IATSS Research最新文献

In Japan, the key pieces of legislations governing road safety, namely the Road Transport Vehicle Act and the Road Traffic Act, have been revised to tackle drunk driving and, recently, to allow the development of self-driving cars. Traffic safety on public roads depends on a holistic system of vehicle control and a graduated response to traffic violation. If small violations are dealt with via a simplified system of fines, drunk driving and other form of dangerous driving need a stronger response that also includes criminal incrimination. The other major evolution in traffic-safety law is the gradual introduction of legislation allowing self-driving cars. After reducing the obstacles to the operation of automated vehicles with technology comparable to SAE level 3 on public roads, legislation has recently introduced a system comprising three entities for SAE level 4 cars in order to safely transition to driverless traffic operation. However, the key concepts and definitions—even for terms as simple as the “driver” —still need to be reviewed and improved to better fit this futuristic mode of driving.

Background

As urban landscapes rapidly integrate e-scooters into their transportation ecosystems, understanding pedestrian-e-scooter interactions becomes paramount for safety and planning. This study investigates pedestrian discomfort levels and avoidance strategies when encountering an e-scooter approaching from the front.

Methods

25 participants were exposed to e-scooters approaching at three different speeds and lateral distances. Avoidance paths were plotted, and subjective discomfort levels were recorded and analysed.

Results

Our findings underscored two key behaviours: 1) As the speed of the e-scooter increased, participants initiated avoidance manoeuvres from a further distance ahead, suggesting a heightened perception of risk. 2) Regardless of the e-scooter's speed, the lateral distance maintained during passing remained fairly constant. However, when the e-scooter's initial lateral position was closer to participants, both the initiation distance for avoidance and the reported discomfort level increased noticeably.

Conclusion

The findings underscore the critical influence of lateral distance and e-scooter speed on pedestrian comfort and avoidance behaviour. These insights can guide urban planners and policymakers in designing safer and more efficient shared spaces.

Despite the recent decline in child deaths caused by road traffic crashes in high income countries, low- and middle-income countries (LMIC) have yet to experience a similar trend. Children are among the most vulnerable of road users accounting for 30–40 % of all road traffic deaths in LMICs, 50 % of which are vehicle occupants. Previous research suggests that children ages 0–9 are the second most vulnerable age group in Ghana with 54 % of the children being fatally injured in injury-related crashes. However, little has been done to identify the associated factors influencing injury severity outcomes for child passengers in Ghana. This study investigates the factors that are associated with the various injury severity outcomes for child vehicle occupants less than 9 years old involved in road crashes in Ghana from 2014 to 2020. Results indicate that older child passengers (aged 5–8) were associated with lower injury severities compared to younger passengers. Additionally, crashes in which the driver sustained more severe injuries resulted in a higher likelihood of the child passengers sustaining a fatal injury. Findings from this research emphasize the issue of child passenger safety and support transportation policy and decision making to reduce risks of injury for child passengers.

This study examined the influential roadway and contextual factors affecting drivers' speed selection through a comprehensive investigation employing structural equation modeling. Roadway and contextual variables including road curvature, presence of roundabouts, adverse weather conditions, and access were investigated using infrared speed sensors. The analysis revealed that specific roadway and contextual factors such as field variables, uphill and downhill road inclines, particular road curvature, and rainy weather significantly influence drivers' chosen speeds, while factors such as road access, nighttime conditions, larger road curvature, and signage exhibit a lesser impact. Notably, the study also found a positive correlation between road curvature radius and driver-chosen speed. The study's implications for transportation infrastructure planning and road safety interventions are underscored, with potential applications in road design, signage improvement, and weather-responsive measures to regulate driver speed choices in specific roadway and contextual conditions.

Objective

The purpose of this study is to investigate the effect of visual obstruction when a head-up display (HUD) is presented in front of a driver's field of view.

Background

The use of HUDs is expected to increase due to advances in augmented reality (AR) technology. A conventional HUD is displayed at a relatively low position in relation to the driver's viewpoint and has little effect on their front view. However, while an HUD, such as the AR-HUD, is presented in front of the driver's field of vision and helps them in presenting ADAS information, there is a concern that this display may obscure visual objects on the road from the driver's field of vision.

Method

An experiment using a mockup consisting of a HUD and a driver's seat was conducted with 28 participants. A passenger car, motorcycle, and crossing pedestrian were presented as visual objects on a 65-in monitor behind the image of the HUD. Participants were asked questions under different conditions with changes in the HUD luminance and asked to correctly answer questions regarding changes in the inter-vehicle distance and the existence of visual objects on the road. The number of correct answers provided by the participants was recorded.

Results

The experimental results revealed that the HUD had an effect under the twilight condition wherein the background luminance was low. Furthermore, analyzing the number of correct answers for each visual target revealed that the HUD had an effect under conditions wherein the size of the visual object was small.

Conclusion

It was experimentally derived that the luminance ratio R_luminance = 1.1 was the threshold value that affected the recognition of the visual object in the background when the HUD was presented in front of the driver's field of view.

Implication

The threshold value of the luminance ratio R_luminance obtained in this study can be considered when deciding how to properly display the HUD in front of the driver's field of view.

The growing number of vehicles and the evolving behaviour of road users present new and additional challenges to road safety. Study on the variables that influence the frequency of crash occurrences such as road geometry, junction, speed and land use are needed as they have proven effects on the number and severity of crashes. In this paper, we identify and assess the variables, namely road geometry, vehicle speed, traffic volume, land use and junction type, and develop accident frequency prediction models for a main urban transport corridor in São Paulo, Brazil. Crash data was provided by the traffic management company of the city, other datasets were obtained from a mix of primary and secondary sources including roadside cameras, Geographic Information Systems (GIS) and digital mapping tools. The studied road was segmented and the coefficients associated with variables in the segments were obtained using Poisson regression through a stepwise variable selection procedure. Two models with junctions density per type (access/km, T-junction unsignalised/km, T-junction signalised/km and crossroads/km) and junction density per merged type (signalised/km and unsignalised/km) along with land use per type (commercial and residential) are developed. The junction density and land use are found to be significant and positively correlated with crash frequency. The models were evaluated by statistical means for their accuracy of predicting the crashes, and validated with additional information obtained from field observation.

Lane change has a potential significance in road safety. Gap acceptance phenomena serves as a primary and critical phase in lane change maneuver. This study aims to investigate the gap acceptance behaviour of drivers during lane changes on expressways, with a focus on understanding how various factors influence drivers' decisions to change lanes. An extensive dataset collected through various sensors tailored for expressway driving, known as the ‘Expressway Drive: Instrumented Vehicle (EDIV) Dataset’ is utilized. Driving data from 59 drivers covering a distance of around 4000 km was used in the current study. Total 2578 lane changing events are identified through computing lateral deviations measured through 3D LiDAR sensor. Substantial differences are observed within the groups in primary analysis which suggest that lane-change direction significantly affect gap acceptance. To effectively manage both intra- and inter-cluster variances, this study employs two separate three levels mixed-effects linear models. These models account for the interdependence of gap acceptance characteristics within individual drivers and for different directions of lane changes by incorporating random effects. Furthermore, these models examine relationships between lead/ lag gap acceptance and the various influencing factors as fixed effects. It was found that factors such as speed of the subject vehicle, gap position, relative speeds, and surrounding vehicle types had influence on gap acceptance during lane changes on expressways. The insights gained from this study could inform the development of advanced driver assistance systems (ADAS) as well as development of autonomous vehicles, contributing to improved road safety and traffic flow management in high-speed environments.

This paper proposes a method for identifying the bicycle riding environment using only onboard equipment. Initially, a fundamental subsystem is established for identifying the bicycle riding environment, and its functionality is validated. The findings indicate that the subsystem, utilizing an open-source trained model, can detect riding on roadways but not on sidewalks. Consequently, we emphasize the need for transfer learning, specifically using sidewalk viewpoint images, to enable the identification of bicycle riding environments. Subsequently, we conduct bicycle riding environment identification by employing a transfer learning model with manually labeled training data. The results demonstrate that after transfer learning, sidewalk riding detection, which was previously unachievable, becomes feasible. The identification rate was over 80%. Furthermore, we develop four riding environment identification algorithms, including the transfer learning model, and compare their performance across various road environments and riding conditions. Consequently, it is established that the region of interest (ROI) extension identification algorithm exhibits the highest identification performance (93% on average). As a result, this paper contributes valuable insights into the realization of bicycle riding environment identification, particularly in the context of detecting traffic violations within the riding safety support information system.