Recently, the availability of unmanned aerial vehicle (UAV) opens up new opportunities for smart transportation applications, such as automatic traffic data collection. In such a trend, detecting vehicles and extracting traffic parameters from UAV video in a fast and accurate manner is becoming crucial in many prospective applications. However, from the methodological perspective, several limitations have to be addressed before the actual implementation of UAV. This paper proposes a new and complete analysis framework for traffic flow parameter estimation from UAV video. This framework addresses the well-concerned issues on UAV's irregular ego-motion, low estimation accuracy in dense traffic situation, and high computational complexity by designing and integrating four stages. In the first two stages an ensemble classifier (Haar cascade + convolutional neural network) is developed for vehicle detection, and in the last two stages a robust traffic flow parameter estimation method is developed based on optical flow and traffic flow theory. The proposed ensemble classifier is demonstrated to outperform the state-of-the-art vehicle detectors that designed for UAV-based vehicle detection. Traffic flow parameter estimations in both free flow and congested traffic conditions are evaluated, and the results turn out to be very encouraging. The dataset with 20,000 image samples used in this study is publicly accessible for benchmarking at http://www.uwstarlab.org/research.html.

Crash injury severity prediction is a promising research target in traffic safety. Traditionally, various statistical methods were used for modeling crash injury severities. In recent years, machine learning-based methods are becoming popular due to their good predictive performance. However, machine learning-based models are usually criticized as they perform like a black-box. In this study, we aim at comparing the predictive performance, including prediction accuracy and estimation of variable importance, among various machine learning and statistical methods with distinct modeling logic for crash severity analysis. The crash severity, road geometry and traffic flow data were collected at freeway diverge areas in Florida. We estimated two most commonly used statistical methods which were ordered probit (OP) model and multinomial logit (MNL) model, and four popular machine learning methods including K-Nearest Neighbor (KNN), Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM). The correct prediction rate for each crash severity level and the overall correct prediction rate were calculated. The results showed that machine learning methods had higher predicting accuracy than statistical methods, though they suffered from the over-fitting issue. The RF method had the best prediction in overall and severe crashes while OP was the weakest one. We compared variable importance on crash severity via a perturbation-based sensitivity analyses. The results showed that the inferences of variable importance from different methods were not always consistent and should be paid careful attention.

In recent years, unmanned aerial vehicle (UAV) has become an increasingly popular tool for traffic monitoring and data collection on highways due to its advantage of low cost, high resolution, good flexibility, and wide spatial coverage. Extracting high-resolution vehicle trajectory data from aerial videos taken by a UAV flying over target highway segment becomes a critical research task for traffic flow modeling and analysis. This study aims at proposing a novel methodological framework for automatic and accurate vehicle trajectory extraction from aerial videos. The method starts by developing an ensemble detector to detect vehicles in the target region. Then, the kernelized correlation filter is applied to track vehicles fast and accurately. After that, a mapping algorithm is proposed to transform vehicle positions from the Cartesian coordinates in image to the Frenet coordinates to extract raw vehicle trajectories along the roadway curves. The data denoising is then performed using a wavelet transform to eliminate the biased vehicle trajectory positions. Our method is tested on two aerial videos taken on different urban expressway segments in both peak and non-peak hours on weekdays. The extracted vehicle trajectories are compared with manual calibrated data to testify the framework performance. The experimental results show that the proposed method successfully extracts vehicle trajectories with a high accuracy: the measurement error of Mean Squared Deviation is 2.301 m, the Root-mean-square deviation is 0.175 m, and the Pearson correlation coefficient is 0.999. The video and trajectory data in this study are publicly accessible for serving as benchmark at https://seutraffic.com.