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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.

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 (

Research on distracted driving due to phone use has increased substantially over the past decades, however, very little is explored about commercial vehicle drivers (e.g., truck drivers) in this aspect. This study focused on examining the prevalence of phone use habits and the associated crash risk for data collected from 490 Indian truck drivers. The data on demographic details, driving history, phone use habits (in everyday life and during driving), history of receiving any penalty for phone use and incidences of crash occurrence, was collected through face-to-face interviews with the drivers. Binary logistic models were used to identify the factors affecting phone use habits during driving and the associated crash risk. Further, the incidences of receiving a penalty for the phone use were examined through cross-tabulation and chi-square statistics. The results showed that 55% of the drivers used a phone during driving, mainly for talking purpose. The model revealed that education, vehicle size, vehicle ownership and everyday life phone use habits were the significant factors associated with phone use while driving. Regarding the history of penalty receiving incidences, 41% of the drivers who used a phone during driving had received the penalty, and 52% of these penalty-receiving drivers were penalized repetitively. The model results for crash risk showed that the frequent phone users were 29 times more likely to be involved in a crash due to phone use compared to the non-frequent users. The results suggest a double level (legislative and company level) prohibition policy for phone use during driving for the truck drivers and also to enforce strict and effective legislative ban especially on the truck routes.

The research conducted on overtaking maneuver for evaluating drivers’ safety showed adverse effects of urgency on driving performance and decision making. Therefore, a driving simulator study was designed to examine driving performance of the drivers and its implication on overtaking and crash probabilities under increasing time pressure conditions. Eighty-eight participants data were analyzed in the current study. Three different time pressure conditions: No Time Pressure (NTP), Low Time Pressure (LTP), and High Time Pressure (HTP) were considered for analyzing driving performance of the drivers while executing overtaking maneuvers. The driving performance was assessed using minimum time-to-line crossing and coefficient of variation in speed to dissect the safety margin adopted by the drivers while overtaking the lead vehicle. Further, minimum time-to-line crossing and coefficient of variation in speed were considered as explanatory variables to investigate their influence on overtaking and crash probabilities. Parametric survival analysis and Generalized Linear Mixed Models (GLMM) were used to assess the driving performance, overtaking and crash probabilities. The parametric survival analysis showed that minimum time-to-line crossing reduced by 36.7% and 63.8% in LTP and HTP driving conditions, respectively. The GLMM results revealed that coefficient of variation in speed increased by 3.437% in HTP (no significant effect in LTP) as compared to NTP driving conditions. Further, the GLMM results showed that overtaking and crash probabilities decreased with increment in minimum time-to-line crossing and coefficient of variation in speed values. Additionally, it was observed that male drivers took risky decisions than female drivers. Nevertheless, the comparative analysis revealed that male drivers were less prone to crashes than female drivers. Overall, it can be inferred that the drivers take risky decisions with increment in time pressure to complete the driving task, even at the expense of their own safety which exposed them to high likelihood of crashes.

In the past thirty years, alcohol influence on drivers has been widely researched using driving simulators across the world. However, a critical evaluation of the existing evidence is required to project the current status and provide directions for future investigations. The present study conducts a comprehensive systematic review of the research examining the effects of alcohol on simulated driving performance. The literature search was conducted in academic databases such as Google Scholar, SCOPUS, Transportation Research Information Database (TRID), PubMed, and Web of science. In total, 1015 articles were identified from the initial database search along with 14 additional articles from other sources (Articles Plus, Cochrane Library, Medline, National Technical Information Services, National Highway Traffic Safety Administration, and Medline Grey Literature Report), out of which 110 articles were included in the systematic review. The majority of the studies were conducted in the United States of America (45%), followed by Australia (15%). These studies were reviewed based on the following aspects: type of driving simulator, study design, Blood Alcohol Concentration (BAC) or alcohol dose used in the experiments, driving environment characteristics, driving performance measures, data analysis techniques, and additional factors affecting driver behaviour under the influence of alcohol. Overall, the evidence regarding driving performance under the influence of alcohol shows that the majority of the previous studies have found a significant impact of alcohol on the various aspects of driving behaviour. The systematic review highlighted the methodological limitations observed in the previous driving simulator studies which should be acknowledged in future research.

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.

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.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.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 1In 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.

The present study examines the impact of lockdown measures taken to control the spread of COVID-19 on road fatalities. The study is based on the data collected over the duration of six months for f...

© The Institution of Engineering and Technology 2020. Classical artificial potential approach of motion planning is extended for emulating human driving behaviour in two dimensions. Different stimulus parameters including type of ego-vehicle, type of obstacles, relative velocity, relative acceleration, and lane offset are used. All the surrounding vehicles are considered to influence drivers' decisions. No emphasis is laid on vehicle control; instead, an ego vehicle is assumed to reach the desired state. The study is on human-like driving behaviour modelling. The developed motion planning algorithm formulates repulsive and attractive potentials in a data-driven way in contrast to the classical arbitrary formulation. Interaction between the stimulus parameters is explicitly considered by using multivariate cumulative distribution functions. Comparison of two-dimensional (lateral and longitudinal) performance indicators with a baseline model and generative adversarial networks indicate the effectiveness and suitability of the developed motion planning algorithm in the mixed traffic environment.

Distracted driving leads to performance changes in both longitudinal and lateral control. However, driving performance, being a multidimensional phenomenon, is very difficult to be interpreted from individual performance measures. The present study aimed to exhibit the distraction impacts on overall driving performance estimated by a single measure rather than assessing distraction effects on individual performance metrics such as speed, acceleration, lateral variation, etc. The study also focused on modelling and quantifying the impacts of overall deteriorated driving performance on crash risk. To achieve the objectives, a comparative analysis of investigating phone and music player usage was conducted using Structural Equation Modelling (SEM). In total, 90 drivers’ demographic details and driving performance data in distracted and non-distracted driving environments were collected. Firstly, the latent variable “performance degradation” was derived from longitudinal and lateral performance measures. Then the structural model revealed a positive relationship between the distractions and the overall performance degradation. Finally, the crash risk was modelled against the presence of distraction and performance degradation. With a factor loading of 0.29, the impact of deteriorated driving performance (i.e., indirect impact) was found to be the highest among all the contributory factors of the crash risk. Further, the results showed that among the distractions (i.e., direct impact), texting had the highest impact (factor loading = 0.28) on crash risk followed by visual-manual tasks related to music player (factor loading = 0.21). Thus, the present study quantified the effects of deteriorated driving performance caused by distracted driving on the crash risk. Further, the study also presented quantified effects of each distracting activity on the crash risk which accounted for the factors that could not be considered through the performance degradation measure. The approach used in the present study can be adopted in designing the countermeasures using advanced driver warning systems.

Speeding is a major traffic violation and time pressure is one of the leading contributors to speeding. High-speed driving requires an immediate response to perilous events from the driver to avoid a crash. Reaction time is one of the important driving performance measures to assess the driver’s response to the event. Therefore, the current study examined the influence of time pressure on reaction times of the drivers measured for two different perilous events (pedestrians crossing and obstacle overtaking). Eighty-five Indian licensed drivers participated in a driving simulation study designed for three different time pressure conditions: No Time Pressure (NTP), Low Time Pressure (LTP), and High Time Pressure (HTP). The survival analysis technique was used to model the effect of time pressure and driver characteristics with reaction times of the drivers. It was observed that drivers’ reaction times decreased by 18% and 9% in LTP and 28% and 16% in HTP during the pedestrians crossing and obstacle overtaking events, respectively. Further, 1 m/second increase in approach speed resulted in 2% and 4% reduction in reaction times of the drivers in pedestrians crossing and obstacle overtaking events, respectively. Young drivers responded 21% faster than mature drivers during the pedestrians crossing event. Interestingly, sleeping hours and physical fitness played an important role in driver’s reaction to the events. The drivers performing regular physical exercise and having minimum eight-hours of overnight sleep reacted 16% and 17% earlier in pedestrians crossing and obstacle overtaking events, respectively. The overall findings from this study showed enhanced stimulus-response behaviour of the drivers under time pressure driving conditions. The results obtained from the study can give new insight into various safety-related ITS applications.

This study uses survival models to estimate the arrival time of buses at the downstream stops and intersections. Both accelerated failure time (AFT) and Cox regression based hazard models were considered in this study. Two different types of events: (i) buses arriving at bus stops and (ii) buses arriving at signalised intersections were included for measuring arrival times. Weibull and log-logistic distribution models were fitted for obtaining the arrival times against both the events separately. Various other factors such as distance, speed, bus stop dwell time, passenger count, gradient of the road, intersection length and signal details which included green time, red time, cycle length and so on were considered as explanatory variables. The proposed study was tested on a study corridor of length 59.48 km in the Mumbai arterial roads using public transport (buses). The results reveal that arrival times predicted using the developed models provided smaller uncertainties for 70% of the prediction and reduced prediction variation by 10%. The mean absolute percentage error value obtained for the AFT survival models was 10.04. Overall, the AFT model approach appears to be a promising method compared to Cox regression to predict bus arrival times and the associated uncertainties.

Ridesharing is one of the effective tools to solve some of the environmental and congestion problems; however, it is not very well used by women in developing countries due to safety and comfort issues. Lack of prior planning of addressing the issues which influence the ridesharing choices of women is one of the main hurdles to the growth of ridesharing practices in developing countries like India. This study is designed to examine the safety and comfort perceptions during ridesharing among women residing in India. A comprehensive questionnaire with six different thematic sections (such as demographic attributes, safety perceptions with different co-riders, the importance given to cost, privacy, etc. and various other situational factors concerning ridesharing for women) is prepared. The data from 293 female respondents are collected through a survey. Factor analysis is performed on the perception variables to find out any underlying correlation; and the results showed a four-factor solution. The factors accounting for safety and comfort perceptions during ridesharing are modelled with an ordered logistic regression approach. The model results suggest that young women due to fear of being victimized and unemployed women due to their less exposure to ridesharing have lesser trust in such facilities. Women feel less safe and comfortable when sharing a ride with unknown males or during night. A focus group discussion is also conducted to validate the results and to obtain in-depth knowledge of safety and comfort issues during ridesharing.

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.

Indian cities are facing increases in their population, a crisis with space, and urbanization, which is leading to severe transport sector emissions, congestion, and unsustainable growth i...

Vehicle automation allows drivers to disengage from driving causing a potential decline in their alertness. One of the major challenges of highly automated vehicles is to ensure a timely (with respect to safety and situation awareness) takeover in such conditions. For this purpose, the current study investigated the role of an in-vehicle digital voice-assistant (VA) in conditionally automated vehicles, offering spoken discourse relating specifically to contextual factors, such as the traffic situation and road environment. The study involved twenty-four participants, each taking two drives (counterbalanced): with VA and without VA, in a driving simulator. Participants were required to takeover vehicle control following the issuance of a takeover request (TOR) near the end of each drive. A parametric duration model was adopted to find the key factors determining takeover time (TOT). Paired comparisons showed higher alertness and higher active workload (mean NASA-TLX rating) during automation when accompanied by the VA. Paired t-test comparison of gaze behavior prior to takeover showed significantly higher instances of checking traffic signal, roadside objects, and the roadway during the drive with VA, indicating higher situation awareness. The parametric model indicated that the VA increased the likelihood of making a timely takeover by 39%. There was also some evidence suggesting that male drivers are likely to resume control 1.21 times earlier than female drivers. The study findings highlight the benefits of adopting a digital voice assistant to keep the drivers alert and aware about the recent traffic environment in partially automated vehicles.

The present study attempts to statistically model and quantify the contribution of two sets of potential factors to accident risk: 1) real-time driving behavioral characteristics and 2) distraction conditions. The effects of phone and music player usage on reaction times and accident probabilities were modeled for two events: 1) a pedestrian crossing (started in peripheral vision) and 2) sudden braking (started in central vision) in an urban scenario. In total, 90 subjects drove for baseline and conversation conditions. In all, 70 and 78 of the 90 subjects drove for texting and music player tasks, respectively. Reaction times and accident probabilities were modeled with the help of generalized linear mixed model approach. The results showed that the reaction time increased by 42%, 113%, and 62% due to the presence of conversation, texting, and music player operations, respectively, for the pedestrian crossing event. Whereas, the increments were lower for the sudden braking event. Drivers with higher approach speed detected the events faster. The accident probability increased due to the texting tasks and by operating the player during driving. An increase in reaction time also led to higher risk for the pedestrian crossing event. For the sudden braking event, the effect of music-listening was not significant, but the accident risk increased if the driver approached the event at high speed. Overall, the results suggest that the algorithm for designing a collision warning system should consider the real-time driving characteristics based on event type and quantify the effects of distraction based on the type of distraction.

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.

Driving under the influence of alcohol impairs the driving performance and increases the crash risks worldwide. The present study investigated the effect of different Blood Alcohol Concentrations (BAC) on the reaction time of Indian drivers of two age groups (Young: less than 25 years; Mature: 25 years and above) through a driving simulator experiment. Reaction times were measured for the two hazardous events: (1) pedestrian crossing event and (2) road crossing event by parked vehicles. Seventy nine licensed drivers drove on the driving simulator under the influence of four BAC levels (0.00%, 0.03%, 0.05% and 0.08%). To analyse the reaction times, two Weibull AFT (Accelerated Failure Time) models with gamma frailty were developed for both the events separately by incorporating the different alcohol conditions and driver characteristics (such as driver’s age, driving experience and frequent consumption of alcohol) as the explanatory variables. It was observed that the BAC levels of 0.03%, 0.05% and 0.08% resulted in 36%, 53% and 94% increment in the reaction times of the drivers for the pedestrian crossing event and 64%, 78% and 116% increment in the reaction times for road crossing event by the parked vehicles respectively. A year increase in the driving experience resulted in 2% reduction in the reaction times, and mature drivers responded 15% faster than the young drivers. Interestingly, frequent drinkers showed 11% quicker response than the drivers who drink rarely. Overall, the results showed that the stimulus-response behaviour of the drivers was impaired in all the intoxicated states. The research findings may assist the countermeasure studies and policy-making against alcohol-impaired driving.