In the published article, there was an error in the Data Availability statement. Additional information needs to be added to the Data Availability Statement. The original statement established: The SHRP 2 dataset is currently managed by the Virginia Tech Transportation Institute (VTTI) and is made available to support research efforts. As the data for this dataset was obtained from volunteers, it qualifies as Human Subjects Research, and its usage is restricted. Therefore, obtaining access to both the SHRP2 dataset and the NEST dataset is subject to obtaining a data use license. The correct Data Availability statement appears below.

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Mobile phone use is one of the most frequent causes of distraction among drivers. While there have been a significant number of studies that have examined individuals’ intentions to use a mobile phone while driving, the influence of individuals’ in-situ judgement of driving conditions has received considerably less attention. The aim of this investigation was to provide a systematic understanding of how factors associated with the driving context and environment influence a driver's decision to engage in mobile phone use while driving. Following a systematic classification scheme, 41 research articles from the years 2011 to 2020 were reviewed and synthesised to identify the contextual determinants of mobile phone distraction. Overall, the findings provided support for the role that contextual features play in influencing individuals’ mobile phone use engagement. This finding was particularly the case in instances where mobile phone tasks required relatively high cognitive and physical demands on an individual, such as texting and/or reading mails. The findings also indicated that as contextual complexity increases, mobile phone use decreases as well. A deeper understanding of the relationship between contextual factors and phone use while driving may aid in the design of more efficient driver support systems and the development of distraction-sensitive road design guides. This understanding can also assist in the identification of mobile phone use hotspots and the improvement of law enforcement and educational strategies to prevent the behaviour.

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Fully automated vehicles (FAVs) have the potential to improve road safety and reduce traffic congestion and emissions. Most studies of acceptance of FAVs have focused on motor vehicle users, largely ignoring other road users, such as cyclists. This study investigates the factors that influence cyclists’ receptivity towards sharing roads with FAVs and their behavioural intentions in interactions with FAVs. The online survey collected information on participant demographics (e.g. age, gender, crash experience), self-reported on-road cycling behaviours (e.g. violations, errors, positive behaviours) and their receptivity towards sharing roads with FAVs (e.g. attitude, social norms, trust). Three typical cyclist-vehicle interaction scenarios were presented to test the cyclists’ intention to engage in self-protective behaviours (e.g. giving a hand signal, giving way or moving over) during the interaction with a FAV. Three hundred and fourteen Australian adults (106 females vs 208 males) who had ridden a bicycle at least once in the past year completed the survey. The results show that older cyclists and male cyclists had a lower receptivity towards sharing roads with FAVs than younger cyclists and female cyclists, respectively. Cyclists who reported being involved in a bicycle crash in the last two years and those who reported committing more errors on roads were more willing to share roads with FAVs. Cyclists who had a higher propensity to risky behaviours and positive behaviours were less likely to take intended self-protective behaviours during interaction with FAVs. Findings of the study provide some insights from the cyclist's perspective to facilitate the development and implementation of automated vehicles.@en

Introduction: Driver distraction has been recognized for a long time as a significant road safety issue. It has been consistently reported that drivers spend considerable time engaged in activities that are secondary to the driving task. The temporary diversion of attention from safety-critical driving tasks has often been associated with various adverse driving outcomes, from minor driving errors to serious motor vehicle crashes. This study explores the role of the driving context on a driver’s decision to engage in secondary activities non-critical to the driving task. Method: The study utilises the Naturalistic Engagement in Secondary Tasks (NEST) dataset, a complementary dataset derived from the SHRP2 naturalistic dataset, the most extensive naturalistic study to date. An initial exploratory analysis is conducted to identify patterns of secondary task engagements in relation to context variables. Maximum likelihood Chi-square tests were applied to test for differences in engagement between types of driver distraction for the selected contextual variables. Pearson residual graphs were employed as a supplementary method to visually depict the residuals that constitute the chi-square statistic.Lastly, a two-step cluster analysis was conducted to identify common execution scenarios among secondary tasks. Results: The exploratory analysis revealed interesting behavioral trends among drivers, with higher engagement rates in left curves compared to right curves, while driving uphill compared to driving downhill, in low-density traffic scenarios compared to high-density traffic scenarios, and during afternoon periods compared to morning periods. Significant differences in engagement were found among secondary tasks in relation to locality, speed, and roadway design. The clustering analysis showed no significant associations between driving scenarios of similar characteristics and the type of secondary activity executed. Discussion: Overall, the findings confirm that the road traffic environment can influence how car drivers engage in distracted driving behavior.@en

Despite the promised benefits, the introduction of Automated Vehicles (AVs) on roads will be confronted by many challenges, including public readiness to use those vehicles and share the roads with them. The risk profile of road users is a key determinant of their safety on roads. However, the relation of such risk profiles to road users’ perception of AVs is less known. This study aims to address the above research gap by conducting a cross-sectional survey to investigate the acceptance of Fully Automated Vehicles (FAVs) among different non-AV-user groups (i.e., pedestrians, cyclists, and conventional vehicle drivers). A total of 1205 road users in Queensland (Australia) took part in the study, comprising 456 pedestrians, 339 cyclists, and 410 drivers. The Theory of Planned Behaviour (TPB) is used as the theoretical model to examine road users’ intention towards sharing roads with FAVs. The risk profile of the participants derives from established behavioural scales and individual characteristics are also included in the acceptance model. The study results show that pedestrians reported lowest intention in terms of sharing roads with FAVs among the three groups. Drivers and cyclists in a lower risk profile group were more likely to report higher intention to share roads with FAVs than those in a higher risk profile group. As age increased, pedestrians were less likely to accept sharing roads with FAVs. Drivers who had more exposure time on roads were more likely to accept sharing roads with FAVs. Male drivers reported higher intention towards sharing roads than female drivers. Overall, the study provides new insights into public perceptions of FAVs, specifically from the non-AV-user perspective. It sheds light on the obstacles that future AVs may encounter and the types of road users that AV manufacturers and policymakers should consider closely. Specifically, groups such as older pedestrians and road users who engage in more risky behaviours might resist or delay the integration of AVs.@en

Automated vehicles have started to be integrated into the road transportation system and operate in a mixed traffic environment. To ensure a smooth and successful integration, it is vital to have a good understanding of the human factor challenges involved in the process, especially the issues related to other road users who will share roads with automated vehicles. The study focuses on conventional vehicle drivers’ acceptance of and interaction with fully automated vehicles (FAV). An online survey with experimental scenarios showing an FAV's lane-changing intention was designed to test the interaction responses of participants. The survey also collected the participants’ demographic information (e.g., age, gender, driving experience), self-reported general driving behaviours (e.g., errors, lapses and violations), past benchmark behaviour in the same situation and their acceptance of FAVs. The study recruited 838 participants in total, comprising 465 participants from Australia (216 males vs. 249 females) and 373 participants from China (172 males vs. 201 females). Ordered probit models were developed to predict three types of behavioural responses of drivers in the lane-changing scenario, i.e., positive, disregardful and aggressive responses. The results showed that older drivers, females, and drivers who had less driving experience were more likely to adopt positive interactions with FAVs than their counterparts. Drivers who reported frequent risky driving behaviours (e.g., aggressions, lapses and errors) were less likely to report positive interaction but more likely to report disregardful and aggressive interactions. Drivers reporting more positive/favourable attitudes and a higher trust toward FAVs demonstrated a higher possibility of positive interaction, and those with higher perceived behaviour control were more likely to restrain disregardful interaction. The study helps to form a greater understanding of conventional vehicle drivers’ perception of FAVs and the underlying factors that may influence their interaction behavioural tendency.@en

Cyclists are vulnerable road users and face disproportionately high rates of road trauma, especially in low- and middle-income countries. Behaviour of road users is a system outcome, and thus studying cyclist behaviours can identify problems in the whole road transport system and assist in generating long-lasting, cost-effective solutions to promote cyclist safety. This study aims to investigate the similarities and disparities of cycling behaviour among countries with different income levels and cycling prevalence, and the relationships among cyclist demographic characteristics, behaviours and crash involvement. It applies the Cycling Behaviour Questionnaire (CBQ) to measure the behaviour of cyclists in three countries: Australia (high-income, emerging cycling country), China (mid-income, traditional cycling country) and Colombia (low-income, emerging cycling country). A total of 1094 cyclists (Australia 347, China 368, and Colombia 379) with a mean age of 31.8 years completed the online questionnaire. Confirmatory Factor Analysis was used to examine the factorial structure of the CBQ. The results showed that the three-factor CBQ (i.e., Traffic Violations, Errors and Positive Behaviours) had a stable and uniform factorial structure across all three countries. Cyclists reported more frequent positive behaviours than risky behaviours across all three countries. The Australian cyclists reported more positive behaviours and fewer violations than cyclists from the other countries, which is likely to reflect the road environment, policy and enforcement differences. Male cyclists tended to engage in more risky riding than females and reported more crash involvement. Older cyclists reported less risky riding and less involvement in crashes than younger cyclists. Cyclists who rode more often reported more frequent risky riding. The study helps understand the prevalence of risky/positive cyclist behaviours among countries and provides insights for developing systematic countermeasures to improve cycling environment and cyclist safety.@en

Intentionally blocking the path of fully automated vehicles is an important dimension of pedestrians’ receptivity towards these vehicles. The monetary value of this behaviour can be obtained by asking pedestrians about their perception of the “fine” for blocking the path of a fully automated vehicle. Econometric modelling of the reported fine can shed more light on factors influencing pedestrians’ receptivity towards fully automated vehicles. However, development of such an econometric model is not straightforward due to the unique characteristics of the dependent variable: it has two fundamentally different states; it is right-truncated; and it may be fat-tailed. Despite fairly extensive methodological advancements in econometric modelling of pedestrian behaviour, there is no model that can adequately explain these characteristics. While a beta distribution in a hurdle setting has the potential to address the above complexities, its applicability in dealing with limited dependent variables in transport applications has remained, by and large, unexplored. This study aims to fill this gap by developing a new beta hurdle regression model that systematically considers the dual-state of a right-truncated dependent variable representing the fine associated with intentionally blocking a fully automated vehicle. The hypothesized model is empirically tested using data obtained from a survey administered in Queensland, Australia, and the results are compared with truncated lognormal, and truncated lognormal hurdle regression models. Results indicate that the hurdle models are superior to the non-hurdle model. The beta variant of the hurdle model provides a better statistical fit for the data that are near their right limit. In addition, parametrizing the variance of the beta distribution captures the additional heterogeneity in the data. Age, gender, education level, violations, attitudes, behaviours that appease social interactions, and perceived ease or difficulty of interacting with fully automated vehicles influence the likelihood and/or the propensity of the fine and thus are associated with the perceived monetary value of intentionally blocking the path of a fully automated vehicle.@en

Adoption of Automated Vehicles (AVs) within transport networks relies on the technology acceptance of not only AV users, but also other road users such as pedestrians. However, previous research has mostly focused on user acceptance of AVs and the receptivity of pedestrians towards AVs has been largely unexplored. This study aims to fill this gap by applying the Theory of Planned Behaviour (TPB), the Technology Acceptance Model (TAM), and the Unified Theory of Acceptance and Use of Technology (UTAUT) to investigate pedestrians’ intentions to cross a road in front of a fully AV. To achieve this goal, a 20-minute online questionnaire was administered in Australia and data were collected from a total of 485 participants (average age = 35.35 years, 51.5% female). Bivariate correlation analysis and hierarchical regression models were then applied on the data to investigate the association between pedestrian attributes and their behavioural intentions. The findings revealed that the TPB and the UTAUT explained 46% and 43% of the variance in intentions to cross a road in front of a fully AV, respectively, with perceived behavioural control (PBC) and subjective/social norms the most significant unique predictors of intentions within the TPB and UTAUT, respectively. The TAM, however, only explained 35% of the variance in intentions to cross a road in front of a fully AV. When added into Step 2 of the hierarchical regression, age accounted for additional variance above the TAM predictors, indicating that younger participants reported higher intentions to cross a road in front of a fully AV than older participants. Age was not a significant predictor of intentions when entered with the predictors of the TPB and UTAUT. This study provides support for the use of these theoretical models to understand pedestrians’ acceptance of AVs.@en

Mobile phone use is often considered to be the main source of distraction on the road. Gap acceptance at intersections is a frequent and complex driving task that requires high visual attention from drivers. This study aims to investigate the effect of mobile phone use on the gap acceptance manoeuvre at intersections. Different mobile phone use positions, intersection type, gap size and driver characteristics were considered in the study. A total of 41 licenced drivers drove in an advanced driving simulator in three phone use conditions: baseline (no phone use), using the phone under the steering wheel (covert) and using the phone above the steering wheel (overt). Drivers drove the simulator three times and experienced two intersection types (straight-forward vs. left-turn) and two gap sizes (4 s vs. 7 s) during each drive. A parametric accelerated failure time (AFT) duration model was developed to evaluate the intersection crossing completion time of drivers. The results showed no significant difference of gap acceptance behaviours between the two phone use positions. The distraction task did not affect drivers’ gap acceptance decision, but it increased the crossing completion time by over 10 % compared to baseline. Besides, drivers behaved conservatively at intersections while using a mobile phone, such as adopting a larger deceleration, waiting a longer time, and mainting a larger distance to the front vehicle, etc. However, these compensational behaviours were not helpful in improving the intersection traffic situation regarding both safety and efficiency. Intersection type and gap size were both significant factors of gap acceptance decision and crossing completion time. Additionally, younger drivers were more likely to accept a gap than older drivers, and female drivers spent longer time to cross the intersection than males.@en

Eco-safe driving is a promising approach to improve road safety while reducing transport emissions. The application of an eco-safe driving system is feasible with the support of vehicle-to-vehicle/infrastructure technologies. To guarantee system usability and safety appropriateness, a key precondition is to ensure that driver mental workload and visual demands required for using the system are reasonable. This study explored how drivers’ mental workload and visual demands were affected when driving with an eco-safe driving HMI (human-machine-interface). Four in-vehicle eco-safe HMI information conditions were evaluated, including baseline, advice only, feedback only, and advice & feedback. Two traffic scenarios (stop-sign intersection with traffic vs. stop-sign intersection without traffic) were simulated using an advanced driving simulator. Behavioural variables (e.g. brake force, acceleration), visual variables (e.g. blink metrics, pupil size) and subjective workload scores were collected from 36 licensed Australian drivers. The experiment results showed that the HMI prompted drivers to apply a smooth and stable brake force when they approached the intersection and a smooth acceleration when they left the intersection. Drivers’ mental workload indicated by visual measurements were consistent with their subjective reported workload levels. Drivers had a higher mental workload when they received and processed additional eco-safe information in the advice & feedback condition. An increase in mental workload induced by the in-vehicle cognitive task initiated more blink activities while the increase in visual demand caused by a complex road situation led to blink inhibition. The study shows the HMI could significantly promote eco-safe driving behaivours without causing excessive mental and visual workload of drivers.@en

Mobile phone distraction has been recognized as an adverse factor that degrades drivers’ performance on road. Although research showed that drivers take various compensatory strategies to minimize the risk in distracted driving, little consensus has been achieved regarding the actual change in collision risk because of compensatory behaviours. This study aims to investigate the impact of mobile phone use and drivers’ compensatory behaviours on the collision risk in a car-following situation. By using a high-fidelity driving simulator, 37 participants completed the simulation experiment in three mobile phone use conditions: no phone (baseline), hands-free and hand-held. Cluster analysis was adopted to classify the final collision risk into different levels. Two logit regression models were developed to examine the relationships between drivers’ characteristics, mobile phone use, collision avoidance performances and their involvement in the collision risk. Results show that compared to no phone and hands-free, drivers using hand-held phone had a longer brake reaction time and also an increased likelihood of being involved in a high risk group. Drivers compensated to reduce the likelihood of safety-critical events through a simultaneous control of car-following speed and distance (i.e. Time-to-collision (TTC)) in distracted condition. Additionally, the results also indicated that female drivers and non-professional drivers were more likely to be involved in high risk group than male drivers and professional drivers. The study provided a systematic method to quantify the impact of mobile phone distraction and drivers’ compensation behaviors on collision risk. The effectiveness of compensatory strategy by controlling TTC also shed light on the development of intelligent transport systems to help distracted drivers avoid safety-critical situations.@en