Advancing proactive crash prediction: A discretized duration approach for predicting crashes and severity.

Driven by advancements in data-driven methods, recent developments in proactive crash prediction models have primarily focused on implementing machine learning and artificial intelligence. However, from a causal perspective, statistical models are preferred for their ability to estimate effect sizes using variable coefficients and elasticity effects. Most statistical framework-based crash prediction models adopt a case-control approach, matching crashes to non-crash events. However, accurately defining the crash-to-non-crash ratio and incorporating crash severities pose challenges. Few studies have ventured beyond the case-control approach to develop proactive crash prediction models, such as the duration-based framework. This study extends the duration-based modeling framework to create a novel framework for predicting crashes and their severity. Addressing the increased computational complexity resulting from incorporating crash severities, we explore a tradeoff between model performance and estimation time. Results indicate that a 15 % sample drawn at the epoch level achieves a balanced approach, reducing data size while maintaining reasonable predictive accuracy. Furthermore, stability analysis of predictor variables across different samples reveals that variables such as Time of day (Early afternoon), Weather condition (Clear), Lighting condition (Daytime), Illumination (Illuminated), and Volume require larger samples for more accurate coefficient estimation. Conversely, Daytime (Early morning, Late morning, Late afternoon), Lighting condition (Dark lighted), Terrain (Flat), Land use (Commercial, Rural), Number of lanes, and Speed converge towards true estimates with small incremental increases in sample size. The validation reveals that the model performs better in highway segments experiencing more frequent crashes (segments where the duration between crashes is less than 100 h, or approximately 4 days).

Analysis of speed reductions and crash risk of aggressive drivers during emergent pre-crash scenarios at unsignalized intersections.

Aggressive driver behavior (ADB) is often linked with road crashes, especially during crash imminent situations. Previous studies demonstrated that ADB was positively correlated with collision risk; however, this relationship has not quantified evidently. This study aimed to analyze drivers' collision risk and speed reduction behavior during an emergent pre-crash scenario (such as a conflict encroaching into an unsignalized intersection at different critical time gaps) using a driving simulator. The effect of ADB on crash risk is investigated using the time to collision (TTC). Further, drivers' collision evasive behavior is analyzed using speed reduction time (SRT) survival probabilities. Fifty-eight Indian drivers are identified as aggressive, moderately aggressive, and, non-aggressive based on aggressive indicators such as vehicle kinematics (percentage of the time spent in speeding and rapid accelerations, maximum brake pressure, etc.). Two separate models are built to analyze ADB effects on TTC and SRT using a Generalized Linear Mixed Model (GLMM) and a Weibull Accelerated Failure Time (AFT) model, respectively. From the results, it can be observed that aggressive drivers' TTC and SRT are reduced by 82% and 38%, respectively. Compared to a 7 sec conflict approaching time gap, TTC is reduced by 18%, 39%, 51%, and 58% for 6 sec, 5 sec, 4 sec, and 3 sec conflict approaching time gaps, respectively. The estimated SRT survival probabilities for aggressive, moderately aggressive and non-aggressive drivers are 0%, 3% and 68% at 3 sec of conflict approaching time gap, respectively. SRT survival probability increased by 25% for matured drivers and decreased by 48% for drivers who tend to engage in frequent speeding. Important implications of the study findings are discussed.

Investigating the role of beliefs in influencing the hand-held and hands-free mobile phone use among pedestrians in India.

Mobile phone distraction is a significant contributor to pedestrian injuries. However, mobile phone engagement among pedestrians has been scarcely explored in a developing country like India. The present study utilized the beliefs-based theory of planned behaviour to examine the association between pedestrian beliefs towards distracted walking (behavioural, normative, and control) and their mobile phone use frequencies. Based on a survey of 560 pedestrians (64.6% males), it was found that the major use of mobile phones was for listening to music (30.7%), followed by receiving a call (25%), making a call (18.9%), texting (9.8%), navigation (8.5%) and internet browsing (7.1%). A series of multivariate ANOVAs and logistic regression models were developed to investigate the relationships between the beliefs and frequencies of mobile phone use in hands-free and hand-held conditions. Significant multivariate differences were found for behavioural and normative beliefs in hands-free conditions and all three types of beliefs in hand-held conditions. The frequency of mobile phone use was significantly predicted by normative beliefs (p < 0.001) in the hands-free condition, and by behavioural (p = 0.041) and normative beliefs (p = 0.004) in the hand-held condition. The findings may assist the road safety countermeasures in addressing the issue of pedestrian distraction.

A comparative assessment of subjective experience in simulator and on-road driving under normal and time pressure driving conditions.

This study conducts a comparative assessment of subjective experience of real-world and simulated world driving for investigating factors leading to simulator sickness. Thirty professional car drivers drove a fixed-base driving simulator in real and simulated worlds under No Time Pressure (NTP) and Time Pressure (TP) driving conditions. Drivers rated their perceptions based on real-world driving and simulator driving experiences after each driving session with respect to three factors: simulator sickness, mental workload, and sense of presence. The structural equation model results revealed that drivers experienced high mental workload due to TP driving conditions (factor loading = 0.90) and repeated exposure to simulated world (factor loading = 0.20) which induced simulator sickness (factor loading = 0.41) and resulted in low sense of presence (factor loading = -0.18). Thus, it can be concluded that lack of experience with virtual reality induced high simulator sickness, increased mental workload, and low sense of presence.

Modelling work- and non-work-based trip patterns during transition to lockdown period of COVID-19 pandemic in India.

COVID-19 pandemic has significantly affected the transportation sector across the world. Implementation of lockdown (that includes restricted travel activities) is a prevention strategy executed by various governments to minimize the spread of COVID-19. India went into complete lockdown from 25th March 2020; however, change in commuter's travel behavior was observed from the third week of March (termed as transition to lockdown) due to pandemic fear. In total 1945 participants participated in the travel behaviour survey and their responses with respect to work-based and non-work-based trips during transition period were analysed to understand their adaptation towards COVID-19. The study also attempted to quantify the effects of influencing factors which can explain change in the commuters' travel behaviour. The findings revealed that one-year increment in traveller's age had 2% reduced probability of no travel during transition than pre-transition. For non-work-related travel, chances of lower travel frequency were significantly greater during the transition period as compared to pre-transition. Compared to the non-essential trips, the chances of reduced travel frequency for the essential trips were found to be lower by 92%. By examining these behavioural changes, the present study aims to assist the policymakers in understanding the dynamics of fluctuating travel demand with respect to trip purpose during pandemic situations like COVID-19.

Are you willing to drink and drive? An investigation in Indian scenario using an extended prototype willingness model.

OBJECTIVE: Driving While Intoxicated (DWI) is a significant threat to traffic safety worldwide, but little is known about the motivational factors behind the willingness to drink and drive especially in low- and middle-income countries (LMICs) such as India. The present study applied an extended version of prototype willingness model (PWM) to investigate the factors predicting the willingness to drink and drive. METHODS: One hundred and forty-three participants (77% males) responded to an online survey in India. The survey included the standard PWM constructs such as attitudes, subjective norms, prototype similarity and prototype favourability. Additionally, the measures of traffic fatalism and risk-perceptions were also incorporated to examine their effects on the willingness to drink and drive. RESULTS: The hierarchical regression model showed that the PWM constructs explained 84% of the variance in willingness to drink and drive. When the additional measures (traffic fatalism and risk perception) were included along with the PWM variables, it resulted in an increase of additional 2% of the variance in explaining the willingness, thereby leading to a total of 86%. In both the steps, the variable "attitudes" was found to be the strongest predictor of willingness to drink and drive. CONCLUSIONS: This study is first of its kind to evaluate the effectiveness of PWM model in Indian scenario for understanding the willingness to engage in the act of drunk driving. The study findings may prove useful to the stakeholders of educational and awareness programs, where the focus is required to be aimed at attitudes, subjective norms, prototype similarity and fatalistic beliefs of the drivers.

Analysis of factors influencing aggressive driver behavior and crash involvement.

OBJECTIVE: Aggressive driver behavior is one of the major contributing factors to road crashes. However, the relationship between aggressive driver behavior and crash risk is scarcely explored. The present study focused on quantifying the effect of aggressive driver behavior on crash probability. METHOD AND DATA SOURCES: A sample of 405 Indian drivers were analyzed to model the aggressive driver behavior using self-reported measures. Generalized linear models were developed to quantify the effects of independent variables such as age, gender, personality traits (e.g., driving anger, physical aggression, hostility), and individual predilections to commit violations (e.g., excessive speeding and frequent risky overtaking) on aggressive driver behavior and crash probabilities. RESULTS: K-means clustering technique was applied to the Aggressive Driving Scale (ADS) scores to cluster the drivers into three groups (aggressive, normal, and cautious). Gender was significantly correlated with aggressive driver behavior. Compared to female drivers, male drivers were 2.57 times more likely to engage in aggressive driving. Driver's age was negatively correlated with aggressive driving. With one-year increment in driver's age, the tendency of a driver to engage in aggressive driving was reduced by 26%. In addition, the likelihood of being engaged in aggressive driving was increased by 2.98 times and 2.15 times for the drivers who engage in excessive speeding and frequent risky overtaking, respectively. Driver's personality traits were significantly correlated with aggressive drivers. The crash involvement model showed that aggressive drivers were 2.79 times more likely to be involved in road crashes than cautious drivers. Further, married drivers were 2.17 times less likely to be involved in crashes, whereas for professional drivers the crash involvement probability was increased by 75%. CONCLUSIONS: The results revealed that in addition to age and gender personality traits were significant predictors of driving aggression. Further, the driver's marital status was negatively correlated with the crash involvement and professional drivers were more likely to be involved in crashes than nonprofessional drivers. The study findings can be used in identifying specific risk-prone drivers to provide safety measures via in-vehicle Advanced Driver Assistance Systems (ADAS).

Modelling brake transition time of young alcohol-impaired drivers using hazard-based duration models.

Braking performance of drivers is a crucial factor in evaluating the collision patterns and implementing road safety measures. Further, alcohol is known to impair driving control. The present study aims to examine the influence of a comprehensive range of Blood Alcohol Concentration (BAC) levels (0%, 0.03 %, 0.05 % and 0.08 %) on brake transition times of drivers. As young drivers show significantly higher crash risks compared to the experienced drivers, fifty-four young Indian drivers in the age group of 21-25 years (forty males and fourteen females) participated in the driving simulator experiments. The study adopted the framework of a within-subjects design, where each driver encountered rural and urban driving scenarios in a counterbalanced order, during experimental driving at each of the four BAC levels. Their brake transition times were estimated with respect to sudden pedestrian crossing events. Weibull Accelerated Failure Time (AFT) models with shared frailty were developed for quantifying the effects of BAC levels along with driver attributes on brake transition time. Preliminary analysis showed significant main effects of BAC (p < 0.001) and driving environment (p = 0.002) on brake transition time; however, their interaction effect was not significant (p = 0.485). The models revealed that 0.03 %, 0.05 % and 0.08 % BACs significantly reduced the brake transition times by 16 %, 28 % and 52 % in rural driving environment, and by 23 %, 37 % and 53 % in urban driving environment, compared to 0% BAC. The study outcomes may find application in assisting collision warning systems which take into account the braking behaviour of drivers.

Modeling the influence of 0.03%, 0.05% and 0.08% blood alcohol concentrations on lane positioning and steering control of Indian drivers.

OBJECTIVE: Alcohol-impairment of drivers has significant influence on road traffic safety; however, no experimental research has been conducted on the lateral driving control of Indian drivers. Therefore, the present study investigates the effects of different Blood Alcohol Concentration (BAC) levels (0%, 0.03%, 0.05% and 0.08%) and driver attributes on the lane positioning and steering control of Indian drivers. METHOD: A driving simulator experiment was designed where 82 adult licensed drivers (62 males, 20 females) completed driving on the simulated urban arterial road environment under the influence of varying BACs. Lateral driving performance measures associated with lane positioning (lane position variability) and steering control of drivers (steering angle variability and steering reversal rates (SRR)) were analyzed. RESULTS: The findings reported that lane position variability was significantly influenced only at 0.08% BAC (5.8% increment); no significant influence was observed at 0.03% and 0.05% BACs. Compared to 0% BAC, steering angle variability increased by 0.105 degrees or 15.7%, 0.142 degrees or 21.4%, and 0.176 degrees or 25.7% at 0.03%, 0.05% and 0.08% BACs respectively. No significant differences were observed between the sober-state driving and alcohol-impaired driving at 1ο and 5ο SRR. However, 10ο SRR was found to be higher by 36%, 65% and 92% at 0.03%, 0.05% and 0.08% BACs compared to 0% BAC. Among the driver attributes, male drivers displayed higher impairment in lane positioning behavior as compared to female drivers. One-year increase in drivers' age reduced the lane position variability by 0.6%. Drivers who performed regular physical exercise were found to have better lateral control on the vehicle, as observed in their steering angle variability. Prior crash history had negative association with the steering reversal rates, indicating that drivers who have previous experience of crash involvement show better steering control than the drivers without any crash experience. CONCLUSION: In conclusion, the outcomes of this study provide novel insights into the alcohol-impaired lateral vehicle control of Indian drivers which can assist in policy interventions aiming to reduce crashes with alcohol as a major crash causation factor.

Sleep-deprived car-following: Indicators of rear-end crash potential.

Safety assessment among sleep-deprived drivers is a challenging research area with only a few sleep-related studies investigating safety performance during car-following. Therefore, this study aimed to measure the effects of partial sleep deprivation on driver safety during car-following. Fifty healthy male drivers with no prior history of any sleep-related disorders, drove the driving simulator in three conditions of varying sleep duration: a baseline (no sleep deprivation), test session (TS1) after one night of PSD (sleep ≤4.5 h/night) and TS2 after two consecutive nights of PSD. The reduced sleep in PSD sessions was monitored using an Actiwatch. Karolinska Sleepiness Scale was used to indicate loss of alertness among drivers. Each drive included a car-following task to measure longitudinal safety indicators based on speed and headway management: normalized time exposed to critical gap (TECG'), safety critical time headway and speed variability with respect to leading vehicle's speed (SPV). Crash potential index (CPI) was also determined from deceleration rate of drivers during car-following and was found correlated with other indicators. Therefore, to determine the aggregate influence of PSD on safety during car-following, CPI was modelled in terms of TECG, SPV, THW and other covariates. All safety metrics were modelled using generalized mixed effects regression models. The results showed that compared to the baseline drive, critical time headway decreased by 0.65 and 1.08 times whereas speed variability increased by 1.34 and 1.28 times during the TS1 and TS2, respectively, both indicating higher crash risk. However, decrease in TECG' by 64 % and 56 % during TS1 and TS2, respectively indicate compensatory measures to avoid risks due to sleep loss. A fractional regression model of crash potential revealed that low time-headway and higher speed variability and high time exposed to critical gap (TECG') significantly contribute to higher CPI values indicating higher safety risk. Other covariates such as sleep duration, professional driving experience and history of traffic violations were also associated with safety indicators and CPI, however no significant effects of age were noticed in the study. The study findings present the safety indicators sensitive to rear-end crashes specifically under PSD conditions, which can be used in designing collisions avoidance systems and strategies to improve overall traffic safety.

Exploring the effectiveness of a digital voice assistant to maintain driver alertness in partially automated vehicles.

OBJECTIVE: Vehicle automation shifts the driver's role from active operator to passive observer at the potential cost of degrading their alertness. This study investigated the role of an in-vehicle voice-based assistant (VA; conversing about traffic/road environment) to counter the disengaging and fatiguing effects of automation. METHOD: Twenty-four participants undertook two drives- with and without VA in a partially automated vehicle. Participants were subsequently categorized into high and low participation groups (based on their proportion of vocal exchanges with VA). The effectiveness of VA was assessed based on driver alertness measured using Karolinska Sleepiness Scale (KSS), eye-based sleepiness indicators and glance behavior, NASA-TLX workload rating and time to gain motor readiness in response to take-over request and performance rating made by the drivers. RESULTS: Paired samples t-tests comparison of alertness measures across the two drives were conducted. Lower KSS rating, larger pupil diameter, higher glances (rear-mirror, roadside vehicles and signals in the drive with VA) and higher feedback ratings of VA indicated the efficiency of VA in improving driver alertness during automation. However, there was no significant difference in alertness or glance behavior between the driver groups (high and low-PR), although the time to resume steering control was significantly lower in the higher engagement group. CONCLUSION: The study successfully demonstrated the advantages of using a voice assistant (VA) to counter these effects of passive fatigue, for example, by reducing the time to gain motor-readiness following a TOR. The findings show that despite the low engagement in spoken conversation, active listening also positively influenced driver alertness and awareness during the drive in an automated vehicle.

Investigating the effects of driving environment and driver characteristics on drivers' compliance with speed limits.

OBJECTIVE: Speed compliance of drivers plays a significant role in influencing crash risks, and remains a major road safety issue. Majority of the speed compliance behavioral studies have been conducted in the western world; relatively little knowledge is available about the speed compliance of the drivers of developing nations. The present study aims to investigate the speed compliance behavior of Indian drivers in changing driving environment, and to identify the significant predictors influencing their speed compliance. METHOD: The driving scenario (consisting of rural and urban driving environments) representing heterogenous Indian driving conditions was designed on a driving simulator where eighty-two licensed drivers completed the driving task. Driver attributes (demographics and driving characteristics) were recorded with the help of a self-reported questionnaire. Speed compliance of drivers was estimated as an indication of the difference of driving speed from the posted speed limit of a particular driving environment, averaged over the duration of driving. The low speed difference indicated better speed compliance of drivers and vice-versa. A Generalized Linear Model (GLM) was developed using speed difference as the dependent variable along with driving environments and driver attributes as predictor variables. RESULTS: Speed compliance of drivers was found to be better in urban driving environment compared to rural driving environment, indicating that drivers were less able to adapt to the corresponding speed limits in rural environment. Drivers' age was positively associated with the speed compliance. For male drivers, speed compliance was found to be lower than the female drivers. Compared to the drivers who studied till their secondary education, speed compliances of post-graduate and graduate drivers were found to be better. Moreover, driving experience was observed as a negative predictor of speed compliance. Drivers who had prior crash history were found to have better speed compliance compared to the other drivers. Other predictors such as vehicle type and preferred time of driving did not show any significant influence on the speed compliance of drivers. CONCLUSION: This is the first exploratory study which investigated the speed compliance behavior of Indian drivers. Findings of the present study may assist the road safety strategies and policy interventions in reducing the speed-related crashes.

Gender differences in driving control of young alcohol-impaired drivers.

BACKGROUND: Male and female drivers exhibit different degrees of vehicle control while driving under the influence of alcohol. However, this interaction between alcohol and gender is understudied. The present study examined the effects of different alcohol levels on the driving control of male and female drivers with the help of driving simulator experiments in heterogeneous traffic conditions. METHOD: Forty young drivers (20 males and 20 females) completed simulated driving at four Blood Alcohol Concentration (BAC) levels: 0% (control), 0.03%, 0.05% and 0.08%. Driving impairment in vehicle control was measured in terms of average speed, acceleration variability and reaction time of drivers. Repeated-measures ANOVA tests were conducted and regression models were developed for male and female drivers to quantify the effects of BAC levels and driver characteristics on the driving control measures. RESULTS: Significant effects of gender were observed for average speed (p < 0.001) and acceleration variability (p = 0.015) but not for reaction time of drivers (p = 0.891). Further, the effect of BAC was significant in all the three measures of vehicle control (p < 0.001). Driving control improved with increasing age of male drivers while caffeine consumption was observed as an alcohol-antagonizing factor in female drivers. CONCLUSION: The findings suggest that vehicle control of female drivers is more likely to get affected even at low BAC levels, providing evidence that they belong to critical section of driving community in terms of alcohol-related impairment. The findings may help in discouraging drinking and driving among male and female drivers.

An investigation on the risk factors associated with driving errors under the influence of alcohol using structural equation modeling.

Objective: Driving errors contribute to traffic crashes and thus the causal factors associated with driving errors are of great interest to the road safety researchers. The present study attempts to identify and quantify the effects of various risk factors that are likely to influence driving error with the application of structural equation modeling (SEM). These risk factors include blood alcohol concentration (BAC) levels (0%, 0.03%, 0.05%, and 0.08%), different driving environments (rural, urban), and driver attributes (such as age, gender, education).Method: Eighty-two licensed drivers participated in a driving simulator experiment. They completed driving under the influence of 4 BAC levels in the driving environments within the framework of a full-factorial within-subjects design. Driving error was modeled as an unobserved latent variable based on several driving simulator indicators. An SEM approach was utilized to examine the influence of BAC level, driving environment, and driver attributes on the latent variable pertaining to driving error.Results: The findings suggest the suitability of an SEM approach in the investigation of driving error. The results revealed that all 3 positive BACs (0.03%, 0.05%, and 0.08%) had a significant positive influence on driving error compared to 0% BAC, and the tendency toward driving error increased with increasing BAC (factor loadings for 0.03%, 0.05%, and 0.08% BAC were 0.22, 0.31, and 0.37, respectively). Moreover, driving in an urban environment led to more driving errors compared to a rural environment, including sober drivers. Among the driver attributes, gender and awareness about drink and drive laws were the only factors influencing driving error.Conclusion: This study highlights a novel approach to investigate driving error by modeling it as a latent variable instead of modeling individual performance measures. The successful execution of SEM in alcohol impairment research may serve as a significant step in the human factors field moving from piecemeal analysis to a combined analysis where interrelationships among numerous risk factors and driving error can be established. The study outcomes may serve as a reference while developing strategies to enhance road traffic safety where special emphasis can be given to the critical risk factors influencing driving error identified in the study.

Effects of Partial Sleep Deprivation on Braking Response of Drivers in Hazard Scenarios.

This study aimed at modeling the Response Time (RT) and Total Braking Time (TBT) of drivers under Partial Sleep Deprivation (PSD). Fifty male participants drove the driving simulator in three experimental conditions: two test sessions and a baseline. The two test sessions were conducted after one and two nights of PSD (sleep = 4.25 ±â€¯0.5 h), respectively. Sleep reduction was recorded using a wrist-worn Actiwatch. The baseline session was conducted after full rest (7-8 h sleep/day for a week). The order of test sessions and baseline was randomized. Each test included two hazard events: 1) pedestrians crossing a road and 2) parked vehicles merging into a roadway. Karolinska Sleepiness Scale (KSS) and Sleepiness Symptoms Questionnaire (SSQ) ratings were also recorded during each drive. Four separate models using parametric accelerated failure time (AFT) with Weibull distribution were developed for RT and TBT in the two events. The models were chosen with clustered heterogeneity to account for intra-group heterogeneity due to repeated measures across tests. In the case of pedestrians crossing event, RT increased by 10% in the first test session and no significant effect observed on RT in the second test session. The overall TBT reduced by 25% and 28% during the first and second PSD sessions, respectively. In the case of vehicle merging event, both response time and total braking time delayed by 44% and 17% respectively after PSD. Other factors such as age, experience, work-rest hours, KSS and SSQ rating, often exercising, approaching speed and braking force were also found significant in the analysis. The parametric AFT approach adopted in this study showed the change in 'response time' and 'total braking time' concerning the type of hazard scenario and partial sleep-deprivation.

Alcohol-impaired driving in rural and urban road environments: Effect on speeding behaviour and crash probabilities.

Speeding behaviour is known to influence crash risk among alcohol-impaired drivers, but this relationship is scarcely explored. The present study investigated the effects of different Blood Alcohol Concentrations (BAC) levels on driving performance with respect to mean speed of drivers and their ability to avoid crashes during sudden events while driving. Eighty-two drivers participated in the simulation driving experiment at four BAC levels (0%, 0.03 %, 0.05 % and 0.08 % BAC) in rural and urban driving scenarios. Two sudden events (pedestrian crossing and road crossing by parked vehicles (a car and a truck) in the perpendicular direction of traffic) were designed to evaluate the crash probabilities in both the driving scenarios. Generalized linear mixed models were developed to analyse the effects of BAC levels and driver attributes (e.g., age, gender) on mean speeds and crash probabilities. Results for mean speed showed that, compared to sober state, drivers drove 3.5 kmph, 5.76 kmph and 8.78 kmph faster at 0.03 %, 0.05 % and 0.08 % BAC respectively in the rural environment and this increment was 3.6 kmph, 3.69 kmph and 4.13 kmph in the urban environment. The model results for crash probabilities revealed that 0.03 %, 0.05 % and 0.08 % BAC levels increased the crash probabilities by 1.9 times, 2 times and 3 times in case of the rural environment and 2 times, 2.3 times and 3.5 times respectively in the urban driving environment.

Impact of physical distancing due to novel coronavirus (SARS-CoV-2) on daily travel for work during transition to lockdown.

The outbreak of COVID-19 pandemic has resulted in change in both commute and personal travel patterns. Though, in India, lockdown was implemented from 25th March 2020, due to self-awareness and pandemic risk perception, change in commuter behavior was observed from the beginning of March 2020. The period from 15th to 24th March 2020 is considered as the transition phase of COVID-19 outbreak in India (i.e., between no lockdown and lockdown period). This study attempts to use a decision tree approach to investigate the modal preference of 1542 commuters in association with socio-economic and travel characteristics, and safety perceptions with respect to public and private modes during transition to lockdown due to COVID-19 in India. About 41% of commuters stopped traveling during the transition to lockdown phase, 51.3% were using the same mode of transport and 5.3% of commuters shifted from public to private mode. The study findings reported different interactions of factors influencing the decision to use public or private modes of transport for daily commuting during pandemic situations like COVID-19. Interestingly, safety perceptions (associated with personal health) of commuters did not play a significant role in their mode choice behavior during the transition phase. Though people perceived public transportation as unsafe over personal vehicle use, the actual commute patterns did not validate this due to a possible reason that commuters do not have enough alternative modes. Given the uncertainties in the decision making of the commuters regarding their travel behavior due to physical distancing, the insights from this study are important to policymakers and local transport authorities to understand the change in travel patterns.

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

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-To-Maximum 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.

Laboratory analysis of driving behavior and self-perceived physiological impairment at 0.03%, 0.05% and 0.08% blood alcohol concentrations.

BACKGROUND: Alcohol-impaired driving is a critical factor leading to traffic crashes worldwide. Since decades, researchers from the developed nations have been investigating the effects of alcohol intoxication on their drivers. However, alcohol effects on the drivers of developing counties remain understudied. The extent of alcohol impairment may vary among the drivers of developed and developing nations due to the differences in drivers' physiology, driving conditions, drinking habits, policy enforcement and driving attitude. METHODS: This study examined the effects of different Blood Alcohol Concentration (BAC) levels (0%, 0.03%, 0.05%, and 0.08%) on 75 licensed Indian drivers (56 males and 19 females) with the help of a fixed-base driving simulator. Subjects completed driving simulations for all the BAC levels in a driving environment representing heterogeneous traffic conditions. Performance measures included longitudinal and lateral driving control indicators (obtained from simulator) along with the self-reported physiological changes in the drivers (obtained from questionnaire) with varying BAC levels. RESULTS: Mean speed was the only performance measure significantly affected at 0.03% BAC. At 0.05% BAC, mean speed and mean steering wheel angle were the two significantly impaired measures. At 0.08% BAC, all the driving performance measures showed significant impairment except steering wheel angle variability. Physiological characteristics of drivers deteriorated with rising BAC levels. CONCLUSIONS: Alcohol significantly impaired the physiology of Indian drivers resulting in impairment in their driving performance. Mean speed was the only performance measure significantly affected by all the BAC levels, making it a suitable parameter to detect the alcohol-impaired state of the drivers.

Effects of driver sleepiness and fatigue on violations among truck drivers in India.

This study aims at capturing the influence of driver drowsiness on prevalence of traffic violations among long-haul truck drivers. The study is based on a roadside survey of 453 long-haul truck drivers, stopping at eateries and rest locations on highways connected to three Indian cities- Mumbai, Indore and Nagpur. The survey questionnaire was categorized into three sections: driver demographics, work-rest schedules and safety critical driver behavior (violations and lapses) in the last five years. The questions regarding unsafe driving practices like speeding, overtaking were combined to form a single factor 'violations' using Principal Component Analysis (PCA). A generalized linear model using negative binomial regression predicted young drivers (aged below 25 years), long working hours, insufficient sleeping hours, driving after mid-night, sleepiness on the wheel and frequent traffic violations as significant contributors of violations among the long-haul truck drivers.