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

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

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

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

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

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

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

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

The objective of this study is to develop an accurate model for corridor-level travel-time estimation. Different approaches, such as k-nearest neighbour (k-NN), gradient boosting decision tree (GBDT) and support vector machines (SVMs), were used in this study. Further, this study also developed a hybrid model combining a data-driven approach (SVM) and a model-based approach [particle filter (PF)] for corridor-level travel-time estimation. Both static and dynamic parameters, such as road geometry, intersection length, location information from Global Positioning System devices, dwell time etc. were used as influential factors for modelling. The proposed algorithm was tested on a study corridor of length 59.48 km, in the arterials of Mumbai, India. The data was collected using a probe-vehicle technique for five days during the morning peak period (from 8.00 am to 11.00 am) for two modes (car and bus). The mean absolute percentage error values obtained for the hybrid model for the two modes were: 9.96 (car) and 11.24 (bus). The performance of the proposed hybrid (SVM-PF) algorithm showed a clear improvement in accuracy in comparison to existing standard methods such as k-NN, GBDT and SVM.

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.

Objective: The present study examines the accelerating and braking behaviors of drivers at different blood alcohol concentrations (BACs) in heterogeneous driving conditions using driving simulator experiments. Methods: Eighty-two licensed drivers performed simulated driving in a rural road environment designed in the driving simulator at 4 BAC levels: 0.00, 0.03, 0.05, and 0.08%. Driving performance was analyzed using vehicle control variables such as mean acceleration and mean brake pedal force. Generalized linear mixed models were developed to quantify the effect of different alcohol levels and explanatory variables such as driver’s age, gender, and other factors on the driving performance indicators. Results: Alcohol use was reported as a significant factor affecting the accelerating and braking performance of drivers. The acceleration model results indicated that drivers’ mean acceleration increased by 0.013, 0.026, and 0.027 m/s2 for BAC levels of 0.03, 0.05, and 0.08%, respectively. Results of the brake pedal force model showed that drivers’ mean brake pedal force increased by 1.09, 1.32, and 1.44 N for BAC levels of 0.03, 0.05, and 0.08%, respectively. Age was a significant factor in both the models where a 1-year increase in driver age resulted in a 0.2% reduction in mean acceleration and a 19% reduction in mean brake pedal force. Driving experience could compensate for the negative effects of alcohol to some extent while driving. Conclusions: The findings of the present study revealed that drivers tend to be more aggressive and impulsive under the influence of alcohol, which deteriorates their driving performance. Impairment in accelerating and braking behaviors of drivers under the influence of alcohol leads to increased crash probabilities. The conclusions may provide reference in making countermeasures against drinking and driving and contribute to traffic safety.

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.

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 (

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

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

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

Objective: The present study aims to quantify the effects of texting and driver behavior on the accident risk associated with a sudden event. Further, the study attempts to compare the effects of driving behavior of inexperienced young and professional drivers on risk during predetection and postdetection phases of the event. Methods: Forty-nine drivers from 2 categories—inexperienced young drivers and experienced professional drivers—took part in simulated experiments. The participants drove in a free-flow road environment under 3 driving conditions: no distraction (baseline) and writing short and long texts while driving. The participants were exposed to a sudden hazardous event during each drive. Accident probability during the sudden event was modeled with a generalized linear mixed model (with a logit link function). Results: As expected, both texting tasks increased accident risk, and the risk was much higher for inexperienced young drivers than for professional drivers. Time lapsed in reducing speed increased the odds for accident risk significantly. A comparative analysis of the driver categories showed that impairment in driving behavior due to the texting tasks was similar for both groups during the predetection phase. However, the risk associated with the texting tasks was higher for young drivers during the postdetection phase. A possible reason could be that young drivers had 65% and 75% higher approach speeds (than the professional drivers) during the short and long text tasks, respectively. Conclusions: The results provide statistical evidence that increased speed is expressed as increased risk-taking behavior among young drivers, which subsequently is the main reason for their higher accident risk during texting tasks. Moreover, the results confirm that professional drivers are not able to mitigate the increased accident risk associated with texting tasks due to late detection of the event during the tasks.

Real-time passenger information (RTPI) systems have been identified as having benefits in terms of passenger willingness to travel by public transport and their satisfaction levels with services provided. The lack of this amenity in rural areas, however, may hamper public transport use, thus reinforcing patterns of over-reliance on personal vehicles. To explore the potential impacts of providing RTPI in rural areas, a smartphone application ( GetThereBus ) was developed to allow rural bus passengers to share real-time public transport data, and access real-time and timetable information. Through user testing of GetThereBus , this work aimed to address questions related to the impact of the limited availability of rural digital infrastructure on the provision of RTPI; the potential for crowdsourced information to supplement published timetable information given digital limitations; and the potential impacts of such a system on the traveller experience. This study describes the GetThereBus development and evaluation phases. The authors found it was possible to design and develop a system that overcame many of the technological limitations experienced in rural areas, and users reported a positive response to the system. However, despite a campaign of user engagement, it proved difficult to recruit and motivate sufficient users to provide the data needed to achieve area-wide coverage.

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 (p0.001) 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.

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.

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

Individuals in rural areas are often provided with little or no information regarding public transport disruptions. This has strong impact on those with limited access to alternative motorised transport. Though an increasing number of real time passenger information (RTPI) systems are being developed their role in supporting travellers during service disruption is poorly understood, particularly in rural areas. In this paper, we first illustrate and categorise travel disruptions. Requirements for RTPI — particularly for rural public transport users — are then identified for each type and stage of disruption through interviews and focus groups with rural passengers. Patterns of passenger behaviour during travel and transport disruptions are also classified. Further, a conceptual model of the recovery phases of disruption has been developed to map the RTPI requirements for each recovery phase of disruption. The model has been developed and evaluated through a series of focus groups and interviews with passengers, transport service providers, and government agencies. Finally, the paper also discusses and suggests the necessary advances in digital technologies for RTPI systems required to support public transport users during disruptions and thus minimise the number of trips abandoned.

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.

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.

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.

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.

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.

The availability of real-time passenger information (RTPI) is a key factor in making public transport both accessible and attractive to users. Unfortunately, rural areas often lack the infrastructure necessary to provide such information, and the cost of deploying and maintaining the required technologies outside of urban areas is seen as prohibitive. In this paper we present the GetThere system developed to overcome such issues and to provide public transport users in rural areas with RTPI. An ontological framework for representing mobility information is described, along with the Linked Data approach used to integrate heterogeneous data from multiple sources including government, transport operators, and the public. To mitigate possible issues with the veracity of this data, a quality assessment framework was developed that utilises data provenance. We also discuss our experiences working with Semantic Web technologies in this domain, and present results from both a user trial and a performance evaluation of the system. (C) 2017 Elsevier B.V. All rights reserved.

In this research, a real-time positioning method, which utilises crowdsourced positioning data obtained from smartphone GPS is developed. Such vehicle location information obtained from crowdsourcing and smartphones in public transport could replace traditional automatic vehicle location systems. However, the location information from smartphone GPS is more erroneous. The proposed methodology serves as an alternative to existing positioning methods to improve the vehicle positioning accuracy. The developed enhanced particle filter algorithm takes smartphone GPS positioning data [from multiple passengers in a single transit vehicle (e.g. bus)] as input data. This `crowdsourced' data can then be utilised to calculate the vehicles' positioning information with better accuracy using the developed enhanced particle filter algorithm. The developed algorithm was tested using data collected on 14 different bus routes in urban and suburban areas of Mumbai, India, and it was identified that the algorithm is effective in reducing the average error up to 21.3% from a regular smartphone GPS and 10% from extended Kalman filter algorithm and was able to curtail positioning error within 8.672 m (average over 14 routes).

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. (C) 2017 Elsevier Ltd. All rights reserved.

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.

Objective: Road crashes are considered as the eighth leading causes of death. There is a wide disparity in crash severity and law enforcement efficiency among low-, medium-, and high-income countries. It would be helpful to review the crash severity trends in these countries, identify the vulnerable road users, and understand the law enforcement effectiveness in devising efficient road safety improvement strategies.Method: The crash severity, fatality rate among various age groups, and law enforcement strategies of 10 countries representing low-income (i.e., India and Morocco), medium-income (i.e. Argentina, South Korea, and Greece), and high-income (i.e., Australia, Canada, France, the UK, and the United States) are studied and compared for a period of 5years (i.e., 2008 to 2012). The critical parameters affecting road safety are identified and correlated with education, culture, and basic compliance with traffic safety laws. In the process, possible road safety improvement strategies are identified for low-income countries.Results: The number of registered vehicles shows an increasing trend for low-income countries as do the crash rate and crash severity. Compliance related to seat belt and helmet laws is high in high-income countries. In addition, recent seat belt- and helmet-related safety programs in middle-income countries helped to curb fatalities. Noncompliance with safety laws in low-income countries is attributed to education, culture, and inefficient law enforcement.Conclusion: Efficient law enforcement and effective safety education taking into account cultural diversity are the key aspects to reduce traffic-related injuries and fatalities in low-income countries like India.