This paper presents a study carried out to estimate the fire rate in England road tunnels. The result is a dataset of 28 road tunnels and 59 fire incidents. All tunnels studied in this project are continuously monitored by the National Highways, Transport for London, and Fire and Rescue Services. The work presents a hitherto unavailable dataset demonstrating the fire safety rate of these tunnels. The average fire rate was estimated by a safety analysis and the findings indicate that the average fire rate in England’s road tunnels is 1.481 per billion veh-km. There is an obvious difference between the number of fire incidents in England road tunnels and the number of fire incidents on England roads. It is also found that the fire rate in England’s road tunnels is less than in other European countries considered in this study.

The identification of critical sectors at the provincial level is important for achieving China’ s CO₂ mitigation target. To scientifically subdivide the target of emission peak and carbon neutrality in public transport, this article employs a decision tree, taking into a combination of “top-down” and “bottom-up” approaches, to determine selection rules for carbon emission calculation under different data sources. The stepwise regression analysis determines length of vehicle is the key factor affecting bus 100 km energy consumption. The results reveal that, with 383.0 million tons of carbon dioxide being emit, the highest carbon emission from Inner Mongolia ground buses system happened in 2013. The results show that measures including replacing conventional vehicles with electric vehicles could effectively facilitate the road transport sector to gradually approach zero carbon emissions.

A huge and quite important role in safe exit from the street with limited visibility plays traffic mirrors. Traffic mirrors, also known as safety mirrors or blind spot mirrors, have a significant impact on road safety. These mirrors are designed with a curved surface that allows for a wider field of view, making it easier for drivers to see objects and vehicles that may be in their blind spots. In order to commit a safe maneuver driver should be confident that the car moving down the street is at a sufficient and safe distance from the exit. Of course, the driver needs to have a perception of the time of the moving car to the place of exit from the street. All of these things play a significant role in safety exit and avoiding collisions. Traffic mirrors ensure an overview of the situation on the road. Moreover, the driver can measure the approximate distance by counting time for approaching the car. The experiment presented in the work is based on the time of the car’s approach. The purpose of this article is to detect the optimal time as well as distance before the street with limited visibility in order to exit the street safely and to avoid collisions. In this paper, we provide systematic approach to the research of a data and statistics of this data.

Road traffic safety is a global concern. Conventionally, road safety evaluation is carried out by analyzing historical crash data, which is a reactive approach to safety analysis. The safety analyst has to wait for sufficient time to accumulate crash data before taking up any safety analysis as a sample of substantial crashes is required for analysis. But in safety analysis with a proactive approach no crash data is required, which is replaced with surrogate “conflicts”, which can be obtained from the new techniques of traffic conflict technique and surrogate safety parameters. Other approaches can be applied in traffic safety evaluation in anticipation of the crash occurrence. The advent of traffic conflict techniques, i.e., use of traffic conflicts in place of crashes and microsimulation methods like modeling of the traffic flow and pattern in traffic streams on a road network, started to apply as a method of analyzing microscopic simulation models and traffic conflict techniques to determine the safety issues in traffic systems and correlate them to the probable incidences of collisions. In this regard, surrogate safety parameters have been used to determine the level of safety on the typical curve sections of an interurban highway namely, Faridabad-Gurgaon four-lane divided highway in Haryana, India. This is accomplished by the use of vehicle trajectory data extracted through microscopic simulation in Verkher in Staedten Simulation (VISSIM) and analysis in Surrogate Safety Assessment Model (SSAM) for the above-referred corridor. Further, efforts have been made to present the intensity of traffic conflicts happening at the curved sections. The surrogate safety measures time to collision (TTC), deceleration rate (DR), and change in velocity (ΔV), as well as conflicting vehicle speed (Max S), are obtained by analysis from the SSAM model for all the curve sections and are validated using the reported crash data on the curve sections of the candidate corridor for 3 years. With the help of statistical elaboration, the critical threshold for TTC with the heterogeneous traffic movement is found 1.6 s, meaning any conflict occurring less than this time would invariably lead to a fatal crash. Similarly, the critical deceleration rate is observed as 0.569 m/s², implying that any conflict with more than this value may lead to a fatal crash. Further, the DeltaV values deduced for the study corridor on interurban curve sections catering to heterogeneous traffic movement is 4.1 m/s. Again, any conflict more than this value can turn into a potential crash.

One of the main concerns with SAE Level 3 automated vehicles is trust and the factors that influence it. Previous research has identified several factors, including prior experience with driving and similar technology. However, due to the lack of a verified system for measuring influencing factors, the acceptance of automated vehicles is still yet to be ascertained, despite several findings. This article reviews empirical literature examining the effect of prior experience on trust in automated driving. The result suggests that while manual driving skills may no longer be required in AV driving, they will remain essential in certain critical circumstances. The results further indicate that acceptance and trust in technology are affected by different factors depending on age. Older generations may be influenced by their previous experience with traditional driving skills, whereas younger generations may be influenced by their strong familiarity with similar technologies.