Search

• The effect of driving aggression on steering behavior, lane change duration, and crash probability during lane changes was analyzed. • Lane change durations were reduced by 59% for aggressive drivers due to their tendency for hasty lane changing. • Aggressive drivers increased their steering reversal rates by 60% and their maximum steering swerve by 62%. • The risk-taking behavior of aggressive drivers increased their crash rate by 5.10 times. The current study aimed to investigate the influence of driving aggression on lane change behavior. Drivers belonging to three levels of aggressive driving styles (aggressive, moderately aggressive, and non-aggressive) participated in lane change experiments, and 232 lane change maneuvers were recorded. Steering behavior during lane change maneuvers was investigated using three steering performance measures, including the standard deviations of steering angle, steering reversal rates, and maximum steering swerve. Three separate generalized estimating equation models were developed to analyze the effects of aggressive driving behavior on the standard deviation of steering angle, steering reversal rates, and maximum steering swerve. A Weibull accelerated failure time model with clustered heterogeneity was developed to analyze the effect of aggressive driving behavior on lane change execution duration. Additionally, to model the crash probability during lane change, a random effects logistic regression model was developed. The findings indicate that aggressive and moderately aggressive drivers completed lane changes 59% and 38% faster, respectively, compared to non-aggressive drivers. This implies a propensity for aggressive and moderately aggressive drivers to execute lane changes more swiftly than non-aggressive drivers. Aggressive drivers increased their standard deviation of steering angle, steering reversal rates, and maximum steering swerves by 180%, 60%, and 62%, respectively, compared to non-aggressive drivers. This implies that aggressive drivers engaged in extreme steering behavior as a compensatory measure for their reckless driving in order to avoid a collision during lane change maneuvering. However, despite their vigorous compensatory steering behavior, the crash rate (number of crashes observed) increased by 5.10 and 2.83 times for aggressive and moderately aggressive drivers, respectively, compared to non-aggressive drivers. The findings have implications for developing driving assistance systems and lane change simulation models. [ABSTRACT FROM AUTHOR]

Motorized Two-Wheeler (MTW) drivers significantly contribute to road fatalities due to their vulnerability and the higher severity of crashes. Risky driving behavior, such as violations and errors, is a key precursor to road crashes. Understanding the factors that influence such risky behavior can shed light on opportunities for risk management. In this investigation, data from 460 motorized two-wheeler drivers were collected through an online questionnaire, exploring Red Light Running (RLR), lane sharing, and turning without indicating driving tendencies, latent psychological factors, and demographic attributes of the participants. The analysis was conducted in two steps: (a) decision trees were developed to classify risky driving behavior, and (b) binary logistic regression models were developed to quantify risky driving choices based on demographic and latent psychological variables, as well as interaction variables. The results showed that as driver anxiety increased, the probability of lane sharing rose from 76.3% to 87.9% in normal driving situations and from 80.3% to 96.6% in time-pressure driving situations, respectively. Furthermore, the results revealed that the odds of running red lights and lane sharing decreased to 0.57 and 0.68, respectively, for older drivers compared to young drivers. Overall, this study quantified the impact of underlying psychological factors and demographic parameters, as well as their combined effects, on assessing traffic violations, traffic errors, and non-illegal dangerous driving behaviors among motorized two-wheeler drivers. Additionally, the study discusses the implications and policy interventions related to the legalization of lane sharing and the risks associated with time-pressure driving in online food delivery systems. These findings can inform evidence-based policymaking to achieve road safety goals. • Anxious drivers are more likely for lane sharing especially in time constrained situations. • Older drivers are less likely to indulge in red-light running and lane sharing. • Impunctual drivers are more prone to turning without indicating. • The acceptance of traffic violation by pillion rider motivated drivers to violate traffic rules. [ABSTRACT FROM AUTHOR]

This study analyzed the impact of driving environments (real-world and simulated world) and driving conditions (no time pressure and time pressure) on speed compliance and speed adaptation. Professional car drivers were recruited, and the data was collected in real-world and simulated world under no time pressure and time pressure driving conditions. The comparison results using Wilcoxon-signed rank test showed that speed compliance and speed adaptation were not consistently significant and were not in the same direction highlighting the influence of various factors like road features and driver characteristics. The generalized linear mixed model results showed that speed compliance was relatively better in simulated world (by 3.98 kmph) than real-world. Further, speed adaptation under time pressure was about 5.86 kmph lower during real-world as compared to simulated world. The findings from this study can provide new insights on road safety strategies and policy implications for limiting speeding-related crash risks. [ABSTRACT FROM AUTHOR]

This study modelled the effects of partial sleep deprivation (PSD) in terms of sleep debt on mean speed, speed variability and associated crash probability. Fifty male drivers drove the driving simulator in three test sessions (TS): TS1 (night) and TS2 (lunch) after one and two consecutive nights of PSD (sleep ≤4.5h; measured using Actiwatch), respectively and TS3 with no sleep deprivation. The mean sleep debt observed in TS1, TS2 and TS3 were 2.2 h, 1.53 h and no sleep debt, respectively. A generalized linear mixed model (GLMM) model results showed that the crash risk increased 1.5 times with increasing sleep debt, 27% with increasing speed and doubled with increasing speed-variability. The findings show that reducing speed by more than 15% could have eliminated the risk of crashes. However, the drivers suffered from loss of speed control under the sleep debt conditions. [ABSTRACT FROM AUTHOR]

• This study investigated the longitudinal driving performance measures of aggressive, moderately aggressive, and non-aggressive driver behavior. • The Deceleration adjusting time was modelled to analyze and compare the collision avoidance behavior. • The time spent tailgating increased by 107% and 53% for aggressive and moderately aggressive drivers, respectively. • The survival probabilities of non-aggressive, moderately aggressive, and aggressive drivers were 98.4%, 82.82%, and 69.63%, respectively. Driving aggression is a major concern during car-following situations as it is closely associated with collision risk and crash severity. Despite the tendency of reckless driving actions and increased crash risk with the vehicle ahead, aggressive driver behavior remains unexplored. The present study aimed to investigate the effects of aggressive driver behavior (three driver categories: aggressive, moderately aggressive, and non-aggressive drivers) and drivers' tendency to aggressive stimuli (due to lead vehicle (vehicle moving ahead) reckless driving) on car-following behavior. A sample of fifty-eight Indian drivers participated in this study. All the experiments were conducted using a driving simulator. The simulator design includes a lead vehicle dynamic maneuver with rapid accelerations and decelerations (at 1 m/s2, 1.5 m/s2, and 2 m/s2). Three longitudinal performance measures including speed variability (SPV) from LV, speed recovery time (SRCT), and time spent tailgating, were analyzed during LV acceleration stage using generalized linear models (GLM). Further, to assess collision avoidance behavior and crash risk, deceleration adjusting time (DAT) was analyzed during the LV sudden deceleration stage using Weibull Accelerated Failure Time (AFT) analysis. The findings indicate that due to their tendency to risk-raking behavior, aggressive drivers decreased their SPV and SRCT by 25 % and 18 %, respectively. The time spent tailgating increased by 107 % for aggressive drivers. The survival probabilities were decreased by 23.77 % for aggressive drivers at 3 sec DAT. The findings of the study can be used in car-following models to enhance the model performance, improve the reliability of simulation tools, and design interventions to improve safety (in automated driving). [ABSTRACT FROM AUTHOR]

• A systematic review of observational studies on mobile phone distraction is conducted. • 39 research articles were selected using PRISMA methodology. • 35% of the studies were conducted in the United States of America (USA). • 69% of the investigations were done in the last five years (2017 – 2021). • Amongst various distracting activities, pedestrians reported the highest behavioural impairment during texting and walking. • Females were less likely to show risky road crossing behaviour compared to male pedestrians. The growing prevalence of technological distractions amongst pedestrians makes it an important road safety concern. Observational studies are considered a reliable method to investigate the influence of mobile phone distraction on pedestrian road crossing behaviour and crash risks. The present study conducts a systematic review of international literature on pedestrian distraction observations by following the Preferred Reporting Items for Systematic Reviews and meta-Analyses (PRISMA) guidelines. A total of 792 studies were identified from the literature search on six research databases: Scopus, Google Scholar, Web of Science, PubMed, Cochrane library, and Transportation Research International Documentation (TRID). Finally, 39 research articles were assessed using the systematic classification scheme based on the following five research aspects: prevalence of mobile phone distraction, study locations, performance measures, analysis techniques, and additional factors associated with mobile phone use among pedestrians. Over 35% of the studies were conducted in the United States of America (USA) and 69% of the investigations were done in the last five years. Overall, the findings across the studies indicate that mobile phone distraction plays a major role in pedestrian risky road crossing behaviour and violation tendencies. Visual distractions (such as texting) exhibited higher behavioural impairment compared to cognitive distractions (e.g., listening to music, and conversations). Pedestrian characteristics such as gender and age were the key factors examined in 77% and 67% of the observational studies. Finally, important directions for future research are illustrated to aid the researchers working in the area of pedestrian safety. [ABSTRACT FROM AUTHOR]

• Drivers in driving simulator showed higher speed values than field conditions. • Lack of motion cues affected braking behavior of the drivers in simulated world. • Nominal influence of traffic in simulated world ensued less variation in acceleration. • Results showed expediency of driving simulator in human factors research. Driving simulators have become an important tool in human factors research, given that they are appropriately validated. Therefore, this study aims to explore the behavioral (absolute and relative) validity of a fixed-base driving simulator by analyzing different driving behavior measures such as speed, longitudinal acceleration, lateral acceleration, and brake pedal force. Thirty professional drivers participated in the experiment and the data was collected in real and simulated worlds under No Time Pressure (NTP) and Time Pressure (TP) driving conditions. Initially, comparative analyses were conducted on different driving behavior measures using Wilcoxon-signed rank test to examine absolute validity of the driving simulator. Finally, Generalized Linear Mixed (GLM) models were developed for computing the effective distance between real and simulated worlds by quantifying the parameters and for establishing relative validity. In general, the continuous profiles of driving behavior measures followed similar trends in real and simulated worlds and comparative analyses indicated relative validity of the driving simulator. The GLM models showed significant interaction effect of driving environments (real-world and simulated world) and driving conditions (NTP and TP) where high driving speed, high brake pedal force, and low lateral acceleration were observed in simulated world under TP driving condition than real-world under TP driving condition. Overall, the statistical analyses showed qualitative correspondence (relative validity) of the driving behavior measures in between real and simulated worlds. The findings from the current study showed expediency of the driving simulator and its effectiveness in conducting research on human factors and driver safety. [ABSTRACT FROM AUTHOR]

• Mean acceleration, time to stop, and time pressure altered acceleration behavior. • Drivers adopted deceleration-acceleration behavior under high time pressure. • The crossing likelihood decreased with increment in age at the onset of yellow signal. • Drivers showed low propensity to cross when they were 6 s away from the intersection. This study investigates acceleration behavior and crossing decision of the drivers under increasing time pressure driving conditions. A typical urban route was designed in a fixed-base driving simulator consisting of four signalized intersections with varying time to stop line (4 s and 6 s) and maneuver type (right-turn and go-through). 97 participants' data were obtained under No Time Pressure (NTP), Low Time Pressure (LTP), and High Time Pressure (HTP) driving conditions. The acceleration behavior was examined at the onset of yellow signal in four ways: continuous deceleration, acceleration-deceleration, deceleration-acceleration, and continuous acceleration. A random forest model was used to build an acceleration behavior prediction model for identifying the significant explanatory variables based on variable importance ranking. Further, a Mixed Effects Multinomial Logit (MEML) model was developed using the explanatory variables obtained from a random forest model. Additionally, a generalized linear mixed model was incorporated for estimating the likelihood of crossing an intersection by considering all the explanatory variables. A MEML model result revealed that the odds of adopting acceleration-deceleration, deceleration-acceleration, and continuous acceleration instead of continuous deceleration increased by 63 %, 123 %, and 77 %, respectively under HTP driving conditions. Moreover, the likelihood of crossing a signalized intersection increased by 2.73 times and 4.26 times when the drivers were under LTP and HTP driving conditions, respectively as compared to NTP driving condition. Apart from this, time to stop line (reference: 6 s) and age showed negative association with crossing probability. Overall, the findings from this study revealed that drivers altered their acceleration behavior for executing risky driving decisions under increasing time pressure driving conditions. [ABSTRACT FROM AUTHOR]

• Interaction between driver occupation effect and sleep deprivation was examined. • Effects of sleep loss were compared between professional taxi and other drivers. • Sleep loss led to higher performance decrements among professional drivers than other drivers. • Sleep loss led to increased lateral deviations, speed variability and delayed responses. • Interaction effects of occupation and sleep loss were negligible on steering and heading controls. This study aims to determine the effects of partial sleep deprivation (PSD) on driving performance of professional and young non-professional drivers. The study included fifty participants (20 professional taxi drivers and 30 young non-professional drivers) driving the simulator-cab in three conditions. The first test session (TS1) was conducted after one night of PSD followed by the second test (TS2) after two consecutive nights of PSD. The driving performance metrics in two conditions of PSD (i.e., sleep duration = 4.25 ± 0.5 h) were compared with the baseline drive with no sleep loss. Sleep restriction was monitored using Actiwatch. Drivers subjectively self-reported their alertness using Karolinska Sleepiness Scale (KSS). Driving performance metrics and reaction time to emergency events were collected during each drive. A preliminary mixed-design ANOVA showed deterioration in driving performance of all drivers in terms of speed (p < 0.1), speed variability (p = 0.06), standard deviation in lateral positions (SDLP) (p < 0.001) and delayed reaction time (p < 0.05). Separate Mixed-Effects Generalized Linear Models for professional and non-professional drivers showed that speed variability, SDLP and reaction time increased from baseline during both the PSD tests, among both the driving groups. The speed variability, SDLP and reaction time of professional drivers differed significantly from other drivers under PSD conditions. Contrary to the existing belief, the professional drivers had significant decrements in driving performance due to PSD. A critical and comparative analysis revealed that driving experience/skill of professional drivers does not improve their resistance to deteriorating effects of sleep loss. [ABSTRACT FROM AUTHOR]

• Minimum time-to-line crossing reduced by 36–63% in time pressure conditions. • Crash probability decreased with increase in minimum time-to-line crossing and CVS. • Male drivers took risky driving decisions, nevertheless were less prone to crashes. 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. [ABSTRACT FROM AUTHOR]

• A comprehensive systematic review of driving simulator studies on alcohol impairment was conducted. • 110 research articles were included in the final systematic review. • 86% of the studies were conducted in the developed countries. • The heterogeneity observed in the outcomes of previous research is indicated. • The methodological limitations observed in the driving simulator studies are highlighted. 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. [ABSTRACT FROM AUTHOR]

• Drivers executed abrupt steering maneuvers under time pressure conditions. • Steering reversal rate was substantially affected by time pressure driving conditions. • Steering control was significantly influenced by age, gender, and driving experience. The current study focused on analyzing steering control of the drivers during a car-following situation under increasing time pressure conditions. A driving simulator experiment was conducted on ninety-two participants to assess steering performance measures. Five different steering control measures: Variability in Steering Angle (VSA), Steering Reversal Rate (SRR), Steering Speed (SS), Stability of Steering Control (SSC), and Maximum Steering Swerve (MSS) were examined under No Time Pressure (NTP), Low Time Pressure (LTP), and High Time Pressure (HTP) driving conditions. Repeated measures ANOVA (for continuous data) and Friedman's test (count data) with post-hoc analysis and Generalized Estimating Equation (GEE) modeling technique were used to investigate the influence of time pressure and different predictor variables. The statistical analysis showed that time pressure driving conditions significantly affected steering control of the drivers. The pairwise comparison of time pressure conditions revealed that HTP significantly affected most of the steering control measures as compared to LTP. Further, a GEE model also exhibited similar results where steering control measures were substantially influenced by HTP as compared to LTP. Moreover, in addition to time pressure conditions, demographic characteristics showed significant influence on steering control measures. The GEE model results showed that female drivers performed 13% more steering corrections (5° SRR) which led to better SSC by 124.44% than male drivers. Additionally, it was discovered that young-aged and experienced drivers took extra steering efforts to control lateral position of the vehicle by increasing 53.50% SS and 1% SRR compared to middle-aged and inexperienced drivers. The findings from the current study revealed that drivers undergo fast and abrupt steering maneuvers under time pressure conditions. The research approach demonstrated in the current study can be beneficial to discriminate minimum requirement of steering efforts and set-up threshold values for various steering evasion techniques to control and maintain safe lateral position during car-following maneuvers. [ABSTRACT FROM AUTHOR]

Countdown timers at traffic signals display the time remaining for a phase to terminate, influencing driver behaviour. Studies on their effects at the onset of yellow have generally focused on drivers’ stop/go decisions and dilemma zone, without explicitly addressing how they help drivers anticipate the signal change and make ‘informed decisions’. Therefore, this study first analysed the impact of countdown timer on drivers’ anticipation by comparing brake initiation, deceleration, and speed in the absence and presence of countdown timer. Next, driver behaviour was studied using ‘Accelerated Failure Time’ models. A driving simulator experiment was designed for data collection, with demographic and additional driving behaviour data gathered through a questionnaire. When countdown timer was present, drivers anticipated the onset of yellow, resulting in significant variations in driver behaviour. These variations, in turn, influenced the duration of braking and the time taken to cross the stop line from the onset of yellow. [ABSTRACT FROM AUTHOR]

• In time pressure, drivers are required to react swiftly due to high-speed driving. • Simulator study is performed on 85 drivers under increasing time pressure conditions. • Drivers showed enhanced stimulus-response under increasing time pressure conditions. • Regular physical exercise and minimum of 8 h of sleep improved reaction time by 14–17%. 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• Effect of different BAC levels on the reaction time of drivers was quantified. • Driving simulator experiments were conducted on 79 Indian drivers. • Parametric survival models between reaction time and BAC levels were developed. • Driving impairment was higher in the young drivers as compared to mature drivers. • Effects of drivers' experience and drinking frequency were also found significant. 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. [ABSTRACT FROM AUTHOR]

Countdown timers impact driver behavior significantly and are widely used at signalized intersections in many countries. Literature shows potential vehicle class-specific temporal and spatial variations in driver behavior in the presence of timer. Therefore, first, this paper analyzed the temporal, spatial, and vehicle class-specific impacts of timer on driver behavior in heterogeneous traffic conditions based on variations in acceleration. Second, a Long Short-Term Memory model was developed to model vehicle discharge, incorporating the heterogeneity of traffic and impacts of timer. The performance of the model was compared with that of the Intelligent Driver Model using field trajectory data from three signalized intersections in Kerala, India. The model accurately predicts the following behavior with mixed spacing errors of 0.24 to 0.35 and an average mean squared error of spacing of 1.23. The results were better than, or comparable to, results from recent data-driven car-following models and significantly better than IDM. [ABSTRACT FROM AUTHOR]

The high-speed rail (HSR) significantly improves the accessibility between cities. However, these benefits are not uniformly distributed in the region that HSR connects. This study hypothesises that integrating the conventional rail network with HSR can improve regional accessibility and reduce HSR-induced inequity in accessibility. The scenario-based analysis was conducted to test the hypothesis on India’s upcoming HSR corridor. The numerical results were obtained using weighted average travel time, potential accessibility, daily accessibility and area coverage as the indicators of accessibility. The introduction of HSR reduces inter-regional travel times by 45% but increases the inequity in accessibility by 19%. HSR’s integration with conventional rail results in a 53% reduction in travel time and only an 8.5% increment in inequity. The HSR-integrated conventional rail’s average speed improvement by 30 km/h improves the daily accessibility by 79% while reducing the HSR-induced spatial inequity by 13%. This study quantifies the accessibility and equity gains of integrating HSR with conventional rail and suggests intermodal cooperation for equitable and sustainable development. [ABSTRACT FROM AUTHOR]

• Different levels of aggressive drivers are identified based on multi-dimensional vehicle kinematics. • A validation of the driver detection method is performed using self-reported trait and instrumental aggression to ensure the accuracy of the driver detection method. • The effect of aggressive driver behavior on crash risk is quantitatively analyzed based on time to collision (TTC) during an emergent pre-crash scenario. • Further, collision evasive behavior is analyzed based on speed reduction time (SRT) survival probabilities. 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. [ABSTRACT FROM AUTHOR]

• No compensatory measure adapted for eating and drinking tasks at intersections. • Crash risk at unsignalised intersections increased during eating and drinking tasks. • Texting resulted in reduced reaction distance at the intersection approach. • Drivers who drove regularly kept large safety margins while crossing intersections. The present study analysed and compared the impacts of distraction caused by eating, drinking and texting on risky driving behaviour at unsignalised intersections. For this purpose, 89 participants approached the simulated unsignalised intersections through the minor road and encountered a series of vehicles on the major road. Their driving performance data were analysed in terms of reaction distance and accident probability at approach and completion zones respectively. The results revealed that during the texting tasks, the drivers detected the major road traffic at a later stage compared to the baseline. Further, unlike the texting task, the eating and drinking tasks did not affect the driving performance negatively in the approach zone. However, the accident risk in the completion zone was increased significantly due to the presence of the eating and drinking tasks. Unexpectedly, the accident risk was as low as 76% for the texting task while crossing through the intersections, possibly because of the compensatory measures adapted by the drivers. The study suggested that everyday distractions could result in unsafe consequences particularly in complex driving situations because of the lower risk perceptions associated with these distractions. [ABSTRACT FROM AUTHOR]

• Distraction caused by a music player and phone conversation results in reduced crossing probability. • Drivers have higher crossing probability while talking on phone compared to the music player task. • Distracted mid-age drivers are more likely to cross an intersection after the yellow onset. • Experienced drivers keep larger safety margins during yellow signal. The present study attempts to contribute to the literature by identifying and comparing the effects of two important distractions, i.e., phone conversation and music player operations, on drivers' stop/cross decisions at the yellow onset. For this purpose, 74 participants performed driving experiments on a simulated urban route with six signalised intersections. Driver distraction, demographics, intersection configurations and driving attributes were considered as independent variables. Additionally, some specific situations (combinations of the different attributes) associated with the stop/cross decision were identified with a decision tree method. A generalised linear mixed model with a logit link function was developed for estimating the probability of crossing an intersection by considering all the main variables and the potential interaction obtained from the decision tree. The results highlighted that the driving speed was positively associated with the crossing probability. Whereas, Time to Stop Line (TSL), type of manoeuvre and the presence of the distractions had a negative influence on the probability. The odds of crossing an intersection were 17% lesser for the music player distraction than the phone conversation. Additionally, six interaction variables provided insights into effects of age, gender and driving experience on drivers' stop/cross decision in some specific situations. [ABSTRACT FROM AUTHOR]

Highlights • A critical assessment of phone use on young and professional drivers is conducted. • Conversation and texting effects are examined on a driving simulator. • Longitudinal and lateral controls get deteriorated significantly for young drivers. • Distraction effects cannot be diminished even by experienced professional drivers. Abstract Phone use during driving is quite prevalent particularly among young drivers because of their risk-taking attitude and overconfidence on driving skills. Young drivers are at an increased risk because of their psychosocial status and inexperience. Therefore, performing secondary tasks during driving could be more dangerous for this group of drivers. This study aims to find the impact of conversation and texting on the performance of inexperienced young drivers (n = 25) and professional drivers (who are employed as drivers in a transport company; n = 24) with the help of driving simulator experiments. The distraction tasks are presented with two difficulty levels. A critical and comparative analysis of the driver categories is carried out with respect to their longitudinal (speed and acceleration) and lateral vehicle control (lane positioning and steering movements). Results show that young drivers compensate less and have lower longitudinal control during distracted driving. Young drivers manage to keep lesser steering reversals compared to professional drivers; but, show higher variations in lane positioning. The findings suggest that there is a higher deterioration in performance of the inexperienced young drivers during phone use. However, performance degradation of the experienced drivers is also significant, showing that higher experience cannot nullify the effects of increased workload during distracted driving. [ABSTRACT FROM AUTHOR]

Highlights • Driving continuously for more than 6 h heavily increased the odds of driver sleepiness. • Partially sleep deprived drivers (sleeping ≤4 h daily) were at high risk of drowsiness. • Driving under the influence of financial incentives increased the odds of falling asleep by 1.58 times. • Driver alertness reduced with higher intake of caffeine and tobacco while driving. • Frequent violators of traffic rules showed increased risk of drowsiness on the wheel. Abstract The aim of the study is to identify and model the role of payment incentives, driver work-rest patterns and other lifestyle habits influencing the drowsy driving behavior among long-haul truck drivers. To achieve this aim, this study targeted two main objectives: (1) to examine the significant differences between the groups of drowsy and non-drowsy drivers based on the opportunities of monetary incentives and (2) to examine the role of different factors: driver demographics, work-rest patterns, lifestyle and occupational characteristics particularly incentives associated with driving in causing driver sleepiness among Indian truck drivers. The study is based on interview responses from 453 long-haul truck drivers approached in three Indian cities – Mumbai, Indore and Nagpur. Initial principal component analysis of the responses related to financial incentives (occupational characteristics) resulted into two correlated factors: (i) willingness to earn extra payments if offered (WEP) and (ii) incentives available in the current driving experience (ICD) that influence driver work-rest patterns and alertness while driving. Kruskal-Wallis test showed a significant difference among the groups of sleepy and non-sleepy drivers due to these factors (WEP and ICD). Finally, a logistic regression model showed that long driving duration, working days per week, rest patterns, insufficient sleeping hours and history of violations were found significantly associated with drowsy driving among the long-haul truck drivers. Increase in consumption of caffeine and tobacco indicated reduction in driver alertness. According to the model results, the odds of drowsy driving were 77% less for drivers between 46 and 55 years compared to the young drivers (<25 years). Driving under the influence of financial incentives was observed to increase the odds of falling asleep by 1.58 times among the truck drivers. This was apparently the most interesting and intriguing result of the study indicating the need for further research on the influence of financial or socio-economic motivations to sleepiness. [ABSTRACT FROM AUTHOR]

Highlights • A dynamic two-dimensional (D2D) map-matching algorithm has been developed. • In this algorithm, a road segment is virtually expressed as a matrix of homogeneous grids. • The algorithm supports lane-based in-vehicle navigation using data from GPS only. Abstract Existing map-Matching (MM) algorithms primarily localize positioning fixes along the centerline of a road and have largely ignored road width as an input. Consequently, vehicle lane-level localization, which is essential for stringent Intelligent Transport System (ITS) applications, seems difficult to accomplish, especially with the positioning data from low-cost GPS sensors. This paper aims to address this limitation by developing a new dynamic two-dimensional (D2D) weight-based MM algorithm incorporating d ynamic weight coefficients and road width. To enable vehicle lane-level localization, a road segment is virtually expressed as a matrix of homogeneous grids with reference to a road centerline. These grids are then used to map-match positioning fixes as opposed to matching on a road centerline as carried out in traditional MM algorithms. In this developed algorithm, vehicle location identification on a road segment is based on the total weight score which is a function of four different weights: (i) proximity, (ii) kinematic, (iii) turn-intent prediction, and (iv) connectivity. Different parameters representing network complexity and positioning quality are used to assign the relative importance to different weight scores by employing an adaptive regression method. To demonstrate the transferability of the developed algorithm, it was tested by using 5,830 GPS positioning points collected in Nottingham, UK and 7,414 GPS positioning points collected in Mumbai and Pune, India. The developed algorithm, using stand-alone GPS position fixes, identifies the correct links 96.1% (for the Nottingham data) and 98.4% (for the Mumbai-Pune data) of the time. In terms of the correct lane identification, the algorithm was found to provide the accurate matching for 84% (Nottingham) and 79% (Mumbai-Pune) of the fixes obtained by stand-alone GPS. Using the same methodology adopted in this study, the accuracy of the lane identification could further be enhanced if the localization data from additional sensors (e.g. gyroscope) are utilized. ITS industry and vehicle manufacturers can implement this D2D map-matching algorithm for liability critical and in-vehicle information systems and services such as advanced driver assistant systems (ADAS). [ABSTRACT FROM AUTHOR]

Hierarchical clustering analysis framework is developed to identify benchmark and critical regions for effective road safety strategies. The regions are grouped based on agglomeration coefficient of mutually exclusive crash causation parameters. Subsequently, regions from groups with lower than a threshold index value are selected as benchmark for the poorly performing critical counterparts. Euclidean distance-based Ward's, median and centroid clustering techniques are explored through a case study of Indian states and Union Territories. As per data between 2006 and 2015, fatal crash percentages of driving under influence of drug and alcohol, excessive speeding, vehicle malfunction and road conditions related crash causation parameters, severity index and its growth rate are assessed based on respective threshold values of 6.35%, 43.28%, 2.42%, 1.79%, 26.7 and 3.1%. These are the national average of respective indices. It demonstrated the unique application of hierarchical clustering analysis in benchmark and critical region identification. [ABSTRACT FROM AUTHOR]

The transportation sector in India faces significant issues, such as congestion and air pollution, and is in dire need of sustainable strategies. Sharing vehicles is one of the popular sustainable strategies. Sharing auto-rickshaws, a paratransit mode, currently informally operating with a significant mode share, offers an opportunity to tackle sustainability issues. There are several challenges to integrating and promoting auto-rickshaw system as shared transportation using a formal structure of policies. The primary reason is a dearth of studies on sharing auto-rickshaws, leading to policymakers lacking knowledge and focus. The present study contributes to the literature to divert focus on sharing auto-rickshaws in India, considering Mumbai Metropolitan Region (MMR) as a study area. This study attempts to assess and model the intentions of users and non-users toward auto-rickshaw sharing using stated preference (SP) choice experiments and estimate Willingness-to-Pay (WTP) considering multiple socio-economic heterogeneities. Results highlight that the most critical attributes are travel time reliability and access time among different groups. Importance of having real-time information on trips among females and sharing auto-rickshaw users is high. The study provides a transparent direction toward ensuring efficient and integrated policymaking and guidelines for promoting auto-rickshaw sharing in urban areas of the Indian subcontinent with limited resources. • Perception of users and non-users of sharing auto-rickshaws are compared. • Segmentation analysis is used to assess heterogeneity among different subgroups. • Willingness to pay for the subgroups is estimated by Mixed-logit modeling. • Travel time reliability and Access time are most important for all sub-groups. • Clear direction to promote auto-rickshaw sharing and integrate it into policymaking. [ABSTRACT FROM AUTHOR]

This study analysed and modelled the effects of conversation and texting (each with two difficulty levels) on driving performance of Indian drivers in terms of their mean speed and accident avoiding abilities; and further explored the relationship between speed reduction strategy of the drivers and their corresponding accident frequency. 100 drivers of three different age groups (young, mid-age and old-age) participated in the simulator study. Two sudden events of Indian context: unexpected crossing of pedestrians and joining of parked vehicles from road side, were simulated for estimating the accident probabilities. Generalized linear mixed models approach was used for developing linear regression models for mean speed and binary logistic regression models for accident probability. The results of the models showed that the drivers significantly compensated the increased workload by reducing their mean speed by 2.62 m/s and 5.29 m/s in the presence of conversation and texting tasks respectively. The logistic models for accident probabilities showed that the accident probabilities increased by 3 and 4 times respectively when the drivers were conversing or texting on a phone during driving. Further, the relationship between the speed reduction patterns and their corresponding accident frequencies showed that all the drivers compensated differently; but, among all the drivers, only few drivers, who compensated by reducing the speed by 30% or more, were able to fully offset the increased accident risk associated with the phone use. [ABSTRACT FROM AUTHOR]

Phone use during driving causes decrease in situation awareness and delays response to the events happening in driving environment which may lead to accidents. Reaction time is one of the most suitable parameters to measure the effect of distraction on event detection performance. Therefore, this paper reports the results of a simulator study which analysed and modelled the effects of mobile phone distraction upon reaction time of the Indian drivers belonging to three different age groups. Two different types of hazardous events: (1) pedestrian crossing event and (2) road crossing event by parked vehicles were included for measuring drivers’ reaction times. Four types of mobile phone distraction tasks: simple conversation, complex conversation, simple texting and complex texting were included in the experiment. Two Weibull AFT (Accelerated Failure Time) models were developed for the reaction times against both the events separately, by taking all the phone use conditions and various other factors (such as age, gender, and phone use habits during driving) as explanatory variables. The developed models showed that in case of pedestrian crossing event, the phone use tasks: simple conversation, complex conversation, simple texting and complex texting caused 40%, 95%, 137% and 204% increment in the reaction times and in case of road crossing event by parked vehicles, the tasks caused 48%, 65%, 121% and 171% increment in reaction times respectively. Thus all the phone use conditions proved to be the most significant factors in degrading the driving performance. [ABSTRACT FROM AUTHOR]

Distracted driving due to mobile phone use has been identified as a major contributor to accidents; therefore, it is required to develop ways for detecting driver distraction due to phone use. Though prior literature has documented various visual behavioural and physiological techniques to identify driver distraction, comparatively little is known about vehicle based performance features which can identify driver’s distracted state during phone conversation and texting while driving. Therefore, this study examined the effects of simple conversation, complex conversation, simple texting and complex texting tasks on vehicle based performance parameters such as standard deviation of lane positioning, number of lane excursions, mean and standard deviation of lateral acceleration, mean and standard deviation of steering wheel angle and steering reversal rates (for 1°, 5° and 10° angle differences). All these performance measures were collected for 100 licensed drivers, belonging to three age groups (young, mid-age and old age), with the help of a driving simulator. Effects of all the phone use conditions and driver demographics (age, gender and phone use habits) on the measures were analysed by repeated measures ANOVA tests. Results showed that 1°, 5° SRRs are able to identify all the distracted conditions except for simple conversation; while, 10° SSR can detect all the distracted conditions (including simple conversation). The results suggest that 10° SRR can be included in intelligent in-vehicle devices in order to detect distraction and alert drivers of their distracted state. This can prevent mobile phone use during driving and therefore can help in reducing the road accidents due to mobile phone distractions. [ABSTRACT FROM AUTHOR]

• Drivers adopted aggressive gap-acceptance behavior under time pressure. • Physically active male drivers showed conservative gap-acceptance behavior. • Drivers with speed reduction of 12–18 m/s faced high dilemma during gap-acceptance. • Drivers taking overnight sleep of at least 8 h had 54.7% less accident risk. Time pressure is a driving condition experienced by drivers in day-to-day life. However, limited studies are conducted on assessing the effects of time pressure on driving performance, particularly in Low-and-Middle Income Countries. Further, the majority of road safety research in Low-and-Middle Income Countries is based on the assumption of consistency and homogeneity of driving condition without taking into account the risk tolerance of the drivers which highly governs the driving performance and decision making while driving. Thus, the current study was conducted to overcome this particular aspect by examining the gap-acceptance decision of the drivers and its implication on safety at un-signalized intersections under time pressure driving conditions. Ninety-four participants' data were collected on the driving simulator located at Indian Institute of Technology Bombay in three different time pressure conditions: No Time Pressure (NTP), Low Time Pressure (LTP), and High Time Pressure (HTP). The accepted gaps were analyzed using a decision tree which revealed that merge distance of oncoming vehicles, speed reduction, gender, regular exercise, and driving profession along with time pressure significantly influenced the gap-acceptance decisions. Compared to NTP driving condition, the accident probability was observed to increase by 127% and 181% in LTP and HTP driving conditions, respectively. The overall findings from the current study aids in understanding the effect of time pressure on drivers' decision making and safety as well as highlight the role of human factors that needs to be specifically incorporated in the road safety research conducted in Low-and-Middle Income Countries to avoid safety–critical situations. [ABSTRACT FROM AUTHOR]

• Effects of 0.03%, 0.05%, and 0.08% BACs on driving errors of Indian drivers were analysed. • 82 participants were recruited for the driving simulator experiments. • Longitudinal and lateral driving error measures were examined. • Significant driving error tendencies were observed at 0.03% BAC, which is the maximum legal limit for driving in India. Driving error is an intrinsic component of unsafe driving behaviour. Even though alcohol results in substantial crash fatalities on Indian roads, there is hardly any evidence-based research on alcohol impairment in the driving errors of Indian drivers. The present study examined the effects of differentiated Blood Alcohol Concentration (BAC) levels (0%, 0.03%, 0.05%, and 0.08%) on the driving errors of Indian drivers. 82 volunteers participated in the driving simulator study and drove in rural and urban environments in a randomized and counterbalanced order. Generalized negative binomial regression models were developed for longitudinal and lateral driving error measures. Compared to the sober state, the increments in speed limit violations were 53%, 138% and 143% in rural driving environment, and 68%, 89%, 95% in urban driving environment at 0.03%, 0.05% and 0.08% BACs. While 0.03% and 0.05% BACs did not significantly influence the lane excursions in rural environment, 0.08% BAC resulted in 107% increment; corresponding increments were 71%, 84%, and 148% in urban environment. In rural environment, 0.03%, 0.05% and 0.08% BACs resulted in 28%, 39% and 58% increments in low RPM frequencies (RPM < 1000), and 76%, 95% and 106% increments in high RPM frequencies (RPM > 5000). In urban environment, the increments were 35%, 56% and 71% for low RPMs at 0.03%, 0.05% and 0.08% BACs; whereas, for high RPMs, the increments at 0.05% and 0.08% BACs were 45% and 67% respectively (no significant influence of 0.03% BAC). The study identified the significant risk factors influencing driving errors, thereby sketching the risky driving profiles. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Highlights: [•] Critical and comparative assessments of existing road user charging (RUC) systems. [•] Demonstration of system architecture for GNSS based dynamic RUC system. [•] Examination of the feasibility of the proposed truly dynamic charging system. [ABSTRACT FROM AUTHOR]

In this research, a passenger-centric passenger information system is proposed, which uses crowd sourcing and mobile phones so that passengers are not only information consumers but are also providers of information to the system. Passengers can allow the system to track their location through their smart phones when they are traveling on public transportation; this will compensate for the lack of a vehicle tracking system in public transportation, particularly in nonurban areas. Map-matching (MM) algorithms integrate location data (i.e., latitude and longitude) obtained from positioning sensors (in this case, mobile GPS) with a digital geographic information system (GIS) road map. Any map-matching algorithm first identifies the road link on which a vehicle is traveling and then determines the vehicle's location on that road segment. In the proposed information system, at a given point of time, a number of vehicle locations (latitude and longitude) are received from passengers traveling on a bus. In order to provide a precise vehicle location at a given point of time, a novel map-matching algorithm using fuzzy logic, which integrates multiple vehicle locations (obtained from passenger's smart phones) with a GIS road map, has been developed. The developed map-matching algorithm was tested using real-world data collected on four different bus routes in Aberdeenshire, Scotland, and also GPS data collected in and around Nottingham, United Kingdom. It was identified that the developed MM algorithm is efficient and capable of supporting the proposed passenger information system. [ABSTRACT FROM AUTHOR]

Map-matching algorithms integrate data from positioning sensors with a digital map in order, first, to identify the road link on which a vehicle is traveling, and second, to determine the vehicle's location on that link. Due to errors in positioning sensors, digital maps, and the map-matching (MM) process, MM algorithms sometimes fail to identify the correct road segment from the candidate segments. This phenomenon is known as mismatching. Identification of the wrong road link may mislead users and degrade the performance of a location-based intelligent transportation system (ITS) and services. The main objective of this article is to improve a topological map-matching (tMM) algorithm by error detection, correction, and performance re-evaluation. Errors in a tMM algorithm were determined using data comprising 62,887 positioning points collected in three different countries (the United Kingdom, the United States, and India). After map matching, each mismatched case was examined to identify the primary causes of the mismatches. A number of strategies were developed and applied to reduce the risk of mismatching thus enhancing the tMM algorithm. An independent data set of 5,256 positioning points collected in and around Nottingham, UK, was employed to re-evaluate the performance of the enhanced tMM algorithm. The original tMM algorithm correctly identified the vehicle's position 96.5% of the time; after enhancement this increased to 97.8%. This compares very well with the performance of tMM algorithms reported in the literature. The enhanced tMM algorithm developed in this research is simple, fast, efficient, and easy to implement. Since the accuracy offered by the enhanced algorithm is found to be high, the developed algorithm has potential to be implemented in real-time location-based ITS applications. [ABSTRACT FROM AUTHOR]

Abstract: Rural communities face a range of challenges associated with accessibility and connectivity which apply in both the physical and virtual sphere. Constraints in rural transport infrastructure and services are often compounded by limitations in the development and resilience of technological infrastructures. In this context there is significant disparity between urban and rural communities. This paper will examine the context for accessibility and connectivity in rural communities highlighting key transport and technology challenges. It also explores barriers and opportunities to bringing together transport and technology solutions to enhance rural accessibility and connectivity. This is an area where current understanding is weak as most research has been focussed on urban environments. The paper focuses specifically on two issues of current research; firstly, the role of information and associated technologies in supporting rural passengers on public transport, secondly, the use of technologies to support flexible and demand responsive transport services in rural areas. [Copyright &y& Elsevier]

Abstract: Map-matching (MM) algorithms integrate positioning data from a Global Positioning System (or a number of other positioning sensors) with a spatial road map with the aim of identifying the road segment on which a user (or a vehicle) is travelling and the location on that segment. Amongst the family of MM algorithms consisting of geometric, topological, probabilistic and advanced, topological MM (tMM) algorithms are relatively simple, easy and quick, enabling them to be implemented in real-time. Therefore, a tMM algorithm is used in many navigation devices manufactured by industry. However, existing tMM algorithms have a number of limitations which affect their performance relative to advanced MM algorithms. This paper demonstrates that it is possible by addressing these issues to significantly improve the performance of a tMM algorithm. This paper describes the development of an enhanced weight-based tMM algorithm in which the weights are determined from real-world field data using an optimisation technique. Two new weights for turn-restriction at junctions and link connectivity are introduced to improve the performance of matching, especially at junctions. A new procedure is developed for the initial map-matching process. Two consistency checks are introduced to minimise mismatches. The enhanced map-matching algorithm was tested using field data from dense urban areas and suburban areas. The algorithm identified 96.8% and 95.93% of the links correctly for positioning data collected in urban areas of central London and Washington, DC, respectively. In case of suburban area, in the west of London, the algorithm succeeded with 96.71% correct link identification with a horizontal accuracy of 9.81m (2σ). This is superior to most existing topological MM algorithms and has the potential to support the navigation modules of many Intelligent Transport System (ITS) services. [Copyright &y& Elsevier]

• Braking performance of drivers was analysed at 0%, 0.03 %, 0.05 %, and 0.08 % BACs. • Fifty-four young drivers participated in the driving simulator experiment. • Parametric survival analysis was used to model brake transition time. • Brake transition times were higher in rural scenario compared to urban scenario. • Significant reductions in brake transition time were observed with increasing BACs. 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. [ABSTRACT FROM AUTHOR]

• Effects of partially sleep deprivation on driver safety were analysed during car-following. • Safety performance was modelled using longitudinal safety indicators. • Increase in speed variability and smaller headways showed poor safety performance. • Decline in safety performance led to increase in 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. [ABSTRACT FROM AUTHOR]

• Study explored relation between drivers' risk perception towards distracting tasks and actual driving performance. • Driving performance subgroups were then divided into the following crash risk probabilities. • Though conversation was not an extremely risky, high crash risk was observed during the task. • In some cases, crash risk was high even for music player and eating tasks that were perceived less risky. The present study attempts to explore the association of drivers' risk perception towards phone usage as well as other everyday distractions (operating a music player and eating during driving), and their driving performance observed during these distracted conditions. For this purpose, driving simulator experiments were conducted with 90 participants to collect their driving performance data and a questionnaire was conducted to obtain their basic details along with their risk perceptions. Firstly, the driving performance was divided into clusters using hierarchical clustering and the clustered subgroups were compared for crash and non-crash cases to identify the groups having significant performance degradation. Based on this comparison, the driving performance subgroups were then divided into the following crash risk probabilities: High risk, Moderate risk and Low risk. Further, the associations of perceived risk with these performance subgroups and other potential factors were analyzed using association rules mining technique. Most of the drivers (72.06%) reported texting as an extremely risky task. But, surprisingly none of them considered conversation as an extremely risky task. However, in case of conversation, it was found that even though the professional drivers reported the task to be not at all risky, the observed crash risk was high for them (S = 5.21%, C = 67.86%), indicating an underestimation of the associated risk by the drivers. Similarly, the results revealed that for music player and eating tasks, drivers reported the distracting tasks to be less risky, but, in some instances, their driving performance was associated with higher chances of crash occurrence. Many interesting associations of risk perception and driving performance with respect to demographic and driving characteristics were also obtained. The findings can be useful while designing the awareness programs related to distracted driving with an aim to reduce such practices. [ABSTRACT FROM AUTHOR]

• Classical IDM is extended for lateral and longitudinal motion planning. • All the surrounding obstacles are considered to entice a drivers' response. • 8 new model parameters are introduced to emulate human-like response. • A discretionary lateral acceleration model is developed. • A fractional factorial design approach is used to calibrate the model parameters. This study aims to model two-dimensional (lateral and longitudinal) motion of an Ego Vehicle (EV). Intelligent Driver Model (IDM) is enhanced for this purpose. All the surrounding obstacles (vehicles and road boundaries) are considered as stimuli that elicit driver's reactions (lateral and longitudinal accelerations). A Virtual LiDAR sensor on the EV perceives the surrounding. Proximity and speed are used to compute longitudinal and lateral components of average relative velocity. A new formulation is presented to compute the effective gap considering all the surrounding obstacles. It is composed of four components: front, rear, left, and right. The former two affect longitudinal acceleration, while the latter two influence lateral acceleration of the EV. A discretionary lane-change model is also developed to account for the driver's deliberate (not just mandatory) lateral movements. A total of 13 model parameters are used, of which eight are newly introduced. These model parameters are calibrated using 1050 human-driven car trajectories from Next Generation SIMulation dataset. Taguchi's fractional factorial design principle is used to optimize the parameters. Gray relational analysis indicated that the three newly introduced parameters to be the three most influential parameters. Validation using another 450 car trajectories resulted in a mean radial error of 3.97 m over a horizon of 10 s. The performance measures of two-dimensional motion planning (model error, longitudinal error, and mixed gap error) are found to be better than those reported in the literature. The developed human-like AV-IDM offers a proof of concept. [ABSTRACT FROM AUTHOR]

• Crash risk at unsignalised intersections increased during eating and drinking tasks. • No compensatory measure was adapted for eating and drinking tasks while crossing intersections. • Texting task resulted in deteriorated situation awareness while approaching the intersections. • Drivers who drove frequently kept larger safety margins while crossing the intersections. The current study shows the impact of distraction in designing the intelligent in-vehicle systems for assisting the drivers to make stop/cross decision at the onset of yellow signal. In total, 83 participants drove through a simulated urban scenario with six signalised intersections. Firstly, the time taken to execute the stop/cross decision was statistically modelled by using Weibull AFT (Accelerated Failure Time) models. The results showed that compared to the baseline, eating and drinking tasks reduced the stopping time by 6% and 7% respectively. For the crossing encounters, the eating task caused 12% increment in crossing time compared to the baseline. This analysis was followed by modelling the success rates of the executed decisions with binary logistic models. The success rates of the stopping decision showed that reduction in the time lapsed in executing the decision led to failure in stopping the vehicle before the stop line. Similarly, an increment in the time taken to execute the crossing decision led to reduced probability of successfully crossing the stop line within the yellow duration. Hence, the results suggest that the design of the smart assistance system for decision making at the onset of yellow signal should be based on the success rate of the decision which is dependent on the time lapsed in executing the decision. Moreover, the presence of distracting activities should be considered as an input parameter while designing the assistance system. [ABSTRACT FROM AUTHOR]

• Initial response time delayed by 25-44% with partial sleep loss. • Drivers with habitual sleep ≤ recommended 7-8 h, are at higher risks of sleep loss impairments. • Driving experience does not compensate for sleep loss effects in unfamiliar hazard scenarios • Total braking time reduced in 'pedestrian event' and increased in 'vehicle merging event'. 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 ± 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. [ABSTRACT FROM AUTHOR]

• Driving simulator experiments were conducted at 0%, 0.03 %, 0.05 %, and 0.08 % BACs. • Eighty-two Indian drivers participated in the experimental study. • Effects of alcohol on speeding behaviour and accident probabilities were analysed. • Significant increments in driving speed were observed with increasing BACs. • Accident probabilities were higher in urban settings compared to rural for all BACs. 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• Introduces a new duration-based framework for predicting future crashes and their severities. • Demonstrates application of the novel framework using data from four interstate segments in two major cities in Tennessee. • Investigates the sampling of training data to reduce estimation complexity. • Variables sensitive to sampling are identified. • Validation reveals greater accuracy in predictions on segments where crashes are more frequent. 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). [ABSTRACT FROM AUTHOR]

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 fifteen countries based on severity of COVID-19. Lockdown stringency and six daily mobility measures were selected to study the change in road mobility patterns due to lockdown measures. The fifteen countries were categorized into two clusters (C1 and C2) and generalized linear mixed model was considered for analysis. The model results revealed that stringent lockdown measures and high residential mobility reduced Crash Fatality Ratio (CFR) by 0.36% and 1.84%, respectively in cluster C2. Further, in cluster C1, travel restrictions on workplace and recreational activities decreased CFR up to 0.31% and 0.26%, respectively. Thus, overall model results specified that stringent law enforcement and appropriate compliance from the road users can effectively lessen traffic-related fatalities in unprecedented times. [ABSTRACT FROM AUTHOR]

This paper addresses the optimization of make-to-order hybrid manufacturing - remanufacturing system to make capacity and inventory decisions jointly along with production decisions. The proposed system considers a common production facility and the same assembly/disassembly line to perform manufacturing and remanufacturing operations simultaneously. The current study takes into account an environment where new and remanufactured (reman) products competing to each other that is, the common demand stream for both products but different selling prices. Furthermore, the relative capacity consumed by remanufacturing over the manufacturing is explained in two ways, namely less capacity intensive case and more capacity intensive case. Differently, from previous studies, we consider a scenario with a discounted selling price for reman products, shortage penalty costs, lost sales, disposal and uncertainty in demand, amount and yield of returns. Hence, to handle those uncertainties, a scenario-based stochastic programming model in a two-stage setting is presented. In the first stage, the raw material inventory and production capacity levels are planned and in the second stage, the production, inventory and disposal decisions are determined by balancing overage and underage costs. The results indicate that net values associated with new and reman products can be decisive in choosing either manufacturing or remanufacturing. [ABSTRACT FROM AUTHOR]

• Distraction impacts on overall driving performance was estimated by single measure. • Both direct and indirect impacts of distracted driving on crash risk were examined. • Non consideration of indirect impacts results in underestimation of crash risk. • Texting and operating the music player tasks had resulted in increased crash risk. 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. [ABSTRACT FROM AUTHOR]

Highlights • The study explores the relationship of speed and speed variations with crash rates. • Crashes by vehicle (HV/LV) and severity types (serious/injury) are examined. • Both between-lanes and within lane speed variations are considered in the analysis. • Higher within lane speed variation in higher traffic volumes lead to more crashes. • Combinations of traffic factors are more influential than their individual effects. Abstract Speed variations are identified as potentially important predictors of freeway crash rates; however, their impacts on crashes are not entirely known. Existing findings tend to be inconsistent possibly because of the different definitions for speed variations, different crash type consideration or different modelling and data aggregation approaches. This study explores the relationships of speed variations with crashes on a freeway section in the UK. Crashes split by vehicle type (heavy and light vehicles) and by severity mode (killed/serious injury and slight injury crashes) are aggregated based on the similarities of the conditions just before their occurrence (condition-based approach) and modelled using Multivariate Poisson lognormal regression. The models control for speed variations along with other traffic and weather variables as well as their interactions. Speed variations are expressed as two separate variables namely the standard deviations of speed within the same lane and between-lanes over a five-minute interval. The results, similar for all crash types (by coefficient significance and sign), suggest that crash rates increase as the within lane speed variations raise, especially at higher traffic volumes. Higher speeds coupled with greater volume and high between-lanes speed variation also increase crash likelihood. Overall, the results suggest that specific combinations of traffic characteristics increase the likelihood of crash occurrences rather than their individual effects. Identification of these specific crash prone conditions could improve our understanding of crash risk and would support the development of more efficient safety countermeasures. [ABSTRACT FROM AUTHOR]