Short-term traffic speed prediction has become one of the most important parts of intelligent transportation systems (ITSs). In recent years, deep learning methods have demonstrated their superiority both in accuracy and efficiency. However, most of them only consider the temporal information, overlooking the spatial or some environmental factors, especially the different correlations between the target road and the surrounding roads. This paper proposes a traffic speed prediction approach based on temporal clustering and hierarchical attention (TCHA) to address the above issues. We apply temporal clustering to the target road to distinguish the traffic environment. Traffic data in each cluster have a similar distribution, which can help improve the prediction accuracy. A hierarchical attention-based mechanism is then used to extract the features at each time step. The encoder measures the importance of spatial features, and the decoder measures the temporal ones. The proposed method is evaluated over the data of a certain area in Hangzhou, and experiments have shown that this method can outperform the state of the art for traffic speed prediction.
Target tracking has become one of the research hotspots in the field of computer vision in recent years. In this paper, a new intelligent algorithm of infrared multi-pedestrian tracking in vertical view is proposed. In the algorithm, the pedestrians in the infrared image can be quickly detected and located with the method of the Faster Regions with CNN features (RCNN) and then are tracked with the improved Siamese network. The tracking method based on Siamese network transforms the tracking problem into a similarity verification problem and evaluates the similarity score between new frame feature and target frame feature by convolution network. The candidate region with the highest score is considered as the current position of the target. In this paper, the Siamese network is combined with Faster RCNN for multi-pedestrian tracking. In addition, the tracking results of adjacent frames are introduced into the similarity evaluation of current frames to improve the tracking accuracy when the pedestrian posture changes. The experimental results show that the algorithm has good robustness and tracking result and achieves competitive performance. INDEX TERMS Computer vision, Siamese network, infrared detection, pedestrian tracking, convolution network.
Aiming at the problem of intersection signal control, a method of traffic phase combination and signal timing optimization based on the improved K-medoids algorithm is proposed. Firstly, the improvement of the traditional K-medoids algorithm embodies in two aspects, namely, the selection of the initial medoids and the parameter k, which will be applied to the cluster analysis of historical saturation data. The algorithm determines the initial medoids based on a set of probabilities calculated from the distance and determines the number of clusters k based on an exponential function, weight adjustment, and elbow ideas. Secondly, a phase combination model is established based on the saturation and green split data, and the signal timing is optimized through a bilevel programming model. Finally, the algorithm is evaluated over a certain intersection in Hangzhou, and results show that this algorithm can reduce the average vehicle delay and queue length and improve the traffic capacity of the intersection in the peak hour.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.