In this study, we focused on the possibility of improving driving performance as well as reducing mental fatigue and perceived time of drivers by providing appropriate secondary tasks to drivers in traffic congestion. The purpose is to clarify the effect of performing secondary tasks other than driving operations. Specifically, we conducted an experiment in which four types of secondary tasks were imposed over 15-minute periods in an indoor experimental environment that imitated driving operations in traffic congestion, and attempted to measure the effects by reaction delay time and multifaceted psychological indicators. As a result, while the secondary tasks do not make a clear difference in attention level, we found that the passive secondary tasks may increase the perceived time, and that the active secondary tasks may decrease it. In addition, the analysis of gender difference revealed that the active secondary task of "conversation" may have the effect of reducing stress and fatigue in males while increasing stress and fatigue in females.
"Road Type of Shared Carriageways", in which bicycles and cars are mixed on the roadway, is a form that requires special attention to the safety and smoothness of both traffic. Therefore, in this study, we focused on the behavior of a car overtaking bicycles on one-lane mixed-carriageway roads, and examined the width of lanes / shoulders and the traffic volume when the bicycle does not affect the overtaking car. We created virtual spaces with oncoming vehicles (excluding large vehicles) in the opposite lane and conducted experiments by driving simulation.
The results show that on lane width of 2.75 m roads with no shoulder, passing cars can be affected by bicycles at any of the traffic volumes set in the experiment. On the other hand, the results show that on roads with a shoulder width of 0.5 m in traffic volumes of up to 750 vehicles/hour, passing cars cannot be affected by bicycles. Furthermore, this study revealed that on roads with a lane width of 3.00 m and a shoulder width of 0.75 m, there is no effect of bicycles on passing cars at any of the traffic volumes set in the experiment.
In local cities, the bicycle usage rate for high school students is high, and there are many traffic accidents per student when commuting to school. We provided traffic safety sovereign education at a high school in local city where there are many bicycle accidents when commuting to school. The purpose of this study is to examine the traffic safety awareness of high school students through educational practice.
In this study, we conducted a questionnaire survey of high school students in the second grade and found that they were exposed to the risk of traffic accidents when commuting to school by bicycle, and that high school students commute to school while protecting themselves. An analysis of changes in traffic safety awareness due to traffic safety education revealed that the effect of raising awareness was small. And we grasped the relationship between bicycle traffic safety awareness and behavior / awareness related to daily life.
The introduction of Autonomous vehicles is currently under consideration as an innovative mobility option. However, in addition to ensuring a safe driving space, the introduction of automated driving systems will also pose a challenge in terms of how to limit the number of vehicles parked on the road. In this study, a micro traffic simulator was used to understand the influence of vehicle performance, loading zone and its surrounding environment factors in a society where automated driving is prevalent, using average travel speed as an evaluation item. In addition, the combination of these factors was used to identify the street environment in which the stopping of vehicles should be limited. As a result, it became clear that increasing the frequency of stopping and the rate of right and left turns is a factor that reduces travel speed, and that if the rate of right and left turns on the connecting road is high, the installation of a loading zone should not be allowed depending on the frequency of stopping and vehicle performance.
This paper points out that the ratio of longitudinal gradient change at upstream sag is a significant factor for breakdown in flow. In this study, traffic detector data collected on Tohoku Expressway in 2016 and longitudinal gradient data are used to identify positions of bottlenecks and to prepare data set containing with or without the breakdown, traffic flow-rate and ratio of longitudinal gradient change at a sag of bottleneck and their neighboring sags. First, as a result of calibration of logistic regression models using the data set, the ratio of the longitudinal gradient change at the upstream sag of the bottleneck as well as that at the bottleneck are found as significant explanative variables. On the other hand, the ratio of longitudinal gradient change of the downstream sag is not significant. Second, probabilistic capacity curves considering the ratio of the longitudinal gradient change of the bottleneck sag and the right upstream sag are estimated by applying a parametric survival model. This paper concludes that it is essential to revisit mechanisms of flow breakdown at bottleneck in access-controlled section and countermeasures to mitigate congestion.
For the social implementing autonomous vehicles, technology development, demonstration experiments, and legislation are being promoted in the world. However, there are many challenges for introduction. The purpose of our research is to analyze and grasp the impact of low-speed autonomous vehicles on traffic flow and following vehicles in urban area. In particular, we analyzed the data collected by the observations in the demonstration experiment in Toyota City. As a result, it was clarified that the headway-time increases and the passing traffic volume decreases at a signalized intersection, when a low-speed autonomous vehicle is mixed in. Also, the inter vehicular distance between the autonomous driving vehicle and the following vehicles tended to be significantly smaller in non-intersection. Further, it was clarified that the following vehicles of low-speed autonomous driving have been closed for a long time, and the following vehicles have overtaking in spite of the yellow line.
In recent years, the provision of traffic accident risk information has been proposed as a measure to reduce the number of traffic accidents by guiding vehicles to safer routes. Previous studies have shown that the provision of accident encounter risk information has an influence on route choice, but the results of them have shown that the provision of accident occurrence risk information does not have an influence on route choice. In this study, a web-based questionnaire survey was conducted to drivers living or working in the Niigata metropolitan area to clarify their route choice behavior when both snow-covered road surface information and accident risk information were provided, assuming winter traffic situation with relatively high accident risk. The results showed that the snow-covered road surface conditions influenced route choice behavior only when accident encounter risk information was provided, but not when accident occurrence risk information was provided.
There are high expectations for autonomous vehicles as analternative and complementary means of public transportation in rural areas. The purpose of this study is to investigate the impact of the introduction of autonomous vehicles on regional traffic safety. The possibility of collision between pedestrians or bicyclists and vehicles at an intersection with poor visibility and no stop restrictions on the vehicle side was modeled, and this model was aplyed to evaluate the safety of the area around the non-signalized intersections in Iijima-Naganonaka-Cho, Akita City, Akita Prefecture. The results showed that compared to conventional vehicles (normal response), automated vehicles had 98.5% fewer pedestrian crashes, 25.7% fewer bicycle crashes, 93.3% fewer pedestrian crashes without a low speed range for crash avoidance, and 66.7% fewer bicycle crashes without a low speed range for crash avoidance. It was found that the introduction of autonomous vehicles is expected to improve safety in the region.
Damage to road network links caused by a large-scale disaster is considered to be a problem because it can cause serious life-threatening problems, such as isolation of the affected area and delay in relief efforts. Therefore, in order to create a robust network that ensures connectivity between locations even in the event of a disaster, it is essential to develop roads with disaster resistance that takes into account "selection and concentration". In this study, we develop a model for optimizing the maintenance order by combining deep reinforcement learning with the "disaster prevention function evaluation of roads" used by the Ministry of Land, Infrastructure, Transport and Tourism to evaluate road maintenance. As a result of applying the model to a simple network, it was confirmed that the model can search for an effective maintenance sequence for the total number of maintenance sequence patterns with less search than the total search. In the future, it is hoped that the introduction of a maintenance period and application to large-scale networks will enable us to make proposals that aim to achieve a disaster-resistant effect as soon as possible within a limited budget.