Factors reducing the detectability of train horns by road users: A laboratory study.

Level crossing safety is a well-researched safety issue worldwide, but little attention has been placed on the safety benefits of using train horns when a train approaches a level crossing. Given train horns' adverse effects on the health and well-being of residents living near rail tracks, the use of train horns must be beneficial to safety. The current study sought to determine in a laboratory environment whether road users (N = 31) can detect the range of train horns observed in Australia in terms of loudness and duration, using high-definition audio recordings from railway crossings. A repeated measures design was used to evaluate the effects of key factors likely to influence the detectability of train horns, including, visual and auditory distractive tasks, hearing loss and environmental noise (crossing bells). Train horn detectability was assessed based on participants' accuracy and reaction times. Results indicated the duration of the train horn had the most influential effect on the detectability of train horns, with short-duration train horns less likely to be detected. The presence of bells at a crossing was the second most important factor that limited train horn detection. Train horn loudness also affected detectability: faint blasts were less likely to be noticed, while loudest blasts were more likely to be noticed. However, loud horns reduced the ability to detect the side from which the train was approaching and may result in longer times to detect the train, in the field. The auditory distractive task reduced the train horn detection accuracy and increased reaction time. However, the visual distractive task and medium to severe hearing loss were not found to affect train horn detection. This laboratory study is the first to provide a broad understanding of the factors that affect the detectability of Australian train horns by road users. The findings from this study provide important insights into ways to reduce the use and modify the practice to mitigate the negative effects of train horns while maintaining the safety of road users.

Driver influence on vehicle trajectory prediction.

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.

Pedestrians distracted by their smartphone: Are in-ground flashing lights catching their attention? A laboratory study.

Pedestrian distraction is a growing road safety concern worldwide. While there are currently no studies linking distraction and pedestrian crash risk, distraction has been shown to increase risky behaviours in pedestrians, for example, through reducing visual scanning before traversing an intersection. Illuminated in-ground Light Emitting Diodes (LEDs) embedded into pathways are an emerging solution to address the growing distraction problem associated with mobile use while walking. The current study sought to determine if such an intervention was effective in attracting the attention of distracted pedestrians. We conducted a controlled laboratory study (N = 24) to evaluate whether pedestrians detected the activation of flashing LEDs when distracted by a smartphone more accurately and efficiently when the lights were located on the floor compared to a control position on the wall. Eye gaze movements via an eye tracker and behavioural responses via response times assessed the detection of these flashing LEDs. Distracted participants were able to detect the activation of the floor and wall-mounted LEDs with accuracies above 90%. The visual and auditory distraction tasks increased reaction times by 143 and 124 ms, respectively. Even when distracted, performance improved with floor LEDs close to participants, with reaction time improvements by 43 and 159 ms for the LEDs 2 and 1 ms away from the participant respectively. The addition of floor LED lights resulted in a performance similar to the one observed for wall-mounted LEDs in the non-distracted condition. Moreover, participants did not necessarily need to fixate on the LEDs to detect their activation, thus were likely to have detected them using their peripheral vision. The findings suggest that LEDs embedded in pathways are likely to be effective at attracting the attention of distracted pedestrians. Further research needs to be conducted in the field to confirm these findings, and to evaluate the actual effects on behaviour under real-world conditions.