IATSS Research最新文献

Determining an appropriate segment length for highway safety evaluations in low- and middle-income countries (LMICs) poses a significant challenge. This study aims to address this issue by recommending a suitable segment length for such evaluations in India, using a 167 km intercity expressway as a case study. We employed negative binomial (NB) models on datasets segmented from 100 m to 1000 m with 100 m increments. Our findings strongly suggest that segment lengths from 300 m to 700-m suit various safety assessments. However, the study reveals that parameter estimates vary significantly with both segment length and sample size. This highlights the sensitivity of parameters to data aggregation and sample size across different segment lengths, making it difficult to identify a single optimal length. Therefore, we propose selecting the segment length and segmentation approach based on specific local conditions, highway context, data availability and quality. The methodology presented here can guide policymakers in LMICs to make informed choices regarding segment length for safety evaluations, including blackspot identification and treatment on their highways.

The main goal of this study is to investigate the impact of COVID-19 on road crashes in Thailand using time series and interrupted time series analysis. To achieve the goal, road crash data from the Department of Highway (DOH), which includes total crashes, single vehicle crashes, fatalities, fatal crashes, speeding crashes, and drunk driving crashes, was obtained to conduct Seasonal Autoregressive Integrated Moving Average (SARIMA) time series models and Interrupted Time Series (ITS) models. SARIMA models were applied to forecast the number of crashes in the absence of COVID-19 then compare them to the observed values to identify the difference. The impact of a policy change aimed at addressing the spread of COVID-19 was assessed using ITS models on a time series accident dataset. The goal was to ascertain if the intervention had a meaningful and causative impact on the outcome. The result showed that the first wave of COVID-19 caused a significant reduction in all road crash indicators instead of skyrocketing to a peak. After releasing the lockdown measures from the first wave of spreading, an increase was found in all of the crash indicators as well. However, the third wave of COVID-19, which lasted longest for nearly 7 months, also caused a decrease in the number of crashes, but not as much as the first wave of the outbreak. Moreover, the result from the interrupted time series also revealed that curfews and the closure of entertainment places are associated with a significant decrease in the number of speeding crashes and drunk driving crashes from 10 p.m. to 4 a.m., respectively. It can be observed that the COVID-19 countermeasures, such as curfews and bans on the sales of alcoholic beverages, led to a drop in the number of speeding and drunk driving crashes.

Herein, we propose a “Comprehensive Road Safety Management for School Routes” to realize “Vision Zero for School Routes”, launched by International Association of Traffic and Safety Sciences (IATSS). Specifically, we discuss Niigata City's “Vision Zero” project rolled out at Hiyoriyama Elementary School, which aims to eliminate accidents resulting in deaths of or serious injuries to children. Hiyoriyama Elementary School was to be opened by merging four elementary schools in Chuo-ku, Niigata City, and traffic safety measures were discussed with the participation of local residents to ensure the safety of the new route to the school, which would bring in many children from a wide range of localities. In a workshop attended by road administrators, police, school officials, and local residents, issues were identified, a traffic survey was conducted to understand the current situation, and a traffic simulation was conducted to verify concerns that might arise subsequent to the implementation of traffic control measures. The traffic in front of the elementary school was restricted during school hours; furthermore, to ensure compliance with these restrictions, rising bollards were installed for the first time along a school route in Japan. We also clarified the long-term effects of the rising bollards installed on a public road in Japan for the first time in an IATSS project. We hope that this report contributes to the expansion of Vision Zero, which secures the safety of schoolchildren.

It is difficult to accurately predict when and where natural disasters such as major earthquakes will occur, and it is difficult to establish and permanently maintain a large-scale disaster response system for the entire country in order to realize a sustainable, safe, and secure society. Therefore, the key point is how to quickly and efficiently collect on-site information and establish evacuation, rescue, and support systems in the affected areas when a disaster occurs.

The authors launched a research project in April 2012, following the IATSS earthquake disaster investigation project in 2011, immediately after the Great East Japan Earthquake, to investigate the use of probe information obtained from vehicle-mounted sensors to assess the on-site situation in the initial response to such disasters. In the project, a prototype of a system that uses a probe function mounted on a motorcycle to assess road surface damage and detect the presence or absence of people in need of rescue while driving was built and its performance was checked, and the effectiveness of the proposed system was found. After the completion of the research project, the author has spent about 10 years trying to improve the accuracy of the system's detection of minute road surface wear and tear, which can be used for road management, as well as to verify the social acceptability of the system through long-term experiments in social implementation.

In this paper, the author summarizes the research trends to date, reviews the progress and results, and reports the results of discussions on the role of vehicles equipped with the probe function for the implementation and diffusion of sustainable disaster mitigation and disaster prevention systems in the future.

Adaptive Cruise Control (ACC) is a technology that can reduce traffic. However, its availability in Iran is relatively limited compared to more developed countries. This research examines the acceptance and willingness to pay for ACC among Iranian drivers. Data from an online survey of 453 respondents were analyzed using an ordered logit model and a structural equation model. The results of modelings show that perceived ease of use and perceived usefulness affect attitudes towards using ACC, which in turn influence behavioural intentions. In addition, drivers who find ACC easy to use and user-friendly, own expensive vehicles, and female drivers who have experience with cruise control are more likely to pay for ACC. To enhance the adoption of ACC in Iran, it is recommended to target early adopters, especially women and capitalists, who can influence others with their positive feedback. The benefits of ACC for traffic safety and environmental sustainability should also be emphasized. Furthermore, future studies would better concentrate on public perceptions and assessing the necessary infrastructure for ACC in Iran.

Until early 2000s, modern roundabouts (RABs) were poorly understood in Japan and were not fully recognized as a form of at-grade intersections. The RAB research projects of the International Association of Traffic and Safety Sciences (IATSS), which began in 2009, started with examining the applicability of RABs in Japan and have steadily progressed to various stages of field experiments, social implementation, and dissemination promotion activities. These RAB research projects, which lasted for approximately seven years, are considered to have played a significant role in the practical application of RAB in Japan. This paper provides a chronological overview of the history, content, and development status of these projects and records and discusses the role that IATSS research projects played in the early days of RABs in Japan.

Modelling traffic characteristics is the foundation for resolving various traffic and transportation issues. Among them, traffic speed has a significant impact on roadway crashes at blackspot (BS) locations. Speed is a random variable; several studies have recommended normal distribution to characterize the distribution of traffic speed for uninterrupted flow. However, a mixed-traffic situation causes heterogeneity, and the distribution of speeds deviates from the normal distribution. The present study investigates the distributions of traffic speeds for uninterrupted flow at 18 blackspot locations and individual vehicle types in mixed-traffic environments. Seven distribution models, namely Normal, Lognormal, Gamma, Logistic, Weibull, Burr, and Generalized Extreme Value (GEV), are considered to determine the speed characteristics. Different parametric distribution models are fitted to the vehicular speeds using maximum likelihood estimation (MLE) methods. Kolmogorov-Smirnov (KS), Anderson-Darling (AD), and two penalized criteria, i.e., Akaike and Bayesian Information Criteria (AIC and BIC), are used as goodness-of-fit (GoF) measures to find the best-fitting distribution. The overall suitability of each predicted distribution is also determined using a novel ranking method. The test findings suggest that GEV and Burr are the most suitable empirical speed distributions, with GEV fitting best above 96%. When the heavy vehicle composition (truck, bus, and tractor) is below 10%, 10–14%, 15–20%, and above 20%, it follows the Weibull, Gamma, GEV, and Burr distributions, respectively, in a mixed traffic environment.

Following behavior, an integral part of driving, is vital in describing the longitudinal interaction among vehicles. The traffic composition of the stream influences the following behavior. Several studies have concentrated on developing the following behavioral models; however, very few have addressed the sophistication needed to cater to the pragmatic needs of the present day, inducing the real, naturalistic sense of traffic movement. This study endeavors to review the previous following behavior studies in a different aspect and to find the research gaps accordingly. The study disintegrates the following behavioral models based on three levels of heterogeneous traffic conditions: (1) Homogenous Regular Vehicle (Hom-RV); (2) Heterogenous Regular Vehicle (Het-RV); (3) Heterogenous Connected Automated Vehicles (Het-CAV) (4) Heterogenous Regular Vehicle with Connected Autonomous Vehicles (Het-RV-CAV). The categories mentioned above have been explored in terms of the generalized following behavioral model structure having uniform notations to study input-output variables and their inter-relations, data collected and performance measures of the parameters for different traffic conditions. The in-depth review reveals that incorporating human psychological variables, and intelligent vision-based sensors, thereby upgrading the existing dataset and adding more studies considering Het-RV-CAV, can fill the potential gaps in the current knowledge domain.

Sleep-related breathing disorders (SRBD), characterized by episodes of apnea and hypopnea during sleep, are highly prevalent worldwide. Although adherence to continuous positive airway pressure (CPAP) therapy improves quality of life and reduces symptoms in patients with obstructive sleep apnea (OSA), and OSA treatment is well established, the vast majority of individuals with OSA who might benefit from treatment remain undiagnosed. This is attributed to the high-cost of undergoing polysomnography, which is the gold standard test for OSA, and requires special equipment and experts, limiting the number of facilities that can perform this test. SRBD is a risk factor for traffic accidents and lifestyle diseases because it causes daytime/waking sleepiness. Traffic accidents caused by commercial drivers have a serious impact on social safety. Furthermore, obesity and overweight status, which is a main risk factor for SRBD, is more prevalent among commercial drivers than among the general population. Thus, the promotion and awareness of SRBD screening among commercial drivers are urgently required. In this overview, we provide an assessment of the present status and challenges related to SRBD screening in Japan. Our insights are drawn from the outcomes of our project titled “Interdisciplinary research on the dissemination and promotion of screening for sleep disorders”, which received funding from the International Association of Traffic and Safety Sciences from 2012 to 2014. The project focused on exploring the association between SRBD and traffic accidents among commercial drivers in Japan. The findings of our project suggest the importance of early detection and treatment of SRBD through screening using objective measurements for all commercial drivers.

Distracted driving poses one of the most significant risks to road safety. The current study aims to provide a deeper understanding of the factors affecting the severity of distracted driving crashes in New York City and to explore the temporal stability in the effects of different variables on crash outcomes in 2016 to 2019 period by adopting a post-crash perspective. The police-reported data of single-vehicle distraction-related crashes of private cars on urban roads of New York City was used for this study. Three injury categories were considered: no injury, minor injury, and severe injury. To investigate crash severities and identify unobserved heterogeneities, a random parameters logit model was conducted. The results revealed that a wide variety of variables including driver traits, vehicle and temporal characteristics, and crash attributes were found to be significant in explaining distracted-related crash severities. Furthermore, a series of likelihood ratio tests were conducted to identify the temporal shifts of estimated variables during the period. The results of the temporal analysis showed that the estimated variables of the random parameters model were unstable during the 4-year period, which may be the result of shifting trends such as the development of in-vehicle technologies, and new sources of distraction. However, the complex nature of distracted-related crashes and changes in driver behavior should be considered for further interpretation. This research provides a set of policy implications for planners and policymakers, aiming at facing factors contributing to a higher level of injury severity in distracted driving crashes. This includes providing targeted information on distracted driving to high-risk groups, such as younger drivers, and also combining education, awareness programs, higher penalties, and intense patrolling. Engineering measures such as enhanced roadside illumination and audible edge lines can be effective, especially in reducing late-night distracted driving crashes.