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

Connected and automated vehicles (CAVs) are expected to revolutionise transport worldwide and transform urban life. However, there are many unknowns concerning the impacts of these technologies in terms of sustainability, justice, and safety. It has been suggested that CAVs may exacerbate inequities and safety disparities concerning the interaction of vulnerable road users (VRUs) with motorised transport. This paper investigates the justice issues that CAVs policy needs to address concerning VRUs. Our approach to studying CAVs’ capabilities and their potential perverse outcomes uses transport justice as an evaluative framework. The justice-related outcomes discussed include: traffic injuries, impact on sharing road responsibilities, loss of on-street space, access to technology, inclusion for disabled and older adults, CAV technological development, and impact of congestion and air pollution. The paper proposes a future research agenda emphasising areas where CAVs may positively impact VRUs.@en

Drivers continually interact with other road users and use information from the road environment to make decisions to control their vehicle. A clear understanding of different parameters impacting this interaction can provide us with a new design approach for a more effective driver assistance system - a personalised trajectory prediction system. This paper highlights the influential factors on trajectory prediction system performance by (i) identifying driver behaviours impacting the trajectory prediction system; and (ii) analysing other contributing factors such as traffic density, secondary task, gender and age group. To explore the most influential contributing factors, we first train an interaction-aware trajectory prediction system using time-series data derived from the Second Strategic Highway Research Program (SHRP2) Naturalistic Driving Study (NDS). Prediction error is then analysed based on driver characteristics such as driver profile which is subjectively measured through self-reported questions, and driving performance which is based on evaluation of time-series information such as speed, acceleration, jerk, time, and space headway. The results show that prediction error significantly increased in the scenarios where the driver engaged in risky behaviour. Analysis shows that trajectory prediction system performance is also affected by factors such as traffic density, engagement in secondary tasks, driver gender and age group. We show that the driver profile, which is subjectively measured using self-reported questionnaires, is not as significant as the driving performance information, which is objectively measured and extracted during each specific driving scenario.@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

In Australia, the number of collisions at level crossings (LCs) remains stable and even increased between 2003 and 2011. A consistent number of crashes between trains and pedestrians compared to a decreasing number of crashes between trains and vehicles calls for a better understanding of the factors underpinning pedestrians’ decision-making at such risky intersections. This study reports on an innovative method used to measure pedestrians’ crossing intentions and risk perceptions in crossing scenarios recorded at three representative LCs for Queensland (Australia). Predictors of crossing intentions were examined and compared between five scenarios. An online survey featuring the footages of the crossing scenarios was completed by 222 participants. The results revealed that pedestrians’ intentions to transgress depend on the situation, justifying the use of such methodology. Pedestrians perceived the lowest risk and reported the highest likelihood of risk-taking after the pedestrian lights are active, but before the pedestrian gates have started moving, confirming that pedestrian gates are an efficient safety measure. It was found that pedestrians perceive others as more prone to risk-taking at LCs, but not as being more at risk of fatal consequences, suggesting that the risk of being hit by a train as a pedestrian is generally perceived as low. Past unsafe behaviour, descriptive norms and perceived risk of being involved in a crash were found to be consistent predictors of the likelihood to engage in risk-taking behaviour at LCs across the different scenarios. Implications for future research and strategies to reduce habitual unsafe behaviour are discussed.@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