Automated traffic signal performance measures (ATSPMs) are used to collect data concerning the current and historical performance of signalized intersections. However, transportation agencies are not using ATSPM data to the full extent of this “big data” resource, because the volume of information can overwhelm traditional identification and prioritization techniques. This paper presents a method that summarizes multiple dimensions of intersection- and corridor-level performance using ATSPM data and returns information that can be used for prioritization of intersections and corridors for further analysis. The method was developed and applied to analyze three signalized corridors in Utah, consisting of 20 total intersections. Four performance measures were used to develop threshold values for evaluation: platoon ratio, split failures, arrivals on green, and red-light violations. The performance measures were scaled and classified using $k$-means cluster analysis and expert input. The results of this analysis produced a score for each intersection and corridor determined from the average of the four measures, weighted by expert input. The methodology is presented as a prototype that can be developed with more performance measures and more extensive corridors for future studies.

Automated traffic signal performance measures (ATSPMs) have garnered significant attention for their ability to collect and evaluate real-time and historical data at signalized intersections. ATSPM data are widely utilized by traffic engineers, planners, and researchers in various application scenarios. In working with ATSPM data in Utah, it was discovered that five types of ATSPM data anomalies (data switching, data shifting, data missing under 6 months, data missing over 6 months, and irregular curves) were present in the data. To address the data issues, this paper presents a method that enables transportation agencies to automatically detect data anomalies in their ATSPM datasets. The proposed method utilizes the moving average and standard deviation of a moving window to calculate the z-score for traffic volume data points at each timestamp. Anomalies are flagged when the z-score exceeds 2, which is based on the data falling within two standard deviations of the mean. The results demonstrate that this method effectively identifies anomalies within ATSPM systems, thereby enhancing the usability of data for engineers, planners, and all ATSPM users. By employing this method, transportation agencies can improve the efficiency of their ATSPM systems, leading to more accurate and reliable data for analysis.

Automated traffic signal performance measures (ATSPM) have become widely adopted and utilized by state and local agencies in the U.S. for collecting real-time traffic data 24 h a day, 7 days a week. These agencies have developed new performance measures and applications to address their local transportation planning needs. However, recent research has identified data quality issues in the collected data from ATSPM systems. Specifically, the traffic volumes collected through ATSPM exhibit data anomalies that do not accurately reflect the actual traffic patterns at intersections. As such, there is a need to address the data quality issues found in ATSPM datasets. The purpose of this paper is to evaluate the use of machine learning algorithms and statistical methods to predict traffic volume at intersections. Existing traffic volume data, along with additional metrics such as timestamps, weather conditions, crash data, and holidays, are evaluated to predict traffic volume and address the data anomalies present in ATSPM datasets. Two statistical methods and four machine learning algorithms are evaluated to determine their ability to predict traffic volumes. By comparing the root mean square error (RMSE) and the mean absolute percentage error (MAPE) between each model, the results demonstrate that the long short-term memory (LSTM) model exhibits the lowest error in predicting traffic volume compared with the other models. The LSTM model achieves an RMSE as low as 9.4 vehicles and an MAPE as low as 35%. By leveraging the LSTM model, traffic agencies can enhance the quality of their ATSPM data, enabling better decision-making for traffic operations by their engineers and planners.

E-bikes have gained significant popularity in China from their convenience and eco-friendliness, but the lack of helmet use among riders poses serious head injury risks. This study evaluated the effectiveness of safety campaigns and educational initiatives in promoting helmet adoption among e-bike riders in Guangdong Province, China, across three perceived effectiveness levels: ineffective, moderately effective, and effective. Data from a questionnaire survey were analyzed using an optimized eXtreme Gradient Boosting (XGBoost) model, with hyperparameters tuned via Optuna and performance validated through 10-fold cross-validation, achieving 84.6% accuracy. Class imbalance was addressed using the synthetic minority over-sampling technique, yielding macro-averaged precision, recall, and F1-scores of approximately 0.82, indicating balanced predictive performance. Model interpretability with SHapley Additive exPlanations (SHAP) identified safety education frequency, enforcement of helmet use, riding experience, ideal education locations, attitudes toward helmet use, and age group as the most influential predictors of helmet adoption effectiveness. Safety education frequency contributed most at the ineffective (25.36%) and effective (18.65%) levels, while enforcement was most influential at the moderately effective level (19.11%), with persuasion-plus-fines outperforming education-only strategies. Partial dependence plots (PDPs) and individual conditional expectation (ICE) plots showed that monthly or quarterly education substantially improves perceived effectiveness, whereas infrequent or no education increases perceived ineffectiveness. Younger riders (under 25 years) and those with negative helmet attitudes were more likely to perceive initiatives as ineffective. Findings highlight actionable recommendations: institutionalizing frequent safety education in high-impact locations, integrating enforcement with education, and tailoring interventions to demographic and attitudinal profiles, offering policymakers evidence-based approaches to enhance helmet adoption and rider safety.