Aggressive driving, amongst inappropriate driving behaviors, is largely responsible for leading to traffic accidents, which threatens both safety and property of human beings. With the objective to reduce traffic accidents and improve road safety, effective and reliable aggressive driving recognition methods, which enables the development of driving behavior analysis and early warning systems, are urgently needed. Most recently, the research focus of aggressive recognition has shifted to the use of vehicle motion data, which has emerged as a new tool for traffic phenomenon explanation. As aggressive driving corresponds to sudden variations in data, they can be recognized based on the recorded vehicle motion data. In this paper, several kinds of anomaly recognition algorithms are studied and compared, using the motion data collected by the accelerometer and gyroscope of a smartphone mounted on vehicle. Gaussian mixture model (GMM), Partial Least Squares Regression (PLSR), Wavelet Transformation (WT), Support Vector Regression (SVR) are considered as the representative algorithms of statistical regression, time series analysis, and machine learning, respectively. These algorithms are evaluated by the three widely used validation metrics, including F1-score, Precision, and Recall. Empirical results show that GMM, PLSR and SVR are promising methods for aggressive driving recognition. GMM and SVR outperform PLSR when only single-source dataset is used. The improvement of F1-score is almost 0.1. PLSR performs the best when multi-source datasets are used, and the F1-score is 0.77. GMM and SVR are more robust to hyperparameter. In addition, incorporating multi-source datasets helps improve the accuracy of aggressive driving behavior recognition.

Urban travel time estimation is a strategically important task for many levels of traffic management and operation. Although a number of technologies regarding data and model have been developed recently, it has not been well solved yet given the following challenges: effective modeling approach, data sparsity, and traffic condition fluctuation. In this paper, a tensor-based spatial-temporal model is proposed for citywide travel time estimation, using the big and sparse GPS trajectories received from taxicabs. The travel time of different road segments under different traffic conditions in some time slots is modeled with a third-order tensor. Meanwhile, the occurrence probability of different traffic conditions on different road segments in these time slots are modeled with another third-order tensor. Combined with historical knowledge learned from trajectories, missing entries in the two tensors can be estimated by a context-aware tensor factorization approach. Based on the reconstruction results, for any road segment of the urban road network in the current time slot, we can know not only the travel times under different traffic conditions but also the occurrence probabilities of corresponding traffic conditions. The model incorporates both the spatial correlation between different road segments and the deviation between different traffic conditions, as well as the coarse-grain temporal correlation between recent and historical traffic conditions and the fine-grain temporal correlation between different time slots. The model is applied to a case study for the citywide road network of Beijing, China. Empirical results of extensive experiments, based on the GPS trajectories derived from over 32670 taxicabs for a period of two months, demonstrate that the model outperforms the competing methods in terms of both effectiveness and robustness.

Aggressive driving is common across the world. While most aggressive driving is conscious, some aggressive driving behavior may be unconscious on part of motor vehicle drivers. Perceptual bias of aggressive driving behavior is one of the main causes of traffic accidents. This paper focuses on identifying impact factors related to aggressive driving perceptual bias. Questionnaire data from 690 drivers, collected from a drivers’ retraining course administered by the Traffic Management Bureau in Nanjing, China, were used to collect drivers’ socioeconomic characteristics, personality traits, and external environment data. Actual penalty points were considered as an objective indicator and Gaussian mixture model (GMM) was used to cluster an objective indicator into different levels. The driving anger expression (DAX) was used to measure drivers’ self-assessment of aggressive driving behavior and then to identify perceptual biases. Then a binary logistic model was estimated to explore the influence of different factors on drivers’ perceptual bias of aggressive driving behavior. Results showed that bus drivers were less likely to have perceptual bias of aggressive driving behavior. Truck drivers, drivers with an extraversion characteristic, and drivers who have dissatisfaction with road infrastructure and actual work were likely to have a perceptual bias. The findings are potentially beneficial for proposing targeted countermeasures to identify dangerous drivers and improve drivers’ safety awareness.