Modelling braking behaviour and accident probability of drivers under increasing time pressure conditions

Accident Analysis and Prevention 136 (2020) 105401

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Modelling braking behaviour and accident probability of drivers under increasing time pressure conditions

T

Nishant Mukund Pawara, Rashmeet Kaur Khanujaa, Pushpa Choudharyb, Nagendra R. Velagaa,* a b

Transportation Systems Engineering, Department of Civil Engineering, Indian Institute of Technology (IIT) Bombay, Mumbai-400076, India Transportation Engineering, Department of Civil Engineering, Indian Institute of Technology (IIT) Roorkee, Roorkee-247667, India

ARTICLE INFO

ABSTRACT

Keywords: Time pressure Driving simulator Brake pedal force Brake-to-maximum brake transition time Accident probability Driver’s safety

Drivers apply brakes to reduce the speed of a vehicle based on the perceived risk while approaching a certain event. Inadequate or excessive braking can lead to serious consequences. The current study analyses the braking behaviour and accident probability of the drivers under increasing time pressure conditions. Two perilous events (pedestrian crossing and obstacle overtaking) were designed to examine Brake Pedal Force (BPF) and Brake-ToMaximum Brake (BTMB) transition time on a driving simulator. Eighty-five Indian licensed drivers drove the simulator in three different time pressure conditions: No Time Pressure (NTP) (baseline), Low Time Pressure (LTP), and High Time Pressure (HTP). Random parameters Tobit model was used for analysing BPF and duration analysis approach was considered for BTMB analysis. Further, generalized linear mixed model with logit link function was used to study the effect of BPF and BTMB on accident probability of the drivers. The model results showed that gender, driving profession, approach speed, age, driving history, and driving condition significantly affected braking behaviour of the drivers. It was observed that in pedestrian crossing event, LTP and HTP driving conditions resulted in 42.31 % and 87.28 % increase in BPF and 13 % and 23 % reduction in BTMB respectively with respect to NTP driving condition and the corresponding changes were slightly lower in case of obstacle overtaking event. The accident probability model showed that female drivers needed 119.70 % and 186.08 % more BPF and 37.55 % and 58.51 % less BTMB in LTP and HTP driving conditions respectively to have equivalent risk levels as observed for male drivers. Further, non-professional drivers had to increase their BPF by 166.83 % in LTP and 219.93 % in HTP to offset their increased accident risk as compared to professional drivers under time pressure conditions.

1. Introduction Road traffic accidents are the 8th leading cause of deaths all over the world, which accounts for 1.35 million fatalities every year (World Health Organisation, 2018). According to the road safety annual report of International Transport Forum, fatal crashes have increased from 34 % in 2010 to 52 % in 2016 in the urban areas of Greece (International Transport Forum, 2018). Similarly, in Korea, road fatalities in urban areas represented 42 % in 2010, rising to 51 % in 2016 and Portugal witnessed 15 % rise in urban road fatalities from 2010 to 2016 (International Transport Forum, 2018). Total accidents in urban areas of India increased by 6.67 % in 2017 (Ministry of Road Transport and Highways, 2018). One of the major reasons behind the increase in the number of accidents in urban areas is the traffic rules violation, particularly over-speeding (Aarts and Van Schagen, 2006; Nilsson, 2004). In

India, over 70 % of accidents occur due to over-speeding (Ministry of Road Transport and Highways, 2018). It is reported that 5 % increase in average speed results in 10 % rise in injury crashes and 20 % rise in fatal crashes (World Health Organisation, 2008). Grundy et al. (2009) introduced 32 km/h speed zones on road segments with non-zero causalities and observed 41.9 % reduction in road fatalities. From the above-mentioned studies, it is understood that overspeeding is the common traffic violation observed throughout the world. There are numerous factors contributing to over-speeding and traffic rules violation. The most common circumstances are pleasure in swift driving, thrill-seeking, driver’s aggressive nature, peer pressure, and possibly the most prominent rationale is limited or shortage of time to reach the destination (Fuller et al., 2008; Rendon-velez, 2012). Generally, people are late to work and have limited time (time constraint situation) to reach the destination (motivational factor) which

Corresponding author. E-mail addresses: [email protected] (N.M. Pawar), [email protected] (R.K. Khanuja), [email protected] (P. Choudhary), [email protected] (N.R. Velaga). ⁎

https://doi.org/10.1016/j.aap.2019.105401 Received 30 August 2019; Received in revised form 14 November 2019; Accepted 10 December 2019 0001-4575/ © 2019 Elsevier Ltd. All rights reserved.

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creates psychological pressure on the driver, leading them to drive fast. This phenomenon is known as time pressure where drivers are under psychological stress in order to finish the journey in limited time (Rendon-velez et al., 2011; Wickens et al., 1998). Various countries investigated and confirmed time pressure as one of the major causes of over-speeding accidents. Time pressure and reckless driving ranked as the third most leading cause of road accidents in urban areas of the United Kingdom (Lloyd et al., 2015). In 2011, NHTSA conducted an extensive survey to investigate the reasons for over-speeding and found that 35 % responses were “I am late” (Schroeder et al., 2011). The study conducted by Schechtman et al. (2016) confirmed that time pressure is the leading perceived reason for the drivers’ decision to drive the vehicle above speed limit. A study conducted in Scotland revealed that 52–58 % drivers over-sped to attend important meetings (Stradling, 2005). It is important to understand that driving behaviour changes according to the level of time constraint or limitation. Fitzpatrick et al. (2017) performed a cross-sectional study with 36 drivers for different levels of time constraint conditions where the first condition was No Time Pressure (NTP) (i.e., baseline condition), second condition was hurried driving (i.e., Low Time Pressure (LTP) condition) and third condition was very hurried driving (i.e., High Time Pressure (HTP) condition). Travel time of all the drivers under NTP condition was noted and 85th percentile and 15th percentile travel times were used to impose LTP and HTP conditions on the drivers respectively. Mean speed and mean acceleration were increased with an increase in time pressure (Fitzpatrick et al., 2017). Time pressure is considered as the causal factor for risky driving (McKenna, 2005). However, no literature is available indicating the increment in accident risk due to aggressive and risky driving behaviour of the drivers in time pressure conditions (Cœugnet et al., 2013b). Peer (2010) studied time-saving bias of the drivers and reported that drivers underestimate the speed and drive at high-speed to save the time which can have negative outcomes. In real life, over-speeding due to time pressure may lead to serious consequences like injury or fatal accident than just being few minutes late to reach the destination (Fitzpatrick et al., 2017). Time pressure and speeding are the two highly correlated factors where drivers under time pressure condition usually drive over speedlimit. Moreover, the magnitude of over-speeding depends on the extent of time pressure experienced by the driver. Drivers often face perilous events, however, the impact of these events on the braking behaviour of the drivers is rarely explored under time pressure conditions. Aggressive braking is the compensatory measure adopted by the drivers to reduce their speed under such type of situations (Rendon-velez et al., 2011). Further, the extent of efforts required to minimize the accident risk is not investigated. Therefore, the current study focuses on Brake Pedal Force (BPF) and Brake-To-Maximum Brake (BTMB) transition time to examine the braking behaviour of the drivers under time pressure conditions. BPF is the force applied by the drivers on the brake pedal and BTMB is the duration of time required by the drivers to achieve maximum deceleration from the beginning of the brake application as the response to the occurrence of an event (Lee et al., 2002). This is an important braking parameter where drivers in time pressure condition are required to respond early and brake abruptly to achieve maximum deceleration which will give them additional time to further apply brakes gradually to prevent any accident at the ongoing event. These two components evaluate the extent of the aggressive and abrupt braking behaviour of the drivers under time pressure conditions.

the drivers under the influence of time pressure are extensively researched and analysed based on different type of studies, for example: questionnaire (Beck et al., 2013), film (video-recording from the perspective of the driver) (Naveteur et al., 2013) and driving simulator (Lee and LaVoie, 2018). The following subsections provide a summary of the previous research work based on braking behaviour and accident probability of the drivers under time pressure driving conditions followed with different statistical analysis techniques used in the literature. The research motivation for conducting this study is explained in the next sub-section. Finally, based on an extensive literature review, different research investigations are listed along with research objectives in the last subsection. 2.1. Braking behaviour of drivers Drivers under time pressure condition are expected to drive fast and under perilous situation, the drivers must apply brake aggressively and abruptly to avoid an accident or crash (Rendon-velez et al., 2011). Here, aggressive and abrupt braking is the compensatory measure for speed reduction. Various researchers have conducted studies on braking behaviour considering parameters such as mean BPF, type of braking (continuous or stage-wise), brake pedal position, brake pedal push distance, brake latency, BTMB, etc. Researchers have analysed the change in braking behaviour of the drivers with perilous events such as animal crossing, obstacle overtaking, and partial lane closure with and without congestion (Lee and LaVoie, 2018; Prynne and Martin, 1995; Rendon-Velez et al., 2016; Schmidt-daffy, 2013). Schmidt-daffy (2013) inspected fear and anxiety of the drivers under time pressure situations and found that the driver’s anxiety increases when the driver is unable to decide his/her preference between over-speed and safety. Further, under time pressure situations, high brake latency was observed during an animal crossing event. Lee and LaVoie (2018) examined the effect of congestion on driving behaviour under time pressure condition and observed a significant difference in the BPF applied by the drivers. During traffic congestion, mean BPF applied by the drivers was significantly higher under ‘drive quickly’ condition compared to the ‘drive safely’ condition. Prynne and Martin (1995) analysed the braking of the drivers under emergency situations and observed more collisions of the drivers with two-stage low brake pedal force. Rendon-Velez et al. (2016) explored the effect of time pressure on the driving performance and observed that mean brake applications and maximum brake pedal position significantly differed under time pressure and no time pressure conditions. Further, physiological measures were analysed where drivers initiated their visual search earlier and respiration rate increased while approaching the events under time pressure conditions. Researchers also analysed the braking behaviour of the drivers during traffic lights under a time pressure situation. Palat and Delhomme (2016) developed an urban environment with random traffic lights in driving simulator to assess the brake force applied by the drivers under time pressure driving conditions. Driver’s brake force was assessed at the onset of red light and no significant difference was observed amid no time pressure and time pressure driving conditions (Palat and Delhomme, 2016). Further, Dogan et al. (2011) investigated the change in the maximum brake push and brake pedal push distance for signalized intersections (response to green-yellow-red light) and unsignalized intersections (gap-acceptance behaviour) under time pressure conditions. No significant difference was observed for brake pedal push, but the distance covered by the vehicle during brake pedal push had significant increment under time pressure conditions. Lee et al. (2002) studied driver’s response to imminent rear-end collision of the distracted drivers in the form of reaction time, accelerator-to-brake transition time and BTMB. It was observed that drivers braked gradually when less time was required to attain maximum deceleration (BTMB). Further, Wang et al. (2016b) examined BTMB of the

2. Literature review Over-speeding, tailgating, road rage, dangerous overtaking, etc. are the overall recognized factors arising due to driving under time pressure (Parker et al., 1995; Stern, 1999; Fuller et al., 2009; Cœugnet et al., 2013a; Rendon-Velez et al., 2016). Driving performance parameters of 2

Accident Analysis and Prevention 136 (2020) 105401

Time pressure in the form of motivation

Brake force

Average of brake force

The overall studies show that over-speeding of the drivers is the direct outcome of time pressure and it is the well-articulated fact that over-speeding is associated with higher likelihood of an accident. Salminen and Lähdeniemi (2002) indicated time pressure as the most prominent risk factor in traffic during working hours, however, there is no direct evidence highlighting the increment in accident probability due to time pressure (Cœugnet et al., 2013b). Drivers under time pressure conditions, are often observed to exhibit risky driving to complete the journey, even at the possible expense of safety (Cœugnet et al., 2013b; Weyman et al., 2003). Vehicle control of the driver while driving under time pressure plays an important role in the safety of the subject driver as well as the overall road users (Lee and LaVoie, 2018). The study conducted by Svenson (2009) showed that drivers usually underestimate the required braking distance during high-speed driving and are often late to apply brakes leading to increased risk of an accident. A questionnaire study by Cœugnet et al. (2013b) revealed that drivers under time pressure condition were responsible for the critical situations (near accidents) on the road. Under time pressure condition, drivers reported inability to control the vehicle while driving and lack of self-confidence led to road accidents. The accident risk of the drivers is also associated with the brake application of the drivers. Prynne and Martin (1995) assessed braking of the drivers and observed 17 % increase in accident risk of the drivers when they paused during initial braking than those with continuous braking. Wang et al. (2016b) evaluated brake application of the drivers under low, medium, and high situational emergency and observed multi-stage braking behaviour which led to extended delay in achieving maximum deceleration and hard brake application to avoid collisions.

77

2.3. Statistical analysis techniques Researchers have used various analysis techniques to analyse braking behaviour of the drivers (Table 1). Statistical analysis techniques such as paired t-test (Rendon-Velez et al., 2016) and repeated measures ANOVA (Schmidt-daffy, 2013) followed by post hoc tests (Lee and LaVoie, 2018) are extensively used to examine the influence of time pressure on the braking behaviour of the drivers. No study is conducted to establish the relationship between braking behaviour (brake pedal force and brake-to-maximum brake transition time) and explanatory variables. The current study focusses on establishing the relationship between braking behaviour (response variable) and demographic as well as driving characteristics (explanatory variables) using mixed modelling technique and duration analysis. 2.4. Research motivation The overall literature review leads to the finding that drivers under time pressure conditions apply high BPF to reduce the speed of the vehicle under perilous conditions. However, no study is performed to comprehend the extent of BPF or any other compensatory measure in reducing the accident risk of the drivers under time pressure conditions in the presence of perilous events. Further, very few studies are performed to quantify the effect of varying time pressure conditions on

Prynne and Martin (1995)

United States

Traffic lights (response to green-yellow-red light) and gap acceptance for traffic from right to left 36 Netherlands Dogan et al. (2011)

40 Germany

Animal (deer) crossing

2.2. Time pressure and accident probability

Obstacle designed to fully stop the vehicle

Maximum brake push (%), brake pedal push distance (m)

Repeated measures ANOVA Univariate ANCOVA Brake latency

Chi-square test

Time pressure in the form of motivation Time pressure in the form of motivation Time pressure in the form of motivation 94 France

Palat and Delhomme (2016) Schmidt-daffy (2013)

56 Netherlands Rendon-Velez et al. (2016)

30 United States Lee and LaVoie (2018)

drivers for different type of headways in the car following and reported that drivers must attain maximum deceleration in at least 0.92 s to avoid rear-end collision. An overall summary of the literature review on the braking behaviour of the drivers is presented in Table 1. The current study attempts to analyse braking behaviour of the drivers using two different parameters: mean BPF (N) and BTMB (seconds). Mean BPF is well-explored under different driving conditions, but BTMB was first introduced by Lee et al. (2002) to study drivers’ response to imminent rear-end collisions of the distracted drivers.

Time pressure condition had no significant effect on braking behaviour Brake latency was significantly higher for the time pressure situation No significant difference in maximum brake pedal push but an increase in brake pedal push distance was observed in the time pressure situations Drivers encountering accidents had 15 % lower brake force values

Significant difference was observed in mean brake application as well as in maximum brake pedal position Correlation and Paired t test

Mean number of brake applications (number) and maximum brake pedal position (0–1) Brake force

Significant difference in brake pedal force Post hoc t test Brake Pedal Force (N)

Time pressure in the form of motivation Time pressure in the form of time constraint and motivation

Partial blockage of the left lane – before and during congestion Car following, obstacle overtaking with and without traffic in opposing lane and intersections with and without approaching traffic Random traffic lights

Driving performance parameter Condition Test scenario and event Sample Size Country Study

Table 1 Summary of driving simulator experiments examining braking behaviour of the drivers under time pressure condition.

Statistical analysis techniques

Findings

N.M. Pawar, et al.

3

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driving performance. Moreover, only few studies particularly address the issue of quantifying the effect of different compensatory measures on the accident probability of the drivers. Considering all these aspects, the current study is designed to examine the effect of time pressure on the braking behaviour and accident probability of the drivers under two different events: pedestrian crossing and obstacle overtaking. 2.5. Research investigation and objectives The current study concentrates on investigating the following research probes: RI1: Investigating the extent of change in braking behaviour (brake pedal force and brake-to-maximum brake transition time) of the drivers under varying time pressure conditions. RI2: Examining the effect of driver’s demographics, driving characteristics, and vehicle dynamics on braking behaviour and accident probability under different perilous situations. RI3: Investigating the extent of compensatory measure (BPF and BTMB) to offset the increased accident risk under varying time pressure conditions. To assess these investigations, the following three objectives are defined:

Fig. 1. Driving simulator used for the study.

The driving simulator has two different software: SimVista and SimCreator. These two software are used to create the simulation model where SimVista helps in developing the static world like road network and SimCreator helps in controlling ambient traffic and different dynamic events like pedestrian crossing (Choudhary and Velaga, 2018a). Driving performance data like speed, longitudinal and lateral acceleration, brake pedal force, etc. are continuously recorded by the SimCreator model at 120 Hz.

1) Develop a predictive model of drivers applying BPF for two different events, i.e., pedestrian crossing and obstacle overtaking using different explanatory variables; 2) Perform duration analysis of BTMB to understand the driver’s brake application under time pressure in the presence of pedestrian crossing and obstacle overtaking events; and 3) Develop an accident probability model to study the effect of BPF and BTMB as compensatory measures to offset the accident risk under time pressure conditions.

4.2. Time pressure The driving task comprised of driving the test scenarios for three different time pressure conditions. The first driving condition was the baseline state where No Time Pressure (NTP) was imposed on the drivers. The second and third driving tasks had two levels of time pressure conditions where 10 % of the travel time was reduced from the travel time required in the baseline state to impose Low Time Pressure (LTP) condition and 20 % of the travel time was reduced from the baseline state to impose High Time Pressure (HTP) condition on the drivers (Bertola et al., 2012). In addition to time constraint, drivers were given hypothetical situations such as ‘you are late to reach your friend’s house’ for LTP and ‘you are late to reach airport’ for HTP and were asked to imagine these situations while driving, which would act as motivation for the drivers to complete the driving task in the restricted time. Time constraint with some hypothetical situations and monetary benefit are necessary to create time pressure on the drivers participating in the driving simulator experiment (Fitzpatrick et al., 2017; Mahajan et al., 2019; Peer, 2010).

3. Research approach and organisation A comprehensive literature review is conducted to understand the effect of time pressure on the change in driving behaviour. Based on literature review, different time constraint conditions are identified and designed for the study. A questionnaire and test scenario are developed to examine the driving behaviour of the drivers under time pressure conditions. Then, participants are recruited for data collection where all the participants filled the questionnaire and drove the driving simulator. After data collection, preliminary analysis of questionnaire and simulator data is performed to understand the nature of the data. Then, statistical modelling is carried out to quantify the effect of time pressure on the braking behaviour (BPF and BTMB) and accident probability of the drivers. Finally, based on model results, all the framed objectives are reviewed in discussion and conclusions are drawn from the current study.

4.3. Experimental design of test scenarios and events A 6 km long typical urban road (city road) was designed and simulated in the driving simulator. The simulated driving route comprised of a four-lane and two-lane undivided carriageway with 60 km/h and 30 km/h posted speed limits respectively. Traffic volume was specifically kept low so that no congestion occurs which can suppress the effect of time pressure and raise other factors like road rage which may create disbelief in the minds of the drivers to reach the destination before the temporal deadline (Svenson and Benson, 1993; Techer et al., 2019). As mentioned before, two different types of events are designed in the scenario to assess the BPF and BTMB. These events are (1) pedestrian crossing on the four-lane undivided road (Fig. 2) (Choudhary and Velaga, 2017a; Fitzpatrick et al., 2017) and (2) obstacle overtaking

4. Data collection 4.1. Driving simulator The current study is performed using a fixed-base fully instrumented open cab driving simulator system as shown in Fig. 1. Driving simulator cab provides 150° horizontal view using three LED screens of 42 inch. The simulator includes all car controls, for example, feedback power steering wheel with 900-degree rotation, clutch, brake, and throttle pedal, gear shifter (for manual transmission), rear and side view mirrors, turn signals, analogous to the actual car. The simulator is equipped with a screen panel displaying the speedometer, rpm meter, turn signal indicator and the audio system in the simulator was linked with the simulated scenarios to provide sound of traffic and engine noise to recreate the actual car surrounding which enhances the realism of the driving experience on the simulator.

Fig. 2. Pedestrian crossing event. 4

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far distance. The precautionary behaviour of the drivers during both the events will vary according to the driver’s strategy of driving. Therefore, the data needs to be separately extracted and analysed to avoid the influence of one event on other which otherwise may produce biased results. Mean BPF was measured before the occurrence of the event of each individual driver. Further, BTMB was measured just after the reaction of the drivers to the ongoing event to understand the brake application (gradual or abrupt) of the drivers. The participants drove the same test scenario for three different driving conditions: no time pressure, low time pressure (10 % reduction in control state travel time), and high time pressure (20 % reduction in control state travel time). The order of the driving task is not randomized but was in a fixed order of NTP, LTP, and HTP. This was done to facilitate the individual attunement to the time constraint in the time pressure conditions (Paschalidis et al., 2019, 2018; Rendon-Velez et al., 2016). 4.4. Participants and questionnaire data Eighty-five participants (59 males and 26 females) possessing a valid driving license with normal vision (6/6) were recruited for the current study. Age of the participants varied from 18 years to 53 years. Out of these eighty-five drivers, 29 drivers were professional drivers working in a private transport company. The drivers were considered as the professional drivers if all the following conditions were satisfied: driving experience ≥ 5 years; annual mileage ≥ 15,000 kms; age ≥ 25 years (Choudhary and Velaga, 2019). A questionnaire was prepared to collect before-drive and after-drive data from the participants. Before the start of the experiment, all the participants filled the before-drive questionnaire form which was divided into three sections: demographic details; physiological information and driving information. The previous literature has shown a significant effect of regular physical exercise on the BPF applied by the drivers (Yadav and Velaga, 2019a). Therefore, in physiological information, all the participants were asked the number of days and the minimum time duration dedicated by them to perform regular physical exercise. Haskell et al. (2007) performed a study on physical activity and recommended minimum of 30-minute physical exercise for five days a week for adults (18–65 years) to improve and maintain health. Thus, based on this criterion, the participants performing minimum 30-minutes physical exercise for at least five days a week were categorized as regular physical exercise performers. Table 2 shows the details of the explanatory variables which were defined using the information collected through the questionnaire survey. All the participants filled the after-drive questionnaire after each test drive and the details of the same are presented in Table 3. The standard NASA-TLX mental workload questionnaire was directly used in the current study (Hart and Staveland, 1988; Rendon-Velez et al., 2016). Apart from NASA-TLX mental workload questionnaire, different time pressure related questions (Table 3) were formulated to assess the driver’s experience regarding time pressure driving conditions. A 5point Likert scale (1–5) was used to collect the driver’s response where 1 indicated “no experience” and 5 indicated “extreme experience”. After data collection, all the participants received monetary incentives of INR 250 for dedicating their valuable time for this research work.

Fig. 3. Obstacle overtaking event (a) driver approaching the obstacle, (b) opposite lane free from the obstacle; (c) driver overtaking the obstacle.

with traffic in the opposite lane on the two-lane undivided road (Fig. 3) (Rendon-Velez et al., 2016). Pedestrian crossing and obstacle overtaking events are considered as perilous events and are extensively used in transportation safety research to examine driver’s response and effectiveness of Emergency Steering Evasions (ESE) assistance systems, Advanced Driver Assistance Systems (ADAS), etc. (Haque and Washington, 2015; Lindgren et al., 2008; Yadav and Velaga, 2019b; Zhao et al., 2019). Therefore, these events are selected for the current study to assess driver’s braking behaviour in different time pressure driving conditions. For pedestrian crossing event, seven pedestrians were placed on the left side in the direction of the traffic with a minimum speed of 0.6 m/s to the maximum speed of 1.63 m/s (Laxman et al., 2010). Speed of the pedestrians was altered based on the subject driver’s speed. Speed of the pedestrians ranged between 0.6–1.35 m/s or 1.0–1.63 m/s if the subject driver had speed less than 20 m/s or more than or equal to 20 m/s, respectively before the initiation of the event. Pedestrians commenced road crossing when the subject vehicle was 120 m away from them. In the obstacle overtaking event, a jersey barrier (40-meter long) is used as an obstacle to block the lane to hinder the free movement of the subject driver. Four vehicles were parked in the opposite lane parallel to the jersey barrier (obstacle) with a 20-meter gap between two consecutive vehicles. Vehicles in the opposite lane, started moving with 60 km/h speed when the subject vehicle was 90 m away from them. The whole event was designed to stop the subject driver at the beginning of the obstacle or jersey-barrier and make the driver wait till the vehicles from the opposite direction cleared the lane for the subject driver to approach and overtake the obstacle. The distance of 120 m and 90 m were set for pedestrian crossing and obstacle overtaking events based on a pilot study with ten participants before the start of data collection. For more detailed explanation regarding the design of both the events, readers are requested to refer Appendix A. The experiment leader continuously observed the driver throughout the scenario and noted all the accidents which occurred during data collection. Further, accident data is verified with the simulator data where an accident is considered if the driver does not decelerate and apply brakes before the event and collided with the pedestrian or obstacle in the respective events (Choudhary and Velaga, 2017b). Pedestrian crossing event is an unexpected and sudden event whereas obstacle overtaking event can be noticed by the drivers from a

4.5. Procedure The experimental procedure followed in the study is summarized below: (1) One day prior to the experiment, participants were asked to get at least 7−8 hours of sleep (U.S. Department of Health and Human Services, 2011), consume no alcohol before the experiment to ensure no effect of partial deprivation on the driving performance of the drivers (Yadav and Velaga, 2019b). All the participants were 5

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Table 2 Summary statistics of the variables from the questionnaire and simulator output. Variables (Type)

Description

Driver Demographics Age (Continuous) Gender (Categorical)

Age of driver in years Driver’s gender

Physiological Information Exercise (Categorical)

Exercise duration: 30 minutes, five days a week

Driving Characteristics Driving profession (Categorical)

Yes No

42.35 57.65

Professional drivers

Driving Experience in years (Continuous) Annual kilometres driven (Continuous) Type of Trip (Categorical)

Number of years the driver is driving car Approximate annual kilometres of drive completed What accounts for the driver’s everyday trip?

Yes No

34.12 65.88

Driving History Ticketing Over-speeding (Categorical)

Has the driver ever been ticketed for over-speeding?

Accident in the last 5 years Over-speeding (Categorical)

Yes No

4.70 95.30

Driver’s involvement in over-speed accident

Vehicle Dynamics Approach speed (Continuous)

Yes No

40 60

Speed of the vehicle before the brake application

Pedestrian Crossing Obstacle Overtaking

(2)

(3)

(4) (5) (6)

Level

Male Female

requested to dedicate one and half hour for the experiment with no prior commitments before the completion of the experiment to avoid the influence of real-world time pressure on the driving performance of the drivers on a driving simulator experiment. On the experiment day, the participant was briefed about the driving simulator and its importance in assessing safety under varying conditions and subsequently an overview of the current study was given. A practice session was held for each participant before the beginning of the experiment. The practice drive of 1 km was given to the participants to make them familiarize with the simulation as well as driving simulator controls. If requested, drivers were given another round of practice drive before the start of the actual experiment. The participants were then requested to fill the questionnaire form. The participants had to drive the simulator in three different conditions: NTP, LTP, and HTP. After each drive, the participant was given rest for 5−15 mins based on his/her demand to avoid the effect of simulator sickness (if any) on the driving performance. In this period, the participant was requested to fill the after-drive questionnaire form.

Work Recreational

Mean

Standard Deviation

29.44

8.58

7.93 8493.45

18.67 14.98

7.71 22350.53

Percentage

69.41 30.59

63.53 36.47

6.02 4.62

collected from the participants are presented in Table 2. This data is used as explanatory variables during statistical modelling and is broadly classified as a continuous and categorical variable. Mean and standard deviation of continuous variables and percentage of the categorical variables are shown in Table 2. For instance, the driving experience is a continuous variable with mean of 7.93 years and standard deviation of 7.71 years, whereas driving profession is a categorical variable having a 34.12 % of professional drivers and 65.88 % of nonprofessional drivers. Accident data is collected from the participants of the last 5 years where 95.30 % of the total participants are not ticketed and 40 % of the participants encountered accidents. The general reason behind these large number of accidents is that drivers are confident of not getting ticketed even if they are over-speeding in reality. 5.2. Statistical analysis of self-reported measures The statistical analysis of the self-reported measures is conducted to validate the assumption that time pressure affected the driving behaviour of the drivers. Table 3 represents the self-reported measures (only time-related) collected from the drivers after each driving session (i.e., NTP, LTP, HTP). The results show a significant difference (p-value: < 0.01) in self-reported time-related measures. Further, Cohen’s d (|d|) is estimated to examine the significance of standardised difference between two groups’ mean (Rendon-Velez et al., 2016). Cohen (1988) suggested benchmark values of effect size as small (|d| = 0.2), medium (|d| = 0.5), and high (|d| = 0.8) for interpreting results. Cohen’s d of

5. Data analysis and results 5.1. Preliminary analysis of explanatory variables The descriptive statistics of the questionnaire and simulator data Table 3 Inferential statistical analysis of self-reported measures. Self-reported measure

Shortage of time Feeling of hurry Fast driving due to time monitoring Time pressure

Description

Mean (Standard Deviation)

p-value (|d|)

NTP

LTP

HTP

NTP-LTP

LTP-HTP

NTP-HTP

Did you feel shortage of time? Did you experience need to hurry to complete the task? Did time monitoring made you drive fast?

1.09 (0.36) 1.32 (0.71)

2.43 (0.95) 2.92 (1.08)

3.04 (1.18) 3.72 (1.19)

< 0.01 (1.31) < 0.01 (1.21)

< 0.01 (0.46) < 0.01 (0.61)

< 0.01 (1.53) < 0.01 (1.76)

1.84 (1.09)

2.78 (1.02)

3.47 (1.12)

< 0.01 (0.98)

< 0.01 (0.47)

< 0.01 (1.34)

Did you experience any pressure due to the limited availability of time?

0.82 (0.69)

2.73 (1.00)

3.55 (0.86)

< 0.01 (1.49)

< 0.01 (0.73)

< 0.01 (2.45)

|d| = Cohen’s d. 6

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summary statistics (standard error, t-statistic, and p-value) and goodness-of-fit test results using AIC (Akaike Information Criterion), BIC (Bayesian Information Criterion) and log-likelihood values. The model results (Table 4) reveal that the time pressure and increment in time pressure has a significant effect on the mean BPF of the drivers. The model for pedestrian crossing event shows that BPF increased by 10.02 N (42.31 %) in LTP and 20.67 N (87.28 %) in HTP driving conditions. Similarly, in obstacle overtaking event, LTP and HTP driving conditions resulted in 3.28 N (7.88 %) and 16.31 N (39.18 %) increase in BPF, respectively. Further, the results show that female drivers are observed to apply more BPF compared to male drivers in pedestrian crossing (12.97 N) as well as in obstacle overtaking (19.45 N) events. The professional drivers showed more aggressive behaviour and applied 19.76 N and 13.05 N more BPF than the non-professional drivers in pedestrian crossing and obstacle overtaking events, respectively. BPF is observed to increase with the increase in 1 m/second approach speed of the drivers (2.18 N in pedestrian crossing and 1.63 N in obstacle overtaking). Drivers’ age, physiological characteristics, and purpose of driving did not have any significant effect on the BPF due to increment in time pressure of the drivers.

0.2 indicates that the difference between two groups’ means vary by 0.2 standard deviation (Lakens, 2013). This means that the difference is trivial if two groups’ means vary by less than 0.2 standard deviation, even if the result is statistically significant (p-value < 0.05)(Walker, 2007). From Table 3, it can be observed that Cohen’s d of the self-reported time-related measures between NTP-LTP and NTP-HTP is greater than 0.8. This signifies that high significant difference exists between the two groups (NTP-LTP and NTP-HTP). For LTP-HTP group, Cohen’s d value is in medium range for the responses: feeling of hurry (|d| = 0.61) and time pressure (|d| = 0.73), indicating moderate level significant difference between mean of two groups. Further, small difference is observed for the responses: shortage of time (|d| = 0.46) and fast driving due to time monitoring (|d| = 0.47) for LTP-HTP group. Overall, these results indicate that drivers experienced time pressure and the objective of the increasing time pressure driving conditions is achieved. 5.3. Modelling the brake pedal force Drivers under time pressure tend to over-speed while driving to compensate for the travel time required to reach the destination. During the sudden event (pedestrian crossing) or unavoidable situation (obstacle overtaking), drivers must apply brakes to reduce speed to avert an accident. Force exerted on the brakes depends on many factors such as driver demographics, driving characteristics, driving conditions, etc. and thus force applied on brake pedal differs individually. Further, the effect of both the events on the drivers will be different due to which the force applied by the drivers during both the events will vary. Fig. 4 shows the mean BPF applied by the 85 participants across different driving conditions. As a precautionary behaviour in time pressure driving conditions, the increasing trend of mean BPF for pedestrian crossing as well as obstacle overtaking events is observed. In order to establish the relationship between mean BPF and different time pressure conditions and driver characteristics, random parameters Tobit model is developed separately for both the events. Two different models are developed to avoid the biasness in the results due to two different type of events. The Tobit model is extensively used by the researchers to model censored and truncated non-negative data (dependent variable) (Anastasopoulos et al., 2008; Sigrist and Hirnschall, 2019). In the current study, BPF applied by the driver while driving the simulator ranges between 0−170 N. Therefore, the dataset obtained from the simulator data collection can be considered as leftcensored observation (left-censored at zero) (Farah et al., 2009). Thus, a Tobit model is considered for modelling BPF data (left-censored at zero). Here, random parameters are used to model the longitudinal data to handle the individual-level heterogeneity among the participants (Anastasopoulos et al., 2012). The models for both the events (pedestrian crossing and obstacle overtaking) are shown in Table 4 with

5.4. Modelling brake-to-maximum brake transition time BTMB is obtained for each participant for both the events across three driving conditions (NTP, LTP and HTP). Box plots are plotted for both the events as shown in Fig. 5 where a decreasing trend for BTMB is observed under increasing time pressure indicating the immediate and swift application of brakes with the occurrence of an event. BTMB is modelled using duration (survival) analysis to examine the effect of various exogenous factors along with the influence of time pressure. Parametric duration model, also known as hazard-based duration model is a modelling technique to analyse the probable time duration before the occurrence of an event (Washington et al., 2011). In the current study, BTMB- the time required to attain maximum deceleration rate after the early application of the brake pedal (Lee et al., 2002), is used to model the conditional probability of the intervening time before the incidence of an event. Survival modelling with the Accelerated Failure Time (AFT) method, assumes that the covariates accelerate (rescales) the time variable directly in the baseline survivor function (where all covariates are zero) (Yadav and Velaga, 2019b). The probability of the drivers attaining maximum deceleration increases with increase in time, therefore, Weibull distribution is assumed for modelling BTMB (Bella and Silvestri, 2016; Haque and Washington, 2015). The survival function of the Weibull distribution model can be written as:

S (t ) = exp[ ( t ) P ]

Fig. 4. Mean brake pedal force of two different events across three different driving conditions. 7

(1)

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Table 4 Results of random parameters Tobit model and goodness-of-fit for mean brake pedal force for pedestrian crossing and obstacle overtaking events. Pedestrian crossing

Obstacle overtaking

Parameters

Estimate

SE

z

Estimate

SE

z

Intercept LTP HTP Approach speed Gender (Male) Driving profession (Professional drivers)

23.68 10.02 20.67 2.18 −12.97 19.76

4.75 5.68 0.40 4.07 3.94 5.96

3.97*** 2.11** 3.64*** 5.46*** −3.18*** 5.00***

41.62 3.28 16.31 1.63 −19.45 13.05

5.74 1.81 4.32 0.43 3.89 3.74

7.24*** 1.81* 3.77*** 3.76*** −4.99*** 3.48***

Goodness-of-fit of the models Pedestrian crossing

Obstacle overtaking

df

AIC

BIC

Log-Likelihood

df

AIC

BIC

Log-Likelihood

8

2403.40

2431.73

−1193.70

8

2370.01

2398.34

−1177.01

SE = Standard Error; df = degrees of freedom; AIC = Akaike Information Criterion; BIC = Bayesian Information Criterion; *p < 0.10; **p < 0.05; ***p < 0.001.

Fig. 5. Boxplot representing brake-to-maximum brake transition time (seconds) for pedestrian crossing and obstacle overtaking event.

Table 5 Results of Weibull AFT model with gamma frailty for Brake-To-Maximum Brake (BTMB) transition time for pedestrian crossing and obstacle overtaking events. Pedestrian crossing

Obstacle overtaking

Parameters

Estimate

Exp(β)

SE

z-value

Estimate

Exp(β)

SE

z-value

LTP HTP Gender (Male) Age Over-speed accident Intercept P Variance of gamma frailty (θ)

−0.13 −0.25 −0.14 −0.008 – 0.69 3.78 1.16

0.87 0.77 0.86 0.99 – 1.99

0.077 0.076 0.71 0.004 – 0.15 0.52 0.38

−1.85* −3.28** −1.98* −2.01* – 4.63***

−0.10 −0.24 – – −0.19 0.68 4.28 1.06

−0.90 0.79 – – 0.82 1.97

0.052 0.054 – – 0.075 0.088 0.46 0.26

−1.93* −4.44*** – – −2.59** 7.70***

Goodness-of-fit of the model

Log-likelihood at convergence Chi-square (df) LR test of θ: Chi-square Number of groups Number of observations

Pedestrian crossing

Obstacle overtaking

−179.70 21.20*** (4) 29.32*** 85 255

−144.49 27.79***(3) 48.06*** 85 255

SE = Standard Error; df = degrees of freedom; AIC = Akaike Information Criterion; LR = Likelihood Ratio; *p < 0.10; **p < 0.05; ***p < 0.001.

8

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Fig. 6. Brake-to-maximum brake transition time survival probability plot of (a) male drivers and (b) female drivers for pedestrian crossing event in time pressure driving conditions. Fig. 7. Brake-to-maximum brake transition time survival probability plot of drivers (a) with over-speeding accident history and (b) without over-speeding accident history for obstacle overtaking event in time pressure driving conditions.

The location parameter can be formulated in terms of explanatory variables as:

= exp[ P(

0

+

1 X1

+ …)]

(2)

Where β is the coefficient of the explanatory variable X. The above-mentioned parametric duration model is valid for independent observations whereas the current study has repetitive observations of each driver in three different driving conditions (NTP, LTP and HTP). Thus, to account the individual heterogeneity or frailty, Weibull regression model with shared frailty is generally incorporated which accounts the correlation amid observations acquired from the individual driver (Bella and Silvestri, 2016; Choudhary and Velaga, 2017c; Yadav and Velaga, 2019b). Weibull AFT models with gamma frailty are developed separately for pedestrian crossing and obstacle overtaking events using Stata MP-15 software. All the explanatory variables, as stated in Table 2, are considered for modelling. Table 5 presents the summary statistics and goodness-of-fit of the Weibull AFT models with gamma frailty for BTMB. Table 5 shows that LTP and HTP are significant contributors in reducing BTMB of the drivers for both the events. Drivers are observed to attain maximum deceleration in LTP and HTP compared to NTP driving condition by 13 % and 23 % in pedestrian crossing and 10 % and 21 % in obstacle overtaking event respectively. In pedestrian crossing event, male drivers swiftly decelerated (14 %) compared to female drivers. Further, the driver’s age is observed to affect the BTMB where the 1year increase in age resulted in 1 % slower response in BTMB. It is observed that drivers with over-speed accident history (in the last five years) are observed to attain maximum deceleration by 18 % faster than the drivers without any accident history record. The survival probability is estimated, and plots are developed using parameter estimates from Table 5 and using Eq. (1). For instance, BTMB survival probability of male and female drivers after 2.12 s for pedestrian crossing in LTP driving condition is computed as follows: Smale (t = 2.12) 2.123.78] = 0.03

=

exp[-{exp

(t = 2.12) Sfemale 2.123.78] = 0.13

=

exp[-{exp

(-3.78(-0.13*1 + 0.69))}*

Using this approach, BTMB survival probabilities are computed for all three driving conditions for pedestrian crossing event of male and female drivers as shown in Fig. 6a and Fig. 6b respectively. From the graphs, it can be observed that BTMB survival probabilities decrease with the elapsed time indicating an increase in the probability of the drivers attaining maximum deceleration with increased time. From Fig. 6, it can be observed that male drivers have lower survival probability compared to female drivers. For instance, the BTMB survival probabilities at 2.12 s for male drivers are 12 %, 3 %, and 0 % for NTP, LTP, and HTP driving conditions respectively (Fig. 6a). Whereas, the BTMB survival probability for female drivers at 2.12 s increased to 28 %, 13 %, and 4 % for NTP, LTP, and HTP respectively (Fig. 6b). These findings suggest that male drivers decelerate swiftly (i.e., apply brake abruptly) to compensate the increased risk while driving through a sudden event. Similar to pedestrian crossing event, the BTMB survival probabilities are estimated for obstacle overtaking event as shown in Fig. 7 using parameter estimates from Table 5 and Eq. (1). The BTMB survival probabilities are developed for two groups (1) drivers with over-speed accident history (Fig. 7a) and (2) drivers without over-speed accident history (Fig. 7b). The drivers with over-speeding accident history achieved maximum deceleration within 1.96 s in HTP driving condition. The resultant BTMB survival probability of the drivers without any over-speeding accident history at 1.96 s is 7 % under HTP driving condition. The right shift in survival probability of the drivers without any accident history indicates that they do not sense the risk as equivalent as the drivers with accident history and thus, are slow to attain maximum deceleration.

(-3.78(-0.13*1-0.14*1 + 0.69))}*

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Fig. 8. Total number of accidents occurred in pedestrian crossing and obstacle overtaking events under driving conditions.

5.5. Modelling accident probability

As hypothesized earlier, BPF and BTMB affected the accident probability of the drivers. It is observed that 1 N increment in BPF reduced the accident risk by 5 %, whereas 1-second additional time consumed by the drivers to attain maximum deceleration increased the risk by 85 % in pedestrian crossing event. These findings indicate that, for pedestrian crossing event, drivers should apply swift and more BPF, i.e., time taken to reach maximum deceleration should be as minimum as possible to avoid road accident under time pressure conditions. In obstacle overtaking event, BTMB showed no significant effect as a compensatory measure in the accident probability model. Obstacle overtaking is an unavoidable situation where no sudden event occurs. Obstacle and traffic in the opposite lane are clearly visible to the drivers and thus, they cautiously approach the event. Therefore, drivers gradually decelerate by applying brakes based on the speed of the car. Speed of the drivers in time pressure is more compared to no time pressure condition and thus BPF required to stop the vehicle is also more. Therefore, BPF has a significant effect on accident probability of the drivers where increase in 1 N BPF results in 6 % reduction in accident risk of the drivers. Further, it is observed that female drivers have 33 % higher accident risk than male drivers in pedestrian crossing event. Non-professional drivers are observed to have more likelihood of accident (12 %) compared to professional drivers in obstacle overtaking event. The important point from both the models is that although approach speed is considered as an important factor in accident risk in previous research (Choudhary and Velaga, 2017b), it is found to have no significant effect on accident probability models for both the events in the present study.

Throughout the experiment, 28 accidents occurred in pedestrian crossing event and 20 accidents occurred in obstacle overtaking event (Fig. 8). Overall, we can observe an increase in the number of accidents due to increment in time pressure. Here, the pattern of the accident occurred is straightforward, and we can have two possible reasons: less BPF and more BTMB. Thus, to understand the influence of these compensatory measures, accident probability is estimated using GLMM with logit link function (logistic regression with random effects) (Choudhary and Velaga, 2017b; Thiele and Markussen, 2012) which presents the accident probability of a driver for the specific event (pedestrian crossing and obstacle overtaking). The developed models for pedestrian crossing and obstacle overtaking events are presented in Table 6. It shows all the significant parameter estimates with summary statistics and goodness-of-fit of the model. Odds ratio (Exp (β)) is used to quantify the relative occurrence of the response variable for the given explanatory variable representing increase or decrease in the response variable with unit increment in the explanatory variable (Choudhary and Velaga, 2018b; Szumilas, 2010). The model results from Table 6 indicate increase in accident risk due to the influence of LTP and HTP driving conditions. During the obstacle overtaking event, the accident probability of the drivers is increased by 1.84 and 3.1 times in LTP and HTP, respectively. Time pressure show more pronounced effect in the pedestrian crossing event, as LTP and HTP resulted in 2.67 and 4.68 times increment in the accident risk from NTP driving condition.

Table 6 Accident probability model results for pedestrian crossing and obstacle overtaking events. Pedestrian crossing

Obstacle overtaking

Parameters

Estimate

Exp(β)

SE

t-stat

Estimate

Exp(β)

SE

t-stat

Intercept LTP HTP BPF BTMB Gender (Male) Driving profession (Professional drivers)

−1.74 2.67 4.68 −0.056 0.62 −1.11 –

0.18 14.43 107.73 0.95 1.85 0.33 –

1.02 0.79 0.93 0.012 0.28 0.48 –

−1.70 3.37*** 5.05*** −4.63*** 2.16** −2.28** –

−0.77 1.84 3.1 −0.06 – – −2.112

0.46 6.31 22.94 0.94 – – 0.12

0.26 0.86 0.89 0.01 – – 1.04

−2.94** 2.13** 3.45*** −3.37*** – – −2.09**

Goodness-of-fit of the model Pedestrian Crossing

Obstacle Overtaking

df

AIC

BIC

Log-likelihood

df

AIC

BIC

Log-likelihood

8

1344.39

1372.73

−677.18

8

1385.94

1414.27

−689.81

SE = Standard Error; df = degrees of freedom; AIC = Akaike Information Criterion; BIC = Bayesian Information Criterion; *p < 0.10; **p < 0.05; ***p < 0.001. 10

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Fig. 9. (a) Accident probability of male drivers for different time pressure conditions; (b) illustration of the required increment in BPF and reduction in BTMB to reduce the accident risk of male drivers for pedestrian crossing event in LTP and HTP driving conditions.

Fig. 9 illustrates accident probability curves for male drivers under different time pressure conditions for pedestrian crossing event considering both compensatory measures: BPF (ranging from 0−170 N) and BTMB (ranging from 0 to 6s). From Fig. 9a, it is observed that accident probability decreases with increase in BPF and decrease in BTMB. For deeper insight, accident probability of the driver is compared for all three driving conditions and at the same level of BPF (40 N) and BTMB (4.58 s). In general, increase in the accident probability is observed with increase in time pressure at the same level of BPF and BTMB. For example, likelihood of an accident is 9.53 % when the driver applies 40 N BPF and achieves maximum deceleration within 4.58 s. However, for the same level of 40 N BPF and 4.58 s BTMB, accident probability increases to 60.33 % and 91.90 % for LTP and HTP driving conditions respectively. This finding clearly indicates that drivers should apply swift and high BPF to achieve maximum deceleration in shorter time to reduce the risk of an accident. Fig. 9b shows that the driver’s likelihood of an accident in LTP and HTP is equivalent to that in NTP driving condition if the driver is successful in applying 84.30 % and 150.67 % more BPF and takes 26.37 % and 47.33 % less time to reach maximum deceleration in LTP and HTP respectively. Similarly, Fig. 10 shows the accident probability of female drivers for pedestrian crossing event for three different driving conditions with BPF and BTMB as compensatory measures. Model results from Table 6 showed that female drivers are more likely to encounter accidents

compared to male drivers. This finding can be visualized from Fig. 10a where accident probabilities of the female drivers at the same level of 40 N BPF and 4.58 s BTMB are 24.22 %, 82.19 %, and 97.17 % under NTP, LTP, and HTP driving conditions respectively. As shown in Fig. 10b, to reduce the likelihood of accident equivalent to accident probability of male drivers under NTP driving condition, the female drivers are required to be more aggressive in braking and should apply 35.63 %, 119.70 %, and 186.08 % more BPF by consuming less BTMB by 11 %, 37.55 %, and 58.51 % in NTP, LTP, and HTP driving conditions respectively. Accident probability of the professional drivers for obstacle overtaking event is plotted as shown in Fig. 11a using model result from Table 6. Accident probability of professional drivers is 0.47 %, 2.89 %, and 9.78 % when a driver applies 40 N BPF under NTP, LTP, and HTP respectively (Fig. 11a). Thus, professional drivers can counter the augmented accident risk by increasing the BPF by 78.33 % and 131.43 % under LTP and HTP driving conditions as shown in Fig. 11a. Fig. 11b shows the accident probability of the non-professional drivers for obstacle overtaking event under NTP, LTP, and HTP driving conditions. At 40 N BPF, accident probability of the drivers is 3.75 %, 19.77 %, and 47.25 % under NTP, LTP, and HTP driving conditions respectively. In the current situation, non-professional drivers should apply 82.75 % and 135.85 % more BPF under LTP and HTP to reduce their accident probability equal to NTP driving condition. Non11

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Fig. 10. (a) Accident probability of female drivers for three different driving conditions; (b) Required increment in BPF and reduction in BTMB to reduce accident risk of female drivers for pedestrian crossing event in NTP, LTP, and HTP driving conditions.

professional drivers can decrease the increased risk due to additional hassle equivalent to professional driver’s baseline accident risk by applying 91.6 %, 166.83 %, and 219.93 % more BPF in NTP, LTP, and HTP driving conditions respectively.

awareness exhibited by the driver and superior driving skill due to the experience acquired over the years (Kassaagi et al., 2003; Lee et al., 2011; Roody, 2011). Further, drivers with accident history are observed to attain maximum deceleration earlier than the drivers without any accident history. It may be because of cautious driving behaviour due to aggravated risk perception of the drivers with accident history (Gooden et al., 2019; Pérez-Marín et al., 2019). Female drivers are observed to apply high BPF (Ferrante et al., 2020), however, required more BTMB to achieve maximum deceleration compared to male drivers to compensate the increased risk experienced by them in pedestrian crossing event. This finding clearly indicates that drivers who require more time to achieve maximum deceleration are observed to apply high BPF (Lee et al., 2002). Further, it is reported that time pressure has a negative impact on female participants where a drastic drop is observed in their performance (De Paola and Gioia, 2014). The current study showed high accident probability of female drivers than male drivers in time pressure driving conditions where the accident risk increased by 2.67 times and 4.68 times in LTP and HTP respectively in pedestrian crossing event. Further, driving profession showed a significant effect on BPF and accident probability of the drivers. Professional drivers are observed to apply high BPF than non-professional drivers which resulted in lower risk of accident than non-professional drivers. This can be attributed to the

6. Discussion The random parameters Tobit model results showed that BPF increased by 10.02 N and 3.28 N in LTP and 20.67 N and 16.31 N in HTP driving conditions for pedestrian crossing and obstacle overtaking events respectively. Further, duration analysis results showed abrupt braking in time pressure compared to baseline driving condition where BTMB reduced by 13 % and 10 % in LTP and 23 % and 21 % in HTP conditions during pedestrian crossing and obstacle overtaking events respectively. In case of pedestrian crossing and obstacle overtaking events, approach speed significantly increased braking force of the drivers (Lee and LaVoie, 2014). It is generally observed that stopping distance of the drivers increases with driving speed for a constant BPF applied by the drivers (Zulhilmi et al., 2019) and thus, drivers are required to apply high BPF to reduce the speed and stop the vehicle to avert an accident. It is observed that BTMB reduced with increase in driver’s age in pedestrian crossing event. This fact may be attributed to the overall 12

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driving under time pressure increases the accident risk by 1.84–4.68 times compared to baseline condition under different traffic situations (pedestrian crossing and obstacle overtaking events). The different studies on time pressure showed that high-speed driving will not ensure drivers reaching the destination on or before the limited time (Bertola et al., 2012). Moreover, it is often observed that drivers underestimate the available time to complete their journey and thus, drive fast which results in violation of most of the traffic rules and regulations (Peer, 2011, 2010). The questionnaire survey revealed that 95.30 % of the drivers are not penalized or ticketed for over-speeding which highlights the fact that traffic rules are lenient (Maji et al., 2018) and gives additional self-confidence to the drivers for high-speed driving. Therefore, strict enforcement of traffic rules and regulations is required from the authorities to avoid road-users from violating traffic rules, especially in India where drivers are often observed to disobey the traffic rules (Urie et al., 2016). 8. Research contribution The current study focusses on quantifying the influence of increasing time pressure and different driver characteristics (age, gender, etc.) on braking behaviour (BPF and BTMB) and its safety implications in averting an accident. To the author’s knowledge, this is the first study which considered the combined effect of BPF and BTMB in assessing the accident risk of the drivers under varying time pressure driving conditions. Further, the results obtained may benefit the FCW systems by considering the effects of time pressure on the braking performance of the drivers. The major research findings from the current study are: Fig. 11. Accident probability of (a) professional drivers and (b) non-professional drivers for obstacle overtaking event in NTP, LTP, and HTP time pressure driving conditions.

1 Drivers apply abrupt and aggressive braking with increase in time pressure; 2 Female drivers require more time than male drivers to achieve maximum deceleration which resulted in aggressive braking; 3 Drivers are required to apply swift and high BPF to effectively reduce the accident risk in time pressure driving conditions.

years of experience gained by professional drivers by driving under various conditions including time pressure (Adams-guppy and Guppy, 1995; Li et al., 2019; Salminen and Lähdeniemi, 2002). At present, researchers are developing Forward Collision Warning (FCW) systems to improve the safety of the drivers. The FCW system assumes driver’s reaction time with additional braking intensity time to alert the driver (McLaughlin, 2007; Wang et al., 2016a). Wang et al. (2016b) studied rear-end collision avoidance behaviour and reported that drivers require 0.92–1.82 seconds to achieve maximum deceleration. In the current study, under different time pressure conditions in both the events, 4–21 % and 41–95 % of the drivers are observed to attain maximum deceleration in 0.92 s and 1.82 s respectively. In HTP, all the drivers are observed to attain maximum deceleration within 2.4 s in both the events. Therefore, 2.4 s can be used as braking intensity as the additional time with reaction time of 1.48 s (Kusano and Gabler, 2012) in FCW system. The total time of 3.88 s can be used to develop an alarm system in intelligent vehicles for sudden events which are detected using forward-looking radar that continuously monitors all the traffic obstacles in front of the subject vehicle. Further, with precautionary alarm, the FCW system can indicate the required BPF (Wang et al., 2016b) based on the driving speed so that drivers can gradually stop the vehicle (Lee et al., 2002) with continuous braking to reduce the likelihood of accidents (Prynne and Martin, 1995).

9. Limitations and future scope The current study faced the following limitations: 1 The overall sample size of the study is relatively more compared to most of the studies, still, the age and gender balance is not achieved. Recruitment of old age drivers (age above 60 years) and more female drivers can lead to more generalized results. 2 There is a chance of learning effect on change in the driving behaviour of the drivers. But, the past literature suggests nominal impact of learning effect on simulator driving (Cooper et al., 2008; Shinar et al., 2005) where researchers observed that a minimum of 7 drives are required for each driver to get fully familiarized with the driving task designed in the simulator (Bertola et al., 2012). 3 The study is purely based on a driving simulator and thus it is required to validate the results. 4 Reaction time and transfer time of the drivers are important aspects which are not examined in time pressure condition and therefore, further extension of this work can be analysing the influence of time pressure on the change in the reaction time and transfer time of the drivers. 5 A study conducted by Wu et al. (2018) showed that crash warning system can be collaborated with connected vehicles to avoid rearend crashes. The future research can be focussed on analysing and enhancing the effectiveness of the alarm system with vehicle-tovehicle and vehicle-to-infrastructure applications of connected vehicle technology. 6 The future research work must consider the pedal feedback system and examine the effect of brake pedal feedback to develop brake assistance system to minimize aggressive braking of the drivers in

7. Conclusion The overall study highlights that driving under time pressure poses safety risk where accident probability increases with an increase in time constraint on the drivers. In general, most of the drivers are observed to drive above the posted speed limit and previous work on high-speed driving showed serious consequences, even leading to fatal crashes. Drivers under time pressure drive fast to compensate for the lost time (Fitzpatrick et al., 2017). The current study showed that high-speed 13

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different time pressure conditions (Lewis et al., 2010; Prakah-Asante and Rao, 2007). 7 The current study focused on examining the effect of time pressure under low traffic volume conditions. The future research work can examine the effect of time pressure on braking behaviour of the drivers under moderate to high traffic volume. 8 The study analysed pedestrian crossing and obstacle overtaking event in an urban environment. Future work can be performed considering different traffic events like traffic signals, animal crossing, parked vehicles, etc.

conception and design: Nishant M. Pawar, Rashmeet Kaur Khanuja, Pushpa Choudhary and Nagendra Rao Velaga; data collection: Nishant M. Pawar and Rashmeet Kaur Khanuja; analysis and interpretation of results: Nishant M. Pawar, Pushpa Choudhary, and Nagendra Rao Velaga; draft manuscript preparation: Nishant M. Pawar, Pushpa Choudhary and Nagendra Rao Velaga. All authors reviewed the results and approved the final version of the manuscript. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Author contributions The authors confirm contribution to the paper as follows: study Appendix A. Pilot Study Pedestrian crossing event

Pedestrian crossing event is extensively used by Haque and Washington (2015) and Choudhary and Velaga (2017a) in driving simulator study. Haque and Washington (2015) used 110 m distance and Choudhary and Velaga (2017a) used 130 m distance in their studies to analyse distraction effects on driving behaviour. In the current study, seven pedestrians were placed on the left side in the direction of the subject driver and were given varying walking speed ranging from 0.6 m/s to 1.63 m/s based on the study of Laxman et al. (2010). As the speed of the subject driver is difficult to predict, the event was designed for two different observed speed of the subject driver. The pedestrians were given a speed of 0.6 m/s–1.35 m/s if the subject driver had speed less than 20 m/s before the commencement of the event. The speed of the pedestrians was varied between 1 m/s–1.63 m/s if the subject driver had speed more than 20 m/s before the initiation of the event. 20 m/s speed was considered as the threshold value to vary pedestrians’ walking speed (0.6–1.35 m/s and 1–1.63 m/s). This was done because the subject driver with speed less than 20 m/s experienced dilemma, whether to stop or to accelerate and move ahead for the pedestrian’s walking speed of 0.6–1.35 m/s. The similar situation of the drivers was observed when their driving speed was more than or equal to 20 m/s for the pedestrians’ walking speed of 1–1.63 m/s. Firstly, a distance of 110 m was considered and a pilot study was performed. It was observed that the driver reached earlier at the point of the event and crossed the path of the intersection before all the pedestrians covered the whole road width of 14 m as shown in Fig. A1a. Then, the distance of 130 m was considered and it was observed that pedestrians covered three-fourth of the lane width with an opening of 3.5 m outermost

Fig. A1. Pedestrian crossing event considering (a) 110 m; (b) 130 m; (c).120 m 14

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lane width in the direction of subject driver as shown in Fig. A1b. Finally, the distance of 120 m was considered and it was observed that all the pedestrians covered the whole road width providing no space for the driver to move forward and thus, the driver had to stop and wait till all the pedestrians crossed the road as shown in Fig. A1c. Obstacle overtaking event Previously, Rendon-Velez et al. (2016) and Zhao et al. (2019) considered an obstacle overtaking event to study the physiological measures of the driver under time pressure driving condition and to examine emergency steering evasion assistance control system. For the current study, the whole event was designed based on the pilot study. The important conditions were finalized before developing the event: (a) The subject driver must stop before the obstacle and wait till all four vehicles cleared the adjacent lane parallel to the obstacle; (b) The waiting time due to blockage of the lane should not make the subject driver impatient when the traffic in the adjacent lane is passing the obstacle. These two conditions are extremely important while finalizing the event. The first condition is required because every driver will decelerate when he/she observes the obstacle on the lane. The critical situation arises when the driver has to decide whether he/she has to stop before the obstacle or slowly approach the obstacle and expect the traffic to clear the lane so that the driver can move freely. The second condition is important because if the waiting time is more, then the driver will get frustrated and impatient. Thus, the results obtained cannot be exclusively based on time pressure and might be due to the influence of frustration as well as impatience. Thus, based on these two conditions, the whole event is developed. During the pilot study, it was observed that the subject driver travelled with speed ranging from 20 m/s to 24 m/s due to free-flow traffic conditions. Therefore, on average, subject drivers required 4–5 seconds (considering their deceleration after noticing the traffic in the opposite lane) to reach the obstacle. Four vehicles were considered to design the event. The first vehicle was placed at the beginning of the obstacle. Then, with a 20-meter gap between the vehicles, other three vehicles were placed. The last vehicle (fourth vehicle) required approximately 4.1 s to overtake the obstacle. The whole event is designed by considering the two aforementioned points.

Fig. A2. (a) Driver approaching the obstacle. (b) Driver starts decelerating and slowly approaches the obstacle. (c) Driver stops before the obstacle. (d) Driver waits till the lane opposite to the obstacle is free from traffic. 15

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In the beginning, a distance of 120 m was considered to trigger the traffic placed in the opposite lane of the obstacle. All four vehicles moved with 60 km/h speed when the subject driver was 120 m away from the event. It was observed that all the four vehicles passed the obstacle before the arrival of the subject driver near to the obstacle and the lane opposite to the obstacle was free from traffic and thus the subject driver overtook the obstacle without any difficulty. Then, a distance of 100 m was chosen to trigger the event. Again, the pilot study was performed and it was observed that the three vehicles already passed the obstacle and one vehicle was still in the lane, opposite to the obstacle. The driver decelerated because of the traffic, but the situation did not demand the driver to stop and wait before the obstacle until the lane was free from traffic. The driver slowly approached the obstacle and accelerated as soon as the traffic cleared the opposite lane. Finally, a distance of 90 m was chosen to trigger the event. It was observed that all the four vehicles started moving when the subject driver was 90 m away from them as shown in Fig. A2a. One vehicle passed the obstacle when the subject driver reached the obstacle (Fig. A2b). The subject driver had to stop the vehicle and wait for the vehicles to clear the lane, opposite to the obstacle (Fig. A2c and d). This situation fulfils both the conditions framed before developing the event. Therefore, a distance of 90 m was finalized for the data collection.

appreciate relevance of initial speed. Accid. Anal. Prev. 41, 10–14. https://doi.org/ 10.1016/j.aap.2008.07.013. Gooden, J.R., Ponsford, J.L., Charlton, J.L., Ross, P., Marshall, S., Gagnon, S., Bédard, M., Stolwyk, R.J., 2019. Self-regulation upon return to driving after traumatic brain injury. Neuropsychol. Rehabil. 29, 92–106. https://doi.org/10.1080/09602011.2016. 1261716. Grundy, C., Steinbach, R., Edwards, P., Green, J., Armstrong, B., Grundy, C., Steinbach, R., Edwards, P., Green, J., Wilkinson, P., 2009. The effect of 20 mph traffic speed zones on road injuries in London, 1986-2006 : a controlled interrupted time series analysis. Bmj 339, b4469. Haque, M., Washington, S., 2015. The Impact of Mobile Phone Distraction on the Braking Behaviour of Young Drivers : a Hazard-based Duration Model. 50. pp. 13–27. https://doi.org/10.1016/j.trc.2014.07.011. Hart, S.G., Staveland, L.E., 1988. Development of NASA-TLX (Task Load Index): results of empirical and theoretical research. Adv. Psychol. 52, 139–183. Haskell, W.L., Lee, I.M., Pate, R.R., Powell, K.E., Blair, S.N., Franklin, B.A., MacEra, C.A., Heath, G.W., Thompson, P.D., Bauman, A., 2007. Physical activity and public health: Updated recommendation for adults from the American College of Sports Medicine and the American Heart Association. Circulation 116, 1081–1093. https://doi.org/ 10.1249/mss.0b013e3180616b27. International Transport Forum, 2018. Road Safety Annual Report 2018. Kassaagi, M., Brissart, Guillaume, Popieul, J., 2003. A study on driver behavior during braking on open road. Proc. Int. Tech. Conf. Enhanc. Saf. Veh. 2003 11-p. Kusano, K.D., Gabler, H.C., 2012. Safety benefits of forward collision warning, brake assist, and autonomous braking systems in rear-end collisions. IEEE trans. Intell. Transp. Syst. 13, 1546–1555. https://doi.org/10.1109/TITS.2012.2191542. Lakens, D., 2013. Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs. Front. Psychol. 4, 863. https://doi.org/10. 3389/fpsyg.2013.00863. Laxman, K.K., Rastogi, R., Chandra, S., 2010. Pedestrian Flow Characteristics in Mixed Traffic Conditions. J. Urban Plan. Dev. 136, 23–33. Lee, J.D., Mcgehee, D.V., Brown, T.L., Reyes, M.L., 2002. Collision warning timing, driver distraction, and driver response to imminent rear-end collisions in a high-fidelity driving simulator. Hum. Factors 44, 314–334. Lee, Y.-C., LaVoie, N., Lauper, U., Cianciolo, A., 2011. Use of a simulator to objectively distinguish behaviors between low-risk and high-risk drivers. Proc. Sixth Int. Driv. Symp. Hum. Factors Driv. Assessment, Train. Veh. Des. 417–423. Lee, Y.C., LaVoie, N., 2018. Instruction-prompted objective behaviors as proxy for subjective measures in a driving simulator. Transp. Res. Part F Traffic Psychol. Behav. 55, 58–66. https://doi.org/10.1016/j.trf.2018.03.002. Lee, Y.C., LaVoie, N., 2014. Relationship between frustration justification and vehicle control behaviors - A simulator study. Proc. Hum. Factors Ergon. Soc. 2235–2239. https://doi.org/10.1177/1541931214581429. 2014-Janua. Lewis, D.J., Russell, J.D., Tuttle, C.S., 2010. Driver feedback to improve vehicle performance. U.S. Pat. 7,798,578. Li, X., Oviedo-Trespalacios, O., Rakotonirainy, A., Yan, X., 2019. Collision risk management of cognitively distracted drivers in a car-following situation. Transp. Res. Part F Traffic Psychol. Behav. 60, 288–298. https://doi.org/10.1016/j.trf.2018.10.011. Lindgren, A., Chen, F., Jordan, P.W., Zhang, H., 2008. Requirements for the design of advanced driver assistance systems - the differences between Swedish and Chinese drivers. Int. J. Des. 2, 41–54. Lloyd, D., Wilson, D., Mais, D., Deda, W., Bhagat, A., 2015. Reported Road Casualties Great Britain: 2014. Mahajan, K., Velaga, N.R., Kumar, A., Choudhary, P., 2019. Effects of driver sleepiness and fatigue on violations among truck drivers in India. Int. J. Inj. Contr. Saf. Promot. 1–11. https://doi.org/10.1080/17457300.2019.1660375. Maji, A., Velaga, N.R., Urie, Y., 2018. Hierarchical clustering analysis framework of mutually exclusive crash causation parameters for regional road safety strategies. Int. J. Inj. Contr. Saf. Promot. 25, 257–271. https://doi.org/10.1080/17457300.2017. 1416485. McKenna, F.P., 2005. What shall we do about speeding-education? Int. Conf. Traffic Transp. Psychol. McLaughlin, S.B., 2007. Analytic Assessment of Collision Avoidance Systems and Driver Dynamic Performance in Rear-end Crashes and Near-crashes. Dr. Diss. Virginia Tech. Ministry of Road Transport & Highways, 2018. Road Accidents in India - 2017. https:// doi.org/10.1016/S0386-1112(14)60239-9. Naveteur, J., Cœugnet, S., Charron, C., Dorn, L., 2013. Impatience and time pressure : Subjective reactions of drivers in situations forcing them to stop their car in the road. Transp. Res. Part F Psychol. Behav. 18, 58–71. https://doi.org/10.1016/j.trf.2012.

References Aarts, L., Van Schagen, I., 2006. Driving speed and the risk of road crashes : a review. Accid. Anal. Prev. 38, 215–224. https://doi.org/10.1016/j.aap.2005.07.004. Adams-guppy, J.R., Guppy, A., 1995. Speeding in relation to perceptions of risk, utility and driving style by British company car drivers. Ergonomics 38, 2525–2535. https://doi.org/10.1080/00140139508925284. Anastasopoulos, P.C., Mannering, F.L., Shankar, V.N., Haddock, J.E., 2012. A study of factors affecting highway accident rates using the random-parameters tobit model. Accid. Anal. Prev. 45, 628–633. https://doi.org/10.1016/j.aap.2011.09.015. Anastasopoulos, P.C., Tarko, A.P., Mannering, F.L., 2008. Tobit analysis of vehicle accident rates on interstate highways. Accid. Anal. Prev. 40, 768–775. https://doi.org/ 10.1016/j.aap.2007.09.006. Beck, K.H., Daughters, S.B., Ali, B., 2013. Hurried driving : Relationship to distress tolerance, driver anger, aggressive and risky driving in college students. Accid. Anal. Prev. 51, 51–55. https://doi.org/10.1016/j.aap.2012.10.012. Bella, F., Silvestri, M., 2016. Driver’s braking behavior approaching pedestrian crossings : a parametric duration model of the speed reduction times. J. Adv. Transp. 50, 630–646. https://doi.org/10.1002/atr.1366. Bertola, M.A., Balk, S.A., Shurbutt, J., 2012. Evaluating Driver Performance on Rural Two-Lane Horizontal Curved Roadways Using a Driving Simulator. https://doi.org/ 10.1002/aic. Choudhary, P., Velaga, N.R., 2019. Effects of phone use on driving performance: a comparative analysis of young and professional drivers. Saf. Sci. 111, 179–187. https://doi.org/10.1016/j.ssci.2018.07.009. Choudhary, P., Velaga, N.R., 2018a. Performance degradation during sudden hazardous events: a comparative analysis of use of a phone and a music player during driving. IEEE trans. Intell. Transp. Syst. 1–11. https://doi.org/10.1109/TITS.2018.2879968. Choudhary, P., Velaga, N.R., 2018b. Effects of texting on accident risk during a sudden hazardous event : analysis of predetection and postdetection phases. Traffic Inj. Prev. 19, 806–811. https://doi.org/10.1080/15389588.2018.1517237. Choudhary, P., Velaga, N.R., 2017a. Analysis of vehicle-based lateral performance measures during distracted driving due to phone use. Transp. Res. Part F Traffic Psychol. Behav. 44, 120–133. Choudhary, P., Velaga, N.R., 2017b. Mobile phone use during driving : Effects on speed and effectiveness of driver compensatory behaviour. Accid. Anal. Prev. 106, 370–378. https://doi.org/10.1016/j.aap.2017.06.021. Choudhary, P., Velaga, N.R., 2017c. Modelling driver distraction effects due to mobile phone use on reaction time. Transp. Res. Part C Emerg. Technol. 77, 351–365. https://doi.org/10.1016/j.trc.2017.02.007. Cœugnet, S., Miller, H., Anceaux, F., Naveteur, J., 2013a. How do time pressured drivers estimate speed and time? Accid. Anal. Prev. 55, 211–218. https://doi.org/10.1016/j. aap.2013.02.040. Cœugnet, S., Naveteur, J., Antoine, P., Anceaux, F., 2013b. Time pressure and driving : work, emotions and risks. Transp. Res. Part F Psychol. Behav. 20, 39–51. https://doi. org/10.1016/j.trf.2013.05.002. Cohen, J.E., 1988. Statistical Power Analysis for the Behavioral Sciences. Cooper, J.M., Strayer, D.L., City, S.L., 2008. Effects of simulator practice and real-world experience on cell-phone – related driver distraction. Hum. Factors 50, 893–902. https://doi.org/10.1518/001872008X374983. Dogan, E., Steg, L., Delhomme, P., 2011. The influence of multiple goals on driving behavior: the case of safety, time saving, and fuel saving. Accid. Anal. Prev. 43, 1635–1643. https://doi.org/10.1016/j.aap.2011.03.002. Farah, H., Bekhor, S., Polus, A., 2009. Risk evaluation by modeling of passing behavior on two-lane rural highways. Accid. Anal. Prev. 41, 887–894. https://doi.org/10.1016/j. aap.2009.05.006. Ferrante, C., Varladi, V., De Blasiis, M.R., 2020. Gender differences measured on driving performances in an urban simulated environment. Adv. Hum. Factors Simulation. AHFE 2019. Adv. Intell. Syst. Comput. 958, 144–156. https://doi.org/10.1007/9783-030-20148-7_14. Fitzpatrick, C.D., Samuel, S., Knodler, M.A., 2017. The use of a driving simulator to determine how time pressures impact driver aggressiveness. Accid. Anal. Prev. 108, 131–138. https://doi.org/10.1016/j.aap.2017.08.017. Fuller, R., Bates, H., Gormley, M., Hannigan, B., Stradling, S., Broughton, P., Kinnear, N., Dolan, C.O., 2008. The Conditions for Inappropriate High Speed : a Review of the Research Literature From 1995 to 2006. Report Under Contract Number PPRO. Fuller, R., Gormley, M., Stradling, S., Broughton, P., Kinnear, N., Dolan, C.O., Hannigan, B., 2009. Impact of speed change on estimated journey time : failure of drivers to

16

Accident Analysis and Prevention 136 (2020) 105401

N.M. Pawar, et al.

Technol. 7, 75–90. Stradling, S.G., 2005. Speeding Behaviour and Collision Involvement in Scottish Car Drivers. Contemp. Issues Road User Behav. Nov. Sci. Publ. Inc. Svenson, O., 2009. Driving speed changes and subjective estimates of time savings, accident risks and braking. Appl. Cogn. Psychol. Off. J. Soc. Appl. Res. Mem. Cogn. 23, 543–560. https://doi.org/10.1002/acp.1471. Svenson, O., Benson, L., 1993. Framing and Time Pressure in Decision Making. Time Press. Stress Hum. Judgm. Decis. Mak., pp. 133–144. https://doi.org/10.1007/978-14757-6846-6_9. Szumilas, M., 2010. Explaining odds ratios. J. Can. Acad. Child Adolesc. Psychiatry 19, 227–229. Techer, F., Ojeda, L., Barat, D., Marteau, J., Rampillon, F., Feron, S., Dogan, E., 2019. Anger and highly automated driving in urban areas : the role of time pressure. Transp. Res. Part F Psychol. Behav. 64, 353–360. https://doi.org/10.1016/j.trf.2019. 05.016. Thiele, J., Markussen, B., 2012. Potential of GLMM in modelling invasive spread. CAB Rev. 016, 1–10. https://doi.org/10.1079/PAVSNNR20127016. U.S. Department of Health and Human Services, 2011. Your Guide to Healthy Sleep. US Dep. Heal. Hum. Serv. 72. Urie, Y., Velaga, N.R., Maji, A., 2016. Cross-sectional study of road accidents and related law enforcement efficiency for 10 countries : a gap coherence analysis. Traffic Inj. Prev. 17, 686–691. https://doi.org/10.1080/15389588.2016.1146823. Walker, I., 2007. Null Hypothesis Testing and Effect Sizes [WWW Document]. Stat. Psychol.. URL https://people.bath.ac.uk/pssiw/stats2/page2/page2.html (accessed 11.4.19). Wang, X., Chen, M., Zhu, M., Tremont, P., 2016a. Development of a Kinematic-Based Forward Collision Warning Algorithm Using an Advanced Driving Simulator. IEEE trans. Intell. Transp. Syst. 17, 2583–2591. https://doi.org/10.1109/TITS.2016. 2528508. Wang, X., Zhu, M., Chen, M., Tremont, P., 2016b. Drivers’ rear end collision avoidance behaviors under different levels of situational urgency. Transp. Res. Part C Emerg. Technol. 71, 419–433. https://doi.org/10.1016/j.trc.2016.08.014. Washington, S.P., Karlaftis, M.G., Mannering, F., 2011. Statistical and Econometric Methods for Transportation Data Analysis. Weyman, A., Clarke, D.D., Cox, T., 2003. Developing a factor model of coal miners’ attributions on risk-taking at work. Work Stress 17, 306–320. https://doi.org/10.1080/ 02678370310001646844. Wickens, C.D., Lee, J., Liu, Y., Becker, S.G., 1998. An Introduction to Human Factors Engineering (Second Edition). World Health Organisation, 2018. Global Status Report on Road Safety 2018. World Health Organisation, 2008. Speed Management: a Road Safety Manual for Decision-makers and Practitioners. Wu, Y., Abdel-Aty, M., Park, J., Zhu, J., 2018. Effects of crash warning systems on rearend crash avoidance behavior under fog conditions. Transp. Res. Part C Emerg. Technol. 95, 481–492. https://doi.org/10.1016/j.trc.2018.08.001. Yadav, A.K., Velaga, N.R., 2019a. Effect of alcohol use on accelerating and braking behaviors of drivers. Traffic Inj. Prev. 20, 353–358. https://doi.org/10.1080/ 15389588.2019.1587167. Yadav, A.K., Velaga, N.R., 2019b. Modelling the relationship between different Blood Alcohol Concentrations and reaction time of young and mature drivers. Transp. Res. Part F Traffic Psychol. Behav. 64, 227–245. https://doi.org/10.1016/j.trf.2019.05. 011. Zhao, Z., Zhou, L., Luo, Y., Li, K., 2019. Emergency steering evasion assistance control based on driving behavior analysis. IEEE trans. Intell. Transp. Syst. 20, 457–475. https://doi.org/10.1109/TITS.2018.2814687. Zulhilmi, I.M., Peeie, M.H., Eiman, R.I.M., Izhar, I.M., Asyraf, S.M., 2019. Investigation on vehicle dynamic behaviour during emergency braking at different speed. Int. J. Automot. Mech. Eng. 16, 6161–6172.

12.008. Nilsson, G., 2004. Traffic Safety Dimensions and the Power Model to Describe the Effect of Speed on Safety (Doctoral Dissertation, Univ.). Palat, B., Delhomme, P., 2016. A simulator study of factors influencing drivers’ behavior at traffic lights. Transp. Res. Part F Psychol. Behav. 37, 107–118. https://doi.org/10. 1016/j.trf.2015.11.009. De Paola, M., Gioia, F., 2014. Who performs better under time pressure? Results from a field experiment who performs better under time pressure? results from a field experiment. Inst. Study Labor 53, 37–53. Parker, D., Reason, J.T., Manstead, A.S.R., Stradling, S.G., 1995. Driving errors, driving violations and accident involvement. Ergonomics 38, 1036–1048. https://doi.org/10. 1080/00140139508925170. Paschalidis, E., Choudhury, C.F., Hess, S., 2019. Combining driving simulator and physiological sensor data in a latent variable model to incorporate the effect of stress in car-following behaviour. Anal. Methods Accid. Res. 22, 100089. https://doi.org/10. 1016/j.amar.2019.02.001. Paschalidis, E., Choudhury, C.F., Hess, S., 2018. Modelling the effects of stress on gapacceptance decisions combining data from driving simulator and physiological sensors. Transp. Res. Part F Traffic Psychol. Behav. 59, 418–435. https://doi.org/10. 1016/j.trf.2018.09.019. Peer, E., 2011. The time-saving bias, speed choices and driving behavior. Transp. Res. Part F Psychol. Behav. 14, 543–554. https://doi.org/10.1016/j.trf.2011.06.004. Peer, E., 2010. Speeding and the time-saving bias : how drivers’ estimations of time saved in higher speed affects their choice of speed. Accid. Anal. Prev. 42, 1978–1982. https://doi.org/10.1016/j.aap.2010.06.003. Pérez-Marín, A.M., Ayuso, M., Guillen, M., 2019. Do young insured drivers slow down after suffering an accident? Transp. Res. Part F Traffic Psychol. Behav. 62, 690–699. https://doi.org/10.1016/j.trf.2019.02.021. Prakah-Asante, K.O., Rao, M.K., Llc, F.G.T., 2007. Vehicle pre-impact sensing and control system with driver response feedback. U.S. Pat. 7,253,724. Prynne, K., Martin, P., 1995. Braking Behaviour in Emergencies. SAE Tech. Pap. Rendon-velez, E., 2012. A pilot study to investigate time pressure as a surrogate of being in haste. Proc. Ninth Int. Symp. Tools Methods Compet. Eng. 393–406. Rendon-Velez, E., van Leeuwen, P.M., Happee, R., Horváth, I., van der Vegte, W.F., de Winter, J.C.F., 2016. The effects of time pressure on driver performance and physiological activity: a driving simulator study. Transp. Res. Part F Traffic Psychol. Behav. 41, 150–169. https://doi.org/10.1016/j.trf.2016.06.013. Rendon-velez, E., Vegte, I.H., van Der, W.W.F., 2011. Identifying indicators of driving in a hurry. ASME 2011 Int. Mech. Eng. Congr. Expo. Am. Soc. Mech. Eng. 111–125. Roody, S.S., 2011. Modeling drivers’ behavior during panic braking for brake assist application, using neural networks and logistic regression and a comparison. SAE Tech. Pap. https://doi.org/10.4271/2011-01-2384. 2011-01-23. Salminen, S., Lähdeniemi, E., 2002. Risk factors in work-related traffic. Transp. Res. Part F Traffic Psychol. Behav. 5, 77–86. https://doi.org/10.1016/S1369-8478(02) 00007-4. Schechtman, E., Bar-gera, H., Musicant, O., 2016. Driver views on speed and enforcement. Accid. Anal. Prev. 89, 9–21. https://doi.org/10.1016/j.aap.2015.12.028. Schmidt-daffy, M., 2013. Fear and anxiety while driving : differential impact of task demands, speed and motivation. Transp. Res. Part F Psychol. Behav. 16, 14–28. https://doi.org/10.1016/j.trf.2012.07.002. Schroeder, P., Kostyniuk, L., Mack, M., 2011. 2011 National Survey Of Speeding Attitudes And Behaviors. Shinar, D., Tractinsky, N., Compton, R., 2005. Effects of practice, age, and task demands, on interference from a phone task while driving. Accid. Anal. Prev. 37, 315–326. https://doi.org/10.1016/j.aap.2004.09.007. Sigrist, F., Hirnschall, C., 2019. Grabit: gradient tree-boosted Tobit models for default prediction. J. Bank. Financ. 102, 177–192. https://doi.org/10.1016/j.jbankfin.2019. 03.004. Stern, E., 1999. Reactions to congestion under time pressure. Transp. Res. Part C Emerg.

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