 3-second yellow light, so that the driver do not jump the yellow light.

It is possible to rationalize the length of traffic signals during the day and at night, as well as to increase the duration of traffic signals at night as needed.

For maladjusted drivers, they can learn about traffic signals and watch videos of accidents related to yellow light jumping online, then they can realize the seriousness of the incident and achieve a reduction in yellow light jumping; for adapted drivers, they can learn to prevent yellow light jumping through videos.

Although this paper investigates the driving decisions of drivers with different driving styles under yellow lights based on different lighting conditions, there are same limitations:

This article is more general in its classification of driving styles. Under maladaptive driving styles, there are three different driving styles: dangerous, anxious, and anger driving styles. Whether these three driving styles are still used with the conclusions of this paper is worthy future research.

This paper only considers the yellow light decision in daytime and nighttime scenarios, and it is unclear whether it is used in other scenarios, especially in bad weather.

The yellow interval studied in this paper is 3 seconds, but a 3-second yellow interval is not sufficient [43]. Future studies can be conducted on different yellow light intervals and different intersection types.

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Page 1, Line 6, 8, 12 :driving styles------>driving style

Page 1, Line 7 : driving styles deserve---->driving style deserves

Page 1, Line 15 :divide---->divided

Page 2, Line 31, 32 : driving styles------>driving style

Page 3, Line 77 : play------> plays

Page 3, Line 101, 103 : included------> includes

Page 3, Line 111 : design------> design

Page 6, Line 186-187 : variables divided ------> variables are divided

Page 7, Line 225, 226, 228 :driving styles------>driving style

Page 7, Line 227: to the gender ------> to gender

Page 8, Line 239 : the drivers------>those

Page 8, Line 267 : therefore------>so they

Page 11, Line 384: as dangerous drivers ------> dangerous

Page 11, Line 395-396: and they trust their skills and believe that no dangerous accidents will occur.------> and trust their skills, no dangerous accidents will occur.

Page 11, Line 398, 410 :driving styles------>driving style

Page 12, Line 430 : the maladaptive driving styles ------> maladaptive driving styles

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Following the editor’s suggestion, we have included detailed information in the cover letter.

“This work was supported by Humanities and Social Sciences projects of the Ministry of Education(18YJA760020).The authors declare that the funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.”

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As suggested by the editor, we upload the relevant information about the data in the support information under the name "Data".

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Following the editor's advice, we registered an ORID account under the following name: maoyy85@163.com.

7.Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

We sincerely thank the editors for raising the issue of references. We have thoroughly proofread the entire manuscript, as detailed below:

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22. Michael D. Keall and William J. Frith and Tui L. Patterson. The contribution of alcohol to night time crash risk and other risks of night driving. Accident Analysis& Prevention. 2005;37(5):816-824.

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Reviewer #1:

The authors propose that driving style and lighting conditions have an impact on the driver's decision to drive at yellow lights. The study was conducted using a driving simulator and a VR device, which has led to new technological developments to address the driving aspect of the study. The study shows that maladjusted drivers are more likely to run yellow lights and point to relevant solutions that are relevant to solving the problem of yellow light running.

The paper is interesting and should be useful to some practitioners and researchers in the field. However, the main drawback is that the contribution of this work is not specific enough, examining only the two main categories of driving styles without studying them separately in detail.

I have the following suggestions for how the authors could improve the submission:

We sincerely thank this reviewer for his/her comments. All comments from this reviewer were carefully addressed in this revised manuscript. The revisions are explained in detail below.

Introduction:

1. Although the text is generally well written, there are errors in grammar and misspellings that make comprehension difficult. For example, on page 3 in the paragraph: “modelling- modeling”. For example, in this paper, driving styles or driving style? I strongly recommend that the author revise the text to address these errors prior to publication.

We sincerely thank this reviewer for raising grammatical issues. We have corrected these errors and have thoroughly proofread the entire manuscript, as detailed below:

Page 1, Line 6, 8, 12 :driving styles------>driving style

Page 1, Line 7 : driving styles deserve---->driving style deserves

Page 1, Line 15 :divide---->divided

Page 2, Line 31, 32 : driving styles------>driving style

Page 3, Line 77 : play------> plays

Page 3, Line 101, 103 : included------> includes

Page 3, Line 111 : design------> design

Page 6, Line 186-187 : variables divided ------> variables are divided

Page 7, Line 225, 226, 228 :driving styles------>driving style

Page 7, Line 227: to the gender ------> to gender

Page 8, Line 239 : the drivers------>those

Page 8, Line 267 : therefore------>so they

Page 11, Line 384: as dangerous drivers ------> dangerous

Page 11, Line 395-396: and they trust their skills and believe that no dangerous accidents will occur.------> and trust their skills, no dangerous accidents will occur.

Page 11, Line 398, 410 :driving styles------>driving style

Page 12, Line 430 : the maladaptive driving styles ------> maladaptive driving styles

2. The traffic running variables in the article include driving speed, distance to the stop line at the start of the yellow light, acceleration noise (or change) before the start of the yellow light,time, and minimum speed. However, the two variables, time, and minimum speed are not shown in the table and decision tree. Are these two variables necessary or unnecessary? If they are not necessary, please fix them, and if they are necessary, please add the relevant data information.

As suggested by the reviewer, we decided to remove the variables time and minimum speed because they were not considered in the study. (Page 8)

3. The ideas presented are already known, so probably you need to improve the discussion so you can add some novelty to this paper.

As suggested by the reviewer, we have improved the Discussion section to make it new and marketable (Page 8-11):

Driving decisions under different light conditions:

“Driver behavior at signalized intersections is considered critical because of its direct impact on traffic safety [36]. And different light conditions may lead to deviations in driving behavior. Such deviations can greatly increase the incidence of traffic accidents. For this reason, VR simulations of lighting scenarios provide realistic and important information for research and are expected to reduce the incidence of deviations.”

“Fig 6 shows that the probability of a driver choosing to pass a yellow light is lower in the daytime situation than in the nighttime situation (31.25% vs 56.25%). This increased probability can be explained by the fact that the driving behavior of the sampled drivers in the nighttime scenario may lead to an increased risk. This result can be attributed to the fact that drivers have different visibility of the road under different lighting conditions [7]. In low visibility conditions, accidents are more likely to occur due to difficulties in recognizing conditions on the road, especially in encounters with pedestrians [8,21]. Conversely, in high visibility conditions, drivers can make the right decision in time to avoid an accident when encountering road conditions.”

“Overall, the findings suggest that drivers tend to choose higher speeds to pass the yellow light in the night scenario than in the day scenario, while the distance to the stop line is shorter and acceleration is higher.”

Driving decisions under different driving styles:

“The results of the study show that driving style has a significant influence on the driver's driving decision to choose to pass a yellow light. This result suggests that driving style has an important basis for drivers to make driving decisions. Driving style predicts driver behavior on the road, which in turn reduces the incidence of traffic accidents.”

“From Fig 6, it can be concluded that non-adaptive drivers are more likely to choose to pass a yellow light than adaptive drivers (59.375% > 28.125%). This can be attributed to the fact that non-adaptive drivers are more likely to make dangerous decisions and tend to violate safe driving norms by passing yellow lights quickly. Adaptive drivers are more likely to make rational decisions due to their emotional stability and tend to follow safe driving guidelines by stopping and waiting at the stop line. This result further emphasizes the importance of driving style in predicting driving behavior.”

“In the literature, drivers' speed is repeatedly cited as a contributing factor in their decision to start at a yellow light. In general, drivers usually choose a faster speed to run a yellow light. To test whether our data would yield factually correct results, an ANOVA was chosen for testing. The results showed that driving style had a significant effect on drivers' driving speeds (F (1,62) = 6.83, p<0.05). Drivers with different driving styles chose different driving speeds. The results also suggest that when non-adaptive drivers drive at higher speeds, they tend to choose a more dangerous route (frequent lane changes) to get through yellow lights. This result can be explained by the fact that non- adaptive drivers tend to perceive themselves as skilled drivers and that higher speeds produce a sense of excitement, which makes non-adaptive drivers feel happy emotions. In contrast, adaptive drivers are ruled by caution and prudence when driving and tend to drive within the prescribed speed limit. As a result, non-adaptive drivers had significantly higher speeds than adaptive drivers (11.53 m/s > 10.13 m/s). Similarly, as acceleration noise increased (F = 0.27, p<0.05) and distance decreased (F = 0.25, p<0.05), the probability of passing a yellow light was significantly higher for non-adaptive drivers compared to adaptive drivers. A similar explanation can be made for the relationship with the probability of passing a yellow light, i.e., the driver's driving style had a significant effect on the change in acceleration, with a positive correlation (F = 0.27, p<0.05). Therefore, to reduce the probability of yellow light jumping by maladjusted drivers, relevant driving regulations should be developed to regulate the behavior of this group of drivers and reduce the probability of traffic accidents.”

The impact of interactions on driving decisions:

“The presence of interaction effects suggests a degree of complexity between driver decision-making at yellow lights and data on speed, distance to the stop line, acceleration, lighting conditions, and driving style. These relationships are further complicated by the effect of driving style on drivers' speed choices at signalized intersections. To test for differential risk, the probability of a driver passing a yellow light was plotted as shown in Figure 6. The results show that the interaction between driving style and lighting conditions had a significant effect (p<0.05) on the driver's driving decision to choose to pass the yellow light. In daytime conditions, maladjusted drivers were more likely to choose to pass the yellow light than adapted drivers (43.75% > 12.5%) and in nighttime conditions (81.25% > 43.75%).”

“The results also suggest that the interaction between lighting conditions and driving style has a negative impact on driving decisions. The effects of different lighting conditions were different for drivers with different driving styles. Firstly, for non- adaptive drivers, adequate lighting mitigated the behavior of passing yellow lights and reduced the likelihood of injury or death (43.75% < 81.25%). Secondly, for adaptive drivers, the effect of light conditions on passing yellow lights was significant (F (1, 31) =0.04, p<0.05), indicating that drivers were more likely to choose to pass yellow lights in nighttime conditions (12.5% < 32.5%).”

Driver’s Gender:

“The probability of passing a yellow light increases with speed for all drivers in both conditions, and males are shorter from the stop line than females.”

“And Fig 5 indicates that there is a gender difference in driving speed, with male drivers driving at a higher speed than female drivers. This result also confirms the influence of light conditions and driving style on driving decisions. That is, daytime adaptive drivers tend to drive at lower speeds and avoid running yellow lights.”

Driver experience:

“Fig 5, 7 and 9 show the probability of passing a yellow light for all driving experience groups. It can be observed that in both cases, the probability of passing the yellow light increases with increasing speed for all drivers, while in the night scenario driving conditions, the probability of passing the yellow light is higher for experienced drivers and the driving distance from the stop line is also greater than for inexperienced drivers. This is because they tend to cross the intersection by increasing their speed at the start of the yellow light. However, as found in this study, the occurrence of this risky behavior can be reduced by predictive information on driving style.”

“Inexperienced drivers appeared to have a lower propensity to pass through yellow lights."

4. The experimental design was demonstrated clearly in the paper. The authors tried to analyze the effects of different demographic information but is it confounded with other variables during group assignment?

We are grateful to the reviewers for pointing this out. We designed a 2 (driving style: adaptive, maladaptive) x 2 (light conditions: day, night) experiment by first surveying drivers for demographic-related information, then dividing drivers' driving styles by their scores on the MDSI questionnaire, and then placing the 2 divided groups of driving styles under different light conditions. We analyzed the different demographics in the context mentioned earlier and did not consider demographic information in the group assignment, thus avoiding confusion with it and variables that could invalidate the results of the analysis. More specifically, one of the aims of our study was to examine the effects of light conditions and driving style on the decision to drive at a yellow light. Demographic variables, however, are only subsidiary to this research purpose and are not the subject of the study. The demographic information considered in this paper is under the independent variable of driving style, and in order to prevent the influence of driving style and lighting conditions, the same number of drivers with the same amount of driving gender and driving experience were selected for the study so that no confounding would occur. For a more thorough explanation, in the experimental section we have added a sub-section on "Driving demographics" to explain in detail the choice of driving experience and driving gender as demographic variables. (Page 4):

“Driving demographics:

Driving gender and driving experience have a significant impact on driving behaviors. Drivers of different genders exhibit different driving behaviors. Male drivers tend to drive faster and are more prone to reckless driving, while female drivers tend to choose to drive more cautiously. Driving experience also has a varying degree of influence on driving behaviors. Experienced drivers tend to choose more dangerous driving styles, while inexperienced drivers tend to be overly cautious and cause anxiety. This paper therefore investigates the differences between the driving styles of drivers in different light conditions by selecting driving gender and driving experience as demographic variables to fill in the gaps in the research. To prevent confounding of demographic variables by driving style and light conditions, drivers of the same driving gender and number of driving experiences were selected for the study.”

5. The authors adopted ANOVA for statistical analysis but lack interpretation. Please add the relevant information in Data Processing.

We agree with the reviewer that an explanation of ANOVA is needed. We have therefore added the relevant details in the Data Processing section.

“The study used analysis of variance to test the experimental hypothesis. Namely, that driving style is significantly associated with yellow light driving decisions. Illumination conditions influence driving decisions across driving styles.” (Page 6)

“20. Jmw, A., Gi, B., Ab, A., & Ic, C. Night-time driving visibility associated with led streetlight dimming - sciencedirect. Accident Analysis & Prevention. 2018; 121: 295-300.”

“34. Panagiotis Papaioannou. Driver behaviour, dilemma zone and safety effects at urban signalised intersections in Greece. Accident Analysis & Prevention. 2007;39(1):147-158.”

6. How were the driving styles manipulated? Please add relevant information in the Experiments section.

We have added the MDSI subsection to the Experimental Equipment section, which focuses on explaining more details about manoeuvring driving styles. (Page 4):

“MDSI:

Based on statements of feelings, thoughts, and behaviors while driving, drivers complete a Likert scale. The scale has a total score of 6, ranging from 1 (not at all) to 6 (very much). As the Cronbach's alpha was reasonable for the four dimensions (0.82 for dangerous driving, 0.82 for anxious driving, 0.77 for angry driving, and 0.70 for cautious driving), each driver's responses to the relevant scales were averaged to produce scores of each of the four driving styles, with higher scores indicating higher levels of that style.”

Reviewer #2:

The authors use a driving simulator and a VR device to study the influence of driving style and lighting conditions on drivers' yellow light decisions. The results show that maladjusted drivers are more likely to run yellow lights. The results of this study are relevant to solving practical problems. The manuscript is well structured and readable. Below I point out some issues I consider the authors need to address for the manuscript to be accepted.

We sincerely thank this reviewer for his/her comments. All comments from this reviewer were carefully addressed in this revised manuscript. The revisions are explained in detail below.

1. There are grammatical errors. Please have a native speaker proofread the manuscript and correct the English grammar and exposition accordingly.

We sincerely thank this reviewer for raising grammatical issues. We have thoroughly proofread the entire manuscript, as detailed below:

Page 1, Line 6, 8, 12 :driving styles------>driving style

Page 1, Line 7 : driving styles deserve---->driving style deserves

Page 1, Line 15 :divide---->divided

Page 2, Line 31, 32 : driving styles------>driving style

Page 3, Line 77 : play------> plays

Page 3, Line 101, 103 : included------> includes

Page 3, Line 111 : design------> design

Page 6, Line 186-187 : variables divided ------> variables are divided

Page 7, Line 225, 226, 228 :driving styles------>driving style

Page 7, Line 227: to the gender ------> to gender

Page 8, Line 239 : the drivers------>those

Page 8, Line 267 : therefore------>so they

Page 11, Line 384: as dangerous drivers ------> dangerous

Page 11, Line 395-396: and they trust their skills and believe that no dangerous accidents will occur.------> and trust their skills, no dangerous accidents will occur.

Page 11, Line 398, 410 :driving styles------>driving style

Page 12, Line 430 : the maladaptive driving styles ------> maladaptive driving styles

2. The bar graphs in the article are not sufficiently aesthetically pleasing. The visibility and accuracy of the bar graphs are low, so please use professional software to create them.

As suggested by the reviewer, we redrew the relevant graphics to make them vectorially visible.:

specifically as the response to reviewers.

3. An explanation of the demographic variables and why driving experience and driving gender were chosen as demographic variables should be added. Without the explanation of the demographic variables, the significance of the variables is not clear. You can provide details in the Experiments or Results section.

As suggested by the reviewer, in the experimental section we have added a sub-section on "Driving demographics" to explain in detail the choice of driving experience and driving gender as demographic variables. (Page 4):

“Driving demographics:

Driving gender and driving experience have a significant impact on driving behaviors. Drivers of different genders exhibit different driving behaviors. Male drivers tend to drive faster and are more prone to reckless driving, while female drivers tend to choose to drive more cautiously. Driving experience also has a varying degree of influence on driving behaviors. Experienced drivers tend to choose more dangerous driving styles, while inexperienced drivers tend to be overly cautious and cause anxiety. This paper therefore investigates the differences between the driving styles of drivers in different light conditions by selecting driving gender and driving experience as demographic variables to fill in the gaps in the research. To prevent confounding of demographic variables by driving style and light conditions, drivers of the same driving gender and number of driving experiences were selected for the study.”

4. The introduction section is detailed, but the logic between sentences needs strengthening. A systematic and complete revision of the relationships between statements is recommended.

As suggested by the reviewer, we strengthen the logic of the statement in the introductory section (Pages 1-3):

Page 1, Paragraph 2: “Driving style has an important influence on driver decision making. Driving style refers to a driver's habits in terms of speed selection, following distance, tendency to overtake other vehicles and violation of traffic rules [18]. It plays an important role in predicting driving behaviors and reflecting the driver's internal state [43]. Thus, it is meaningful to explore driving style. Today, there are many instruments that can be used to measure driving style, but in this study, we chose to use the Multidimensional Driving Style Inventory (MDSI). Because the MDSI has been shown to be a valid and reliable indicator for assessing driver styles [19,30,41,42]. Generally, MDSI can be divided into four driving styles through eight factors: danger, anger, anxiety, and caution [42]. In detail, dangerous driving style is the one in which the driver deliberately violates driving regulations and is driving in pursuit of excitement, speed, or illegal overtaking; anxious driving is driving in a manner where the driver develops feelings and emotions of alertness and nervousness, accompanied by distracting behaviors; an angry driving style is the one in which the driver displays irritating, angry, and hostile attitudes and behaviors; and cautious driving style refers to safe and cautious driving behaviors [42].”

Page 2, Paragraph 4: “Additionally, light conditions also have an important influence on driver decisions. Firstly, drivers have different hazard perceptions under different lighting conditions. In the night scenario, the driver's hazard perception sensitivity index all but drops, and even a complete lack of awareness of the hazard occurs, which leads to an increased rate of vehicle crashes and a significant increase in the severity of injuries that do not occur in the daytime scenario [4,10,37]. This may be related to the psychological needs of the driver. This is because the psychological needs of drivers are higher in night scenes than in day scenes [4]. Not only that, but the psychological needs of drivers differed between driving styles as well. Therefore, it is relevant to study the influence of lighting conditions on driving style. Although there have been many studies on driving performance under different lighting conditions, such as distracted driving [11,44], sleep driving [6,45], driving risk [21,38], visibility [26,33], and visual attention [26], there is a gap in the research on the driving decisions of drivers with different driving styles under different lighting conditions.”

Page 2, Paragraph 5: “Although there are many studies on signalized intersections, there is still a research gap on the decision-making behaviors of drivers with differing driving styles under different lighting conditions at yellow lights.”

Page 2-3, Paragraph 6: “Early research on driver decision-making at yellow lights has focused on modelling the propensity of drivers to run yellow lights as a function of constructive driving speed, distance from the stop line, and demographic variables such as driver age and gender [10,16,36]. Although early studies on yellow light decision making had significant findings, there were some shortcomings that could not be addressed [1]. Today, with the advancement of technology, many scholars study driving decisions at yellow lights through improved models or functions that better reduce the likelihood of traffic accident development. For example, the inclusion of a connected environment at signalized intersections helps drivers to make safer decisions at the onset of yellow lights [1]. In addition, distracted driving models in the form of mobile phone conversations have been constructed to investigate the impact of distracted driving on yellow light decisions [15]. While these and other related studies have confirmed the importance of drivers' yellow light decisions, it is unclear how the affective information provided by driving style affects drivers' decisions at the signal under different lighting conditions. This research gap prompted the present study.”

Page 3, Paragraph 7: “Driving simulators plays an important role in assessing the human factors of road safety [17,27,29]. However, there are still limitations to the fidelity of driving simulators [25,26]. Therefore, immersive virtual environments (IVEs) based on virtual reality (VR) technology offers an alternative approach to the study of driving behavior. Virtual reality is "a real or simulated environment in which the perceiver experiences a sense of remoteness" [39]. The behavior observed in a virtual reality environment is qualitatively comparable to that of the real world [25].”

Page 3, Paragraph 8: “Today, IVE is an effective research tool with reasonable ecological validity to evoke realistic human driving behavior.”

Page 3, Paragraph 9: “Secondly, what driving decisions does drivers with differing driving styles make under different lighting conditions when the traffic lights are yellow?”

“10. Teal Evans and Rwth Stuckey and Wendy Macdonald. Young drivers’ perceptions of risk and difficulty: Day versus night. Accident Analysis & Prevention. 2020; 147:105753.”

“15. Md. Mazharul Haque and Amanda D. Ohlhauser and Simon Washington and Linda Ng Boyle. Decisions and actions of distracted drivers at the onset of yellow lights. Accident Analysis & Prevention. 2016; 96:290-299.”

“16. Qinaat Hussain and Wael K.M. Alhajyaseen and Kris Brijs and Ali Pirdavani and Tom Brijs. Innovative countermeasures for red light running prevention at signalized intersections: A driving simulator study. Accident Analysis & Prevention. 2020;134:105349.”

“17. Hussain, Q., Alhajyaseen, W., Pirdavani, A., Reinolsmann, N., Brijs, K., & Brijs, T. Speed perception and actual speed in a driving simulator and real-world: a validation study. Transportation Research Part F: Psychology and Behaviour. 2019; 62:637-650.”

“18.Motonori Ishibashi and Masayuki Okuwa and Shun’ichi Doi and Motoyuki Akamatsu. Indices for characterizing driving style and their relevance to car following behavior. SICE Annual Conference. 2007:1132-1137.”

“19. Jose-Luis Padilla and Candida Castro and Pablo Doncel and Orit Taubman - Ben-Ari. Adaptation of the multidimensional driving styles inventory for Spanish drivers: Convergent and predictive validity evidence for detecting safe and unsafe driving styles. Accident Analysis & Prevention. 2020; 136:105413.”

“21. Johnell O. Brooks and Richard R. Goodenough and Matthew C. Crisler and Nathan D. Klein and Rebecca L. Alley and Beatrice L. Koon and William C. Logan and Jennifer H. Ogle and Richard A. Tyrrell and Rebekkah F. Wills. Simulator sickness during driving simulation studies. Accident Analysis & Prevention. 2010;42(3):788-796.”

“22. Keall, M. D., Frith, W. J., & Patterson, T. L. The contribution of alcohol to night time crash risk and other risks of night driving. Accident Analysis & Prevention. 2005; 37(5): 816-824.”

“23. Kemeny, A., & Panerai, F. Evaluating perception in driving simulation experiments. Trends in Cognitive Sciences. 2003; 7(1): 31-37.”

“25.Kinateder, Max and Ronchi, Enrico and Nilsson, Daniel and Kobes, Margrethe and Müller, Mathias and Pauli, Paul and Mühlberger, Andreas. Virtual Reality for Fire Evacuation Research. Federated Conference on Computer Science and Information Systems. 2014:313-321.”

“26. Konstantopoulos, P., Chapman, P., & Crun Da Ll, D. Driver's visual attention as a function of driving experience and visibility. using a driving simulator to explore drivers' eye movements in day, night and rain driving. Accident Analysis & Prevention. 2010; 42(3): 827-834.”

“27.Lee, & John. Handbook of driving simulation for engineering, medicine, and psychology. Handbook of driving simulation for engineering, medicine, and psychology. 2011.”

“29. Llopis-Castello, D., Camacho-Torregrosa, F. J., Marin-Morales, J., Perez-Zuriaga, A. M., Garcia, A., & Dols, J. F. Validation of a low-cost driving simulator based on continuous speed profiles. Transportation Research Record: Journal of the Transportation Research Board. 2016; 2602(2026): 104-114.”

“30. Long Sun & Ruosong Chang. Reliability and validity of the Multidimensional Driving Style Inventory in Chinese drivers. Traffic injury prevention. 2019; 20(2): 152–157.”

“33. Mikoski, P., Zlupko, G., & Owens, D. A. Drivers' assessments of the risks of distraction, poor visibility at night, and safety-related behaviors of themselves and other drivers. Transportation Research Part F: Traffic Psychology and Behaviour. 2019; 62: 416-434.”

“34. Papaioannou, P. Driver behaviour, dilemma zone and safety effects at urban signalised intersections in greece. Accident Analysis & Prevention. 2007; 39(1): 147-158.”

“36. Praveena Penmetsa and Srinivas S. Pulugurtha. Risk drivers pose to themselves and other drivers by violating traffic rules. Traffic Injury Prevention. 2017;18(1):63-69.”

“37. Qiong Wu and Feng Chen and Guohui Zhang and Xiaoyue Cathy Liu and Hua Wang and Susan M. Bogus. Mixed logit model-based driver injury severity investigations in single- and multi-vehicle crashes on rural two-lane highways. Accident Analysis & Prevention. 2014; 72:105-115.”

“38. Anna-Maria Sourelli and Ruth Welsh and Pete Thomas. Objective and perceived risk in overtaking: The impact of driving context. Transportation Research Part F: Traffic Psychology and Behaviour. 2021; 81:190-200.” “39. Steuer, J. Defining virtual reality: dimensions determining telepresence. Journal of Communication. 2010;42(4):73-93.”

“41. Orit Taubman - Ben-Ari and Vera Skvirsky. The multidimensional driving style inventory a decade later: Review of the literature and re-evaluation of the scale. Accident Analysis & Prevention. 2016; 93:179-188.”

“42. Orit Taubman-Ben-Ari and Mario Mikulincer and Omri Gillath. The multidimensional driving style inventory—scale construct and validation. Accident Analysis & Prevention. 2004;36(3):323-332.”

“43. Xinmiao, Fan, Gaofeng, Pan, Yan, & Mao. Investigating the effect of personality on left-turn behaviors in various scenarios to understand the dynamics of driving styles. Traffic injury prevention. 2019;20(8):801-806.”

“44. Tamer Yared and Patrick Patterson. The impact of navigation system display size and environmental illumination on young driver mental workload. Transportation Research Part F: Traffic Psychology and Behaviour. 2020; 74:330-344.”

“45. Rainer Zeller and Ann Williamson and Rena Friswell. The effect of sleep-need and time-on-task on driver fatigue. Transportation Research Part F: Traffic Psychology and Behaviour. 2020; 74:15-29.”

5. There are problems with the format of the references. For example, “Lee, & John. Handbook of driving simulation for engineering, medicine, and psychology. Crc Press. 2011.”

We sincerely thank the editors for raising the issue of references. We have thoroughly proofread the entire manuscript, as detailed below:

“1. Ali, Y. and Haque, M. M. and Zheng, Z. and Bliemer, Mcj. Stop or go decisions at the onset of yellow light in a connected environment: A hybrid approach of decision tree and panel mixed logit model. Analytic Methods in Accident Research. 2021;31(7–8):100165.

2. Yasir Ali and Zuduo Zheng and Md. Mazharul Haque and Mehmet Yildirimoglu and Simon Washington Understanding the discretionary lane-changing behaviour in the connected environment. Accident Analysis & Prevention. 2020;137:105463.

3. Almutairi, O. and Wei, H. Effect of speed/red-light cameras and traffic signal countdown timers on dilemma zone determination at pre-timed signalized intersections. Accident Analysis & Prevention. 2021;154:106076.

4. Asadamraji, Morteza and Saffarzadeh, Mahmood and Ross, Veerle and Borujerdian, Aminmirza and Sheikholeslami, Sina. A novel driver hazard perception sensitivity model based on drivers’ characteristics: A simulator study. Traffic Injury Prevention. 2019;20(5):492-497.

5. Francesco Bella. Driving simulator for speed research on two-lane rural roads. Accident Analysis & Prevention. 2008;40(3):1078-1087.

6. Nipjyoti Bharadwaj and Praveen Edara and Carlos Sun. Sleep disorders and risk of traffic crashes: A naturalistic driving study analysis. Safety Science. 2021;140:105295.

7. Alex A. Black and Rebecca Duff and Madeline Hutchinson and Ingrid Ng and Kirby Phillips and Katelyn Rose and Abby Ussher and Joanne M. Wood. Effects of night-time bicycling visibility aids on vehicle passing distance. Accident Analysis & Prevention. 2020;144:105636.

8. Pawe l Dro´zdziel and Rafa l Wrona. Problems with Not Recognising the Roadblocks at Reduced Visibility. Transportation Research Procedia. 2020; 44:189- 195.

9. Noor Elmitiny and Xuedong Yan and Essam Radwan and Chris Russo and Dina Nashar. Classification analysis of driver’s stop/go decision and red-light running violation. Accident Analysis & Prevention. 2010;42(1):101-111.

10. Teal Evans and Rwth Stuckey and Wendy Macdonald. Young drivers’ perceptions of risk and difficulty: Day versus night. Accident Analysis & Prevention. 2020; 147:105753.

11. Freed, S. A., Ross, L. A., Gamaldo, A. A., & Stavrinos, D. Use of multilevel modeling to examine variability of distracted driving behavior in naturalistic driving studies. Accident Analysis & Prevention. 2021;152(4):105986.

12. F. Freuli and G. De Cet and M. Gastaldi and F. Orsini and M. Tagliabue and R. Rossi and G. Vidotto. Cross-cultural perspective of driving style in young adults: 4

Psychometric evaluation through the analysis of the Multidimensional Driving Style Inventory. Transportation Research Part F: Traffic Psychology and Behaviour. 2020; 73:425-432.

13. Gazis, D., & Maradudin, H. A. The problem of the amber signal light in traffic flow. Operations Research. 1960;8(1):112-132.

14. Han, I., & Yang, K. S. Characteristic analysis for cognition of dangerous driving using automobile black boxes. International Journal of Automotive Technology. 2009;10(5):597-605.

15. Md. Mazharul Haque and Amanda D. Ohlhauser and Simon Washington and Linda Ng Boyle. Decisions and actions of distracted drivers at the onset of yellow lights. Accident Analysis & Prevention. 2016; 96:290-299.

16. Qinaat Hussain and Wael K.M. Alhajyaseen and Kris Brijs and Ali Pirdavani and Tom Brijs. Innovative countermeasures for red light running prevention at signalized intersections: A driving simulator study. Accident Analysis & Prevention. 2020; 134:105349.

17. Hussain, Q., Alhajyaseen, W., Pirdavani, A., Reinolsmann, N., Brijs, K., & Brijs, T. Speed perception and actual speed in a driving simulator and real-world: a validation study. Transportation Research Part F: Psychology and Behaviour. 2019; 62:637-650.

18. Motonori Ishibashi and Masayuki Okuwa and Shun’ichi Doi and Motoyuki Akamatsu. Indices for characterizing driving style and their relevance to car following behavior. SICE Annual Conference. 2007:1132-1137.

19. Jose-Luis Padilla and Candida Castro and Pablo Doncel and Orit Taubman - Ben -Ari. Adaptation of the multidimensional driving styles inventory for Spanish drivers: Convergent and predictive validity evidence for detecting safe and unsafe driving styles. Accident Analysis & Prevention. 2020; 136:105413.

20. Joanne M. Wood and Gillian Isoardi and Alex Black and Ian Cowling. Night-time driving visibility associated with LED streetlight dimming. Accident Analysis & Prevention. 2018; 121:295-300.

21. Johnell O. Brooks and Richard R. Goodenough and Matthew C. Crisler and Nathan D. Klein and Rebecca L. Alley and Beatrice L. Koon and William C. Logan and Jennifer H. Ogle and Richard A. Tyrrell and Rebekkah F. Wills. Simulator sickness during driving simulation studies. Accident Analysis & Prevention. 2010;42(3):788-796.

22. Michael D. Keall and William J. Frith and Tui L. Patterson. The contribution of alcohol to night time crash risk and other risks of night driving. Accident Analysis& Prevention. 2005;37(5):816-824.

23. Andras Kemeny and Francesco Panerai. Evaluating perception in driving simulation experiments. Trends in Cognitive Sciences. 2003;7(1):31-37.

24. Max Kinateder and Brittany Comunale and William H. Warren. Exit choice in an emergency evacuation scenario is influenced by exit familiarity and neighbor behavior. Safety Science. 2018; 106:170-175.

25. Kinateder, Max and Ronchi, Enrico and Nilsson, Daniel and Kobes, Margrethe and M¨uller, Mathias and Pauli, Paul and M¨uhlberger, Andreas. Virtual reality for fire evacuation research. Federated Conference on Computer Science and Information Systems. 2014:313-321.

26. Panos Konstantopoulos and Peter Chapman and David Crundall. Driver’s visual attention as a function of driving experience and visibility. Using a driving simulator to explore drivers’ eye movements in day, night and rain driving. Accident Analysis & Prevention. 2010;42(3):827-834.

27. Lee and John. Handbook of Driving Simulation for Engineering, Medicine, and Psychology. Handbook of Driving Simulation for Engineering, Medicine, and Psychology. 2011.

28. Jing Lin and Lijun Cao and Nan Li. How the completeness of spatial knowledge influences the evacuation behavior of passengers in metro stations: A VR-based experimental study. Automation in Construction. 2020; 113:103136.

29. Llopis-Castello, D., Camacho-Torregrosa, F. J., Marin-Morales, J., Perez- Zuriaga, A. M., Garcia, A., & Dols, J. F. Validation of a low-cost driving simulator based on continuous speed profiles. Transportation Research Record: Journal of the Transportation Research Board. 2016;2602(2026):104-114.

30. Long Sun & Ruosong Chang. Reliability and validity of the Multidimensional Driving Style Inventory in Chinese drivers. Traffic injury prevention. 2019;20(2):152–157.

31. Guangquan Lu and Yunpeng Wang and Xinkai Wu and Henry X. Liu. Analysis of yellow-light running at signalized intersections using high-resolution traffic data. Transportation Research Part A: Policy and Practice. 2015; 73:39-52.

32. Siwei Ma and Xuedong Yan. Examining the efficacy of improved traffic signs and markings at flashing-light-controlled grade crossings based on driving simulation and eye tracking systems. Transportation Research Part F: Traffic Psychology and Behaviour. 2021; 81:173-189.

33. Peter Mikoski and Gian Zlupko and D. Alfred Owens. Drivers’ assessments of the risks of distraction, poor visibility at night, and safety-related behaviors of themselves and other drivers. Transportation Research Part F: Traffic Psychology and Behaviour. 2019; 62:416-434.

34. Panagiotis Papaioannou. Driver behaviour, dilemma zone and safety effects at urban signalised intersections in Greece. Accident Analysis & Prevention. 2007;39(1):147-158. 6

35. Nishant Mukund Pawar and Nagendra R. Velaga. Investigating the influence of time pressure on overtaking maneuvers and crash risk. Transportation Research Part F: Traffic Psychology and Behaviour. 2021; 82:268-284.

36. Praveena Penmetsa and Srinivas S. Pulugurtha. Risk drivers pose to themselves and other drivers by violating traffic rules. Traffic Injury Prevention. 2017;18(1):63 -69.

37. Qiong Wu and Feng Chen and Guohui Zhang and Xiaoyue Cathy Liu and Hua Wang and Susan M. Bogus. Mixed logit model-based driver injury severity investigations in single- and multi-vehicle crashes on rural two-lane highways. Accident Analysis & Prevention. 2014; 72:105-115.

38. Anna-Maria Sourelli and Ruth Welsh and Pete Thomas. Objective and perceived risk in overtaking: The impact of driving context. Transportation Research Part F: Traffic Psychology and Behaviour. 2021; 81:190-200.

39. Steuer, J. Defining virtual reality: dimensions determining telepresence. Journal of Communication. 2010;42(4):73-93.

40. Long Sun and Liang Cheng and Qi Zhang. The differences in hazard response time and driving styles of violation-involved and violation-free taxi drivers. Transportation Research Part F: Traffic Psychology and Behaviour. 2021; 82:178- 186.

41. Orit Taubman - Ben-Ari and Vera Skvirsky. The multidimensional driving style inventory a decade later: Review of the literature and re-evaluation of the scale. Accident Analysis & Prevention. 2016; 93:179-188.

42. Orit Taubman-Ben-Ari and Mario Mikulincer and Omri Gillath. The multidimensional driving style inventory—scale construct and validation. Accident Analysis & Prevention. 2004;36(3):323-332.

43. Xinmiao, Fan, Gaofeng, Pan, Yan, & Mao. Investigating the effect of personality on left-turn behaviors in various scenarios to understand the dynamics of driving styles. Traffic injury prevention. 2019;20(8):801-806.

44. Tamer Yared and Patrick Patterson. The impact of navigation system display size and environmental illumination on young driver mental workload. Transportation Research Part F: Traffic Psychology and Behaviour. 2020; 74:330- 344.

45. Rainer Zeller and Ann Williamson and Rena Friswell. The effect of sleep-need and time-on-task on driver fatigue. Transportation Research Part F: Traffic Psychology and Behaviour. 2020; 74:15-29.”

Submitted filename: Response to Reviewers.pdf

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28 Feb 2022

Yellow light decision based on driving style: day or night?

PONE-D-21-39684R1

Dear Dr. Mao,

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5 Mar 2022

PONE-D-21-39684R1

Yellow light decision based on driving style: day or night ?

Dear Dr. Mao:

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Table 3

Driving style score according to gender.

Driving demographics	Variable Description	Driving Speed(m/s)	Count(percentage)
			Day Night
Driving gender	Male	11.50	6(56.25)12
	Female	11.16	3(31.25)7
Driving experience	lack	11.68	5(50.00)11
	plentiful	11.00	4(37.50)8