The main objective of this study is to validate a performance forecasting model for road surface temperatures ranging from 3 to 24 hours in the future using real-time weather forecasts as provided by the Korea Meteorological Administration (KMA). The model used here has been developed by researchers at the Korea Institute of Construction and Technology. The core concept of the model is based on heat transfer between road surfaces and the atmosphere. Preliminary tests were performed through comparing historically observed road surface temperatures obtained from road sensors with predicted temperatures from model runs at a specific site on the CheongWon-SangJoo Expressway. After testing, the model was partially run on the CheongWon-SangJoo Expressway at the Bo-Eun Branch of the Korea Highway Corporation to predict road surface temperatures 24 hours in advance. Test results showed that predicted road surface temperatures matched well with the observed temperatures. In addition, the average (either hourly or daily) difference between the two temperatures lies within ±2°C, indicating that the model performance is reasonable. For a real-time model run, road surface temperatures were predicted only at night as there were no instruments to measure solar radiation in real time along the roads. Nonetheless, a 12 hour forecast performed well, showing the average temperature differences between observed and predicted temperatures of about ±0.5°C.

Road traffic crashes pose significant economic and public health burdens, necessitating an in-depth understanding of crash causation and its links to underlying factors. This study introduces a machine learning-based hurdle model framework tailored for analyzing zero-inflated crash frequency data, addressing the limitations of traditional statistical models like the Poisson and negative binomial models, which struggle with zero-inflation and overdispersion. The research employs a two-stage modeling process using CatBoost. The first stage uses binary classification to identify road segments with potential crash occurrences, applying a customized loss function to tackle data imbalance. The second stage predicts crash frequency, also utilizing a customized loss function for count data. SHapley Additive exPlanations (SHAP) analysis interprets the model outcomes, providing insights into factors affecting crash likelihood and frequency. This study validates the model’s performance with real-world crash data from 2011 to 2015 in South Korea, demonstrating superior accuracy in both the classification and regression stages compared to other machine learning algorithms and traditional models. These findings have significant implications for traffic safety research and policymaking, offering stakeholders a more accurate and interpretable tool for crash data analysis to develop targeted safety interventions.

Evaluating driving safety according to road alignment conditions is a fundamental for safe road design and traffic operations to prevent traffic crashes. A significant intrinsic factor affecting driving safety is the driver’s psychophysiological workload generated in response to various external environmental changes during road driving. Electroencephalogram (EEG) is a well-known measure to evaluate the driver workload resulting from the response to the internal and external stimuli experienced while driving vehicles. However, the use of EEG in research of the transportation engineering domain requires not only high-cost experiments but also great efforts in data analysis. To address these issues, this study developed a promising method to predict the psychophysiological workload affecting driving safety using vehicle maneuvering data. A total of 30 drivers participated in driving simulation experiments using a high-fidelity motion-based driving simulator to obtain both psychophysiological workload data and vehicle maneuvering data to be used for developing the methodology. The proposed methodology consists of two parts: the prediction of the level of psychophysiological workload using a K-nearest neighbor (KNN) algorithm and the derivation of the alertness risk index (ARI) based on the spatiotemporal aggregation of the prediction results. Additionally, evaluating driving safety by different road conditions is conducted based on the proposed methodology.

One of the first steps in improving the road network is to implement a traffic safety program and identify problematic road segments where local risk factors increase the likelihood of vehicular accidents. Numerous studies have attempted to identify both the geometric and traffic features of road segments that may lead to increased risk. The statistical models linking the number of accidents at road sites over a unit of time and the characteristics of these sites contribute to highlighting and quantifying the impact of the development of road infrastructure on the risk of accidents. The evaluation and selection of the best-fit model are usually carried out using an information criterion. The main purpose of this research is to propose a comparative assessment framework to support the evaluation task during the process of modeling crash frequency. Performance evaluation was conducted using additional approaches, specifically data splitting and rootograms. In this study, we focus on Korean rural road accident records. Six generalized linear models were applied, and their performance was compared to determine the best-fitting statistical model. According to each model assessment method used, the negative binomial hurdle model showed the best performance among all the investigated models.

Road surface temperature is more important than ambient temperature in term of road safety during winter season. The conventional method to collect road surface temperature is to install Road Weather Station(RWS). However RWS can only collect spot information and could be costly. To enhance these problem, this study used the commercial vehicle ambient temperature(VAT) sensors can collect the pattern of air temperature along the road effectively and rapidly. Also the thermal mapping equipment was used to collect the road surface temperature(RST). After that, our research analysed the similarity of the patterns of road surface and ambient temperatures using correlation analysis. The result showed that the patterns of temperature variation collected from VAT sensors are statistically comparable to patterns of road surface temperature variation collected from a thermal mapping. In addition, this research developed a way to segment roadway based on variation in road surface temperature. This segmentation is useful to road authorities for managing the road and providing information based on road segmentation. Although VAT sensors do not provide highly accurate temperature readings, they can identify patterns of surface road temperature on roadway sections. This study found out that the VAT can be estimated the patterns of RST and could be replace the road weather station or thermal mapping system. This information can be used so that individual drivers, commercial vehicle and fleet operators and road authorities acquire critical real-time information on road conditions. Further, using these sensors instead of fixed sensors or thermal mapping can significantly reduce road maintenance costs. Also the result could be applied to the automated driving system to provide the road surface condition in the future.

The advent of autonomous vehicles (AVs) in the traffic stream is expected to innovatively prevent crashes resulting from human errors in manually driven vehicles (MVs). However, substantial safety benefits due to AVs are not achievable quickly because the mixed-traffic conditions in which AVs and MVs coexist in the current road infrastructure will continue for a considerably long period of time. The purpose of this study is to develop a methodology to evaluate the driving safety of mixed car-following situations between AVs and MVs on freeways based on a multi-agent driving-simulation (MADS) technique. Evaluation results were used to answer the question ‘What road condition would make the mixed car-following situations hazardous?’ Three safety indicators, including the acceleration noise, the standard deviation of the lane position, and the headway, were used to characterize the maneuvering behavior of the mixed car-following pairs in terms of driving safety. It was found that the inter-vehicle safety of mixed pairs was poor when they drove on a road section with a horizontal curve length of 1000 m and downhill slope of 1% or 3%. A set of road sections were identified, using the proposed evaluation method, as hazardous conditions for mixed car-following pairs consisting of AVs and MVs. The outcome of this study will be useful for supporting the establishment of safer road environments and developing novel V2X-based trafficsafetyinformation content that enables the enhancement of mixed-traffic safety.