Bus arrival time prediction not only provides convenience for passengers, but also helps to improve the efficiency of intelligent transportation system. Unfortunately, the low precision of bus-mounted GPS system, lack of real-time traffic information and poor performance of prediction model lead to low estimation accuracy - greatly influence bus service performance. Hence, in this paper, a GPS calibration method is put forward, while projection rules of specific road shapes are discussed. Moreover, two traffic factors, travel factor and dwelling factor, are defined to express real-time traffic state. Then, considering both historic data and real-time traffic condition, a hybrid dynamic BAT prediction factor, which achieves accuracy enhancement by taking into account traffic flow evaluation results and GPS position calibration, is defined. A LSTM training model is construct to realize BAT prediction. Experiment results demonstrate that our technique can provide a higher level of accuracy compared to methods based on traditional time-of-arrival techniques, especially in the accuracy of multi-stops BAT prediction.

Fatigued driving detection, which employs pattern recognition to discover the state of a driver's fatigue, is considered a key technique to improve road safety. However, the widely used frontal face recognition systems have problems, such as low recognition accuracy, poor real-time ability, and highly complex algorithms. In this paper, a new color space modeling, which uses multi-threshold decision criteria, is used to enhance facial skin extraction performance, and a three-step strategy is designed to eliminate noise and other adverse effects. Experiments show that the proposed algorithm can extract side face contour lines effectively and provide a scientific basis for real-time tracking of fatigue.