The current paper implements a methodology for automatically detecting vehicle maneuvers from vehicle telemetry data under naturalistic driving settings. Previous approaches have treated vehicle maneuver detection as a classification problem, although both time series segmentation and classification are required since input telemetry data are continuous. Our objective is to develop an end-to-end pipeline for the frame-by-frame annotation of naturalistic driving studies videos into various driving events including stop and lane-keeping events, lane changes, left-right turning movements, and horizontal curve maneuvers. To address the time series segmentation problem, the study developed an energy-maximization algorithm (EMA) capable of extracting driving events of varying durations and frequencies from continuous signal data. To reduce overfitting and false alarm rates, heuristic algorithms were used to classify events with highly variable patterns such as stops and lane-keeping. To classify segmented driving events, four machine-learning models were implemented, and their accuracy and transferability were assessed over multiple data sources. The duration of events extracted by EMA was comparable to actual events, with accuracies ranging from 59.30% (left lane change) to 85.60% (lane-keeping). Additionally, the overall accuracy of the 1D-convolutional neural network model was 98.99%, followed by the long-short-term-memory model at 97.75%, then the random forest model at 97.71%, and the support vector machine model at 97.65%. These model accuracies were consistent across different data sources. The study concludes that implementing a segmentation-classification pipeline significantly improves both the accuracy of driver maneuver detection and the transferability of shallow and deep ML models across diverse datasets.

Poor air quality in Alaska's remote communities due to road dust is one of the top environmental concerns of residents in these communities. Most communities are disconnected from the road network, with community roads that are predominantly unpaved. In Alaska, high costs limit widespread paving of roads, leaving communities to rely on alternative dust control strategies. The goals for this study were to assess the magnitude and impact of the dust problem in rural Alaska and use a diversity of experience, including regulatory, research, engineering, and cultural, to develop a road-dust management approach for rural Alaska. The plan incorporates different levels of dust management: institutional controls, road watering, chemical dust suppressants, and road surface stabilization. Geographical zones where use of each different dust management level will be most appropriate are identified based on rainfall frequency. Approximately 50% of Alaska's communities can manage road dust with institutional controls and road watering. Many of the road-dust management ideas presented are transferable to other global regions that experience similar economic and community access challenges as Alaska.