Adverse weather related to reduced visibility caused by fog and rain can seriously affect the mobility and safety of drivers. It is meaningful to develop effective intelligent transportation system (ITS) strategies to mitigate the negative effects of these different types of adverse weather related to reduced visibility by investigating the effect of rain and fog on traffic parameters. A number of previous researches focused on analyzing the effect of adverse weather related to reduced visibility by using simulated traffic and weather data. There are few researchers that addressed the impact of adverse weather instances using real-time data. Moreover, this paper conducts comprehensive investigation to clearly compare the changes of driving behavior and traffic parameters in adverse weather including fog and rain using real-time traffic and weather data collected by advanced vehicle-based traffic sensors and weather sensors. After some preliminary analysis, the analysis of variance method (ANOVA) was applied to further compare the significance of effects of these two kinds of adverse weather on traffic parameters. The conditional regression models were employed finally to explore the relationship between these two types of adverse weather and traffic parameters. The results would be beneficial to develop effective intelligent traffic control countermeasures under these different types of adverse weather conditions related to reduced visibility.

There are few research studies that addressed the impact of reduced visibility due to fog using real-time data. It is thus meaningful to conduct further investigation that can clearly describe the changes in driving behavior and traffic parameters under foggy conditions using real-time traffic and weather data. Field traffic and weather data were collected in this research and fog cases were selected and analyzed by comparing them with clear cases to identify the differences in traffic characteristics under the two different situations. Moreover, vehicles were classified into two types (i.e., passenger cars and trucks) to identify whether the impact of reduced visibility due to fog on traffic varies depending on vehicle types. Afterward, the traffic parameters under different visibility classes and the effects of reduced visibility on different lanes were analyzed using ANOVA. Finally, a matched case–control logistic regression model was applied to further confirm the relationship between traffic parameters and reduced visibility due to fog. It was concluded that the impact of fog on traffic varies by vehicle types and lanes. The impact was also different by visibility classes. The impact of reduced visibility on passenger cars is more significant compared with that on trucks. The effect of reduced visibility on traffic parameters is more significant on inner lanes than outer lanes. Under these weather conditions, drivers should pay more attention to the traffic because higher headway variance is more likely to result in the crash occurrence. The matched case–control logistic regression modeling results indicate that larger average headway, speed variance, headway variance, and occupancy were related to the increase of the likelihood of a reduced visibility. The results would be helpful to understand the change of traffic status and investigate the potential factors for higher crash frequency under foggy conditions.

Purpose: The causes of high-speed railway failures are complex, and it is difficult to quantitatively and accurately describe safety evaluation. The purpose of this paper is to construct a model to ensure the safety of high-speed railway operations. Design/methodology/approach: The authors construct a high-speed railway operation safety evaluation index system from four aspects: personnel, equipment, environment and management and analyze the inter-coupling relationship of various safety factors. Based on the evaluation index system, the use of network analytic hierarchy process (ANP) and fuzzy comprehensive evaluation will be used to establish a high-speed railway operation safety evaluation model. Findings: Through the literature investigation and field investigation, combined with high-speed railway safety key points and system composition, 4 first-level indicators and 17 second-level indicators were selected to construct a high-speed railway operation safety evaluation index system. It can be seen from the results that the personnel management system and the signal and control system have the largest weight. Originality/value: On the basis of establishing an evaluation index system, comprehensively considering the internal coupling relationship between evaluation indexes and the fuzziness of high-speed railway operation safety evaluation, high-speed railway uses ANP fuzzy network analysis method to construct high-speed railway operation, and the safety evaluation model has certain advantages and practicability in the case of the relative lack of high-speed railway operation data and fault data.

It is of great significance to identify dangerous driving behavior by extracting vehicle trajectory through video monitoring to ensure highway traffic safety. At present, there is no suitable method to identify dangerous driving vehicles accurately based on trajectory data. This paper aims to develop a detection algorithm for identifying dangerous driving behavior based on the road scene, which is mainly composed of imbalanced dangerous driver detection and labeling, extraction of driving behavior characteristics and the establishment of a recognition model about dangerous driving behavior. Firstly, this paper defines the risk index of the vehicle related to five types of dangerous driving behavior: dangerous following, lateral deviation, frequent acceleration and deceleration, frequent lane change, and forced insertion. Then, a variety of methods, including K-means clustering, local factor anomaly algorithm, isolation forest and OneClassSVM, are used to carry out anomaly detection on the risk indicators of drivers, and the optimal method is proposed to identify dangerous drivers. Then, the speed and acceleration of each vehicle are Fourier transformed to obtain the characteristics of the driver’s driving behavior. Finally, considering the imbalanced characteristic of the analyzed dataset with a very small proportion of dangerous drivers, this paper compares a variety of imbalanced classification algorithms to optimize the recognition performance of dangerous driving behavior. The results show that the OneClassSVM detection algorithm can be effectively applied to the identification of dangerous driving behavior. The improved Xgboost algorithm performs best for the extremely imbalanced data of dangerous drivers.