The impact of operating speed on traffic crash occurrence has been a controversial topic in the traffic safety discipline as some studies reported a positive association whereas others indicated a negative relationship between speed and crashes. Two major issues thought to be accountable for such conflicting findings are the application of inappropriate statistical methods and the use of sample datasets with varying levels of aggregation. The main objective of this study is therefore to investigate the impacts of data aggregation schemes on the relationships between operating speed and traffic safety. A total of three aggregation approaches were examined: (1) a segment-based dataset in which crashes are grouped by roadway segment, (2) a scenario-based dataset where crashes are aggregated by traffic operating scenarios, and (3) a disaggregated crash-level dataset consisting of information from individual crashes. The first two aggregation approaches were used in examining the relationships between operating speed and crash frequency using Bayesian random-effects negative binomial models. The third disaggregated crash risk analysis was conducted utilizing Bayesian random-effects logistic regression models. From the modeling results, it has been concluded that the scenario-based approach shared similar findings with those of the disaggregated crash risk analysis approach in which a U-shaped relationship between operating speed and crash occurrence was identified. However, the commonly adopted segment-based aggregation approach revealed a monotonous negative relationship between speed and crash frequency. The implications of the different analyses results and the potential applications of the results on speed management systems have therefore been discussed.

Crash risk evaluation studies mainly established the relationship between the macro traffic status and crashes. However, the impact of risky driving behavior, a significant factor in crashes, has not been thoroughly investigated due to the data collection limitations of fixed detectors. In this study, the risky driving behavior data generated by Connected Vehicle (CV) techniques was introduced along with traffic flow data to develop the crash risk evaluation model. An LSTM $+$ Transformer approach was developed, in which the Transformer could extract the non-aggregated spatial-temporal features of risky driving behaviors and LSTM learn the temporal patterns of traffic flow. An ensemble layer was proposed to integrate the macro traffic status features and micro driving behavior, and automatically fit their weights to optimize crash risk evaluation performance. Data from a Chinese freeway was used for empirical analysis. The results show that the proposed LSTM $+$ Transformer model achieved high model accuracy (77.7%), recall (68.6%), and AUC (0.785), with average improvement of between 5.34%, 15.69%, and 5.97%, respectively, compared to existing LSTM, XGBoost, SVM and Logistic Regression (LR) models. Moreover, utilizing risky driving behavior data by incorporating the macro traffic status has proved to capture the pre-crash traffic flow turbulence more precisely. The model results explained by SHapley Additive exPlanations (SHAP) reveal that higher frequency, longer duration and greater acceleration of risky braking behavior increase the number of road vehicles affected, thereby heightening the crash risks. These findings could help the deployment of proactive traffic management and target CV control strategies to reduce crashes.

Turnout is one key fundamental infrastructure in the railway signal system, which has great influence on the safety of railway systems. Currently, turnout fault diagnoses are conducted manually in China; engineers are obliged to observe the signals and make problem solving decisions. Thus, the accuracies of fault diagnoses totally depend on the engineers’ experience although massive data are produced in real time by the turnout microcomputer-based monitoring systems. This paper aims to develop an intelligent diagnosis method for railway turnout through Dynamic Time Warping (DTW). We firstly extract the features of normal turnout operation current curve and normalize the collected turnout current curves. Then, five typical fault reference curves are ascertained through the microcomputer-based monitoring system, and DTW is used to identify the turnout current curve fault through test data. The analysis results based on the similarity data indicate that the analyzed five turnout fault types can be diagnosed automatically with 100% accuracy. Finally, the benefits of the proposed method and future research directions were discussed.