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Different individuals may move to different regions over time, but every individual has several fixed travel positions or unique travel patterns. Predicting destinations of each individual facilitates traffic demand management, which has great research value. Based on the data of multi-day GPS and passengers’ travel survey, a hidden Markov model (HMM) is employed in this paper to predict trip destination for weekdays and weekends. Firstly, the habit of destination choice among consecutive days and weeks can be discovered by identifying frequently visited destinations. Then, on the basis of Viterbi algorithm, this paper takes frequently visited destinations as one of the factors of the predicting process and constructs a travel destination prediction model based on HMM. Then, the HMM is calibrated with Baum-Welch algorithm and passengers’ travel destination characteristics are effectively analyzed. Finally, the HMM was compared with several classical algorithms. The results show that the place of residence and work are the most probable activities to occur and workplace dominates the activities when duration is longer than 8 h. Moreover, the results of frequently visited destinations identification indicate that the patterns of destination choice on weekdays and weekends are different from each other. In addition, the results show that the prediction accuracy on weekdays is higher than that on weekends and HMM outperforms other prevailing algorithms. The method proposed in this paper can be applied to real-time travel navigation applications, as well as supporting health and safety fields, such as epidemic prevention and control.

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As an initial step of intelligent video processing, the efficiency of scene change detection algorithm mostly impacts the whole processing progress. Increasing number of researchers has absorbed in improving the algorithm's accuracy or reducing the computational complexity through various approaches. This paper proposes a new approach to scene change detection which explores the high compression efficiency of compressive sampling theory, and only uses 20 compressive samplers each frame to achieve a high scene change detection accuracy. The main contributions of this paper are as follows: we first introduce the compressive sampling theory into video scene change detection technique, then found the best combination of sampler number and the threshold so as to achieve the highest accuracy, and importantly the computational complexity of the proposed algorithm keeps at a low level. The experimental results demonstrate our algorithm's efficiency: 0.93 of precision rate, 1.0 of recall rate and 0.96 of F1 score on average. Meanwhile, comparing with the algorithm with the similar computation complexity, the proposed algorithm is able to improve the detection accuracy by approximate 20%; comparing with the algorithm with the similar detection accuracy, the proposed algorithm can reduce approximate about 40% of computational time.