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.

Conflicting driving behaviours between automated vehicles and manually driven vehicles may compromise driving safety. The aim of this study is to analyse the safety of mixed traffic on urban roads. The driving simulation tests were conducted using a multi‐agent driving simulator, which allows real‐time synchronization of multiple simulators. These data were further processed to derive the driving behaviour parameters of manually driven vehicles in VISSIM traffic simulations. Driving safety evaluation indicators included conflict‐related indicators, as well as individual safety indicators. The safety evaluation indicators were normalized through min–max normalization, and the risk scores were summed to evaluate the urban roads. The analysis revealed that driving safety was poor at unsignalized intersections with a market penetration rate of 10% and 50% and at signalized intersections with traffic islands and a market penetration rate of 100%, where conflicts arise from the deceleration of leading vehicles and lane changes. This finding is about the driving behaviour of automated vehicles, which maintain a greater distance from the leading vehicle than manually driven vehicles, resulting in poorer driving safety due to lane changes rather than deceleration. Using the findings of this study, criteria for assessing the safety of mixed traffic situations in existing road infrastructures can be established.

A connected vehicle (CV) enables vehicles to communicate not only with other vehicles but also the road infrastructure based on wireless communication technologies. A road system with CVs, which is often referred to as a cooperative intelligent transportation system (C-ITS), provides drivers with road and traffic condition information using an in-vehicle warning system. Road environments with CVs induce drivers to reduce their speed while increasing the spacing or changing lanes to avoid potential risks downstream. Such avoidance maneuvers can be considered to improve driving behavior from a traffic safety point of view. This study seeks to quantitatively evaluate the effect of in-vehicle warning information using per-vehicle data (PVD) collected from freeway C-ITSs. The PVD are reproduced to extract the speed–spacing relationship and are evaluated to determine whether the warning information induces drivers to drive in a conservative way. This study reveals that the in-vehicle warning prompts drivers to increase the spacing while decreasing their speed in the majority of samples. The rate of conservative driving behavior tends to increase during the initial operation period, but no significant changes were observed after this period; that is, the reliability of in-vehicle warning information is not constant in the CV environment.

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.

Blockage of road drainage inlets by debris is a major cause of urban flooding. Manual inspection of urban drainage facilities is time-consuming and costly, highlighting the need for automated inspection technologies. In this study, a closed-circuit television (CCTV)- and AI-based technology was developed to automatically monitor the drainage inlet blockage status by varying camera height and observation angle to examine their influence on detection performance. The YOLOv11l and RT-DETR-L models classified the drainage inlet conditions in the testbed into three categories: fully open (clean), partially blocked (trash), and fully blocked (clogged). Both models achieved F1 scores of 0.994. Under clogged conditions, RT-DETR-L achieved a higher confidence score, whereas YOLOv11l achieved a slightly higher intersection over union (IoU). A three-way analysis of variance (ANOVA) revealed that the inlet condition was the most influential factor affecting both the confidence score and IoU, with a significant height–angle interaction under the clogged condition: performance improved with height at a front-facing angle but declined at oblique angles. These findings indicate that observation height and angle should be considered jointly when evaluating CCTV configurations for drainage inlet monitoring. Further validation using independent real-world CCTV data is required before extending these findings to broader field applications.