UAV-Assisted Traffic Speed Prediction via Gray Relational Analysis and Deep Learning
Author:
Zheng Yanliu1ORCID, Luo Juan12ORCID, Qiao Ying1, Gao Han1
Affiliation:
1. College of Computer Science and Electronic Engineering, Hunan University, No. 2 Lushan South Road, Changsha 410082, China 2. Research Institute of Hunan University, Chongqing 401120, China
Abstract
Accurate traffic prediction is crucial to alleviating traffic congestion in cities. Existing physical sensor-based traffic data acquisition methods have high transmission costs, serious traffic information redundancy, and large calculation volumes for spatiotemporal data processing, thus making it difficult to ensure accuracy and real-time traffic prediction. With the increasing resolution of UAV imagery, the use of unmanned aerial vehicles (UAV) imagery to obtain traffic information has become a hot spot. Still, analyzing and predicting traffic status after extracting traffic information is neglected. We develop a framework for traffic speed extraction and prediction based on UAV imagery processing, which consists of two parts: a traffic information extraction module based on UAV imagery recognition and a traffic speed prediction module based on deep learning. First, we use deep learning methods to automate the extraction of road information, implement vehicle recognition using convolutional neural networks and calculate the average speed of road sections based on panchromatic and multispectral image matching to construct a traffic prediction dataset. Then, we propose an attention-enhanced traffic speed prediction module that considers the spatiotemporal characteristics of traffic data and increases the weights of key roads by extracting important fine-grained spatiotemporal features twice to improve the prediction accuracy of the target roads. Finally, we validate the effectiveness of the proposed method on real data. Compared with the baseline algorithm, our algorithm achieves the best prediction performance regarding accuracy and stability.
Funder
National Natural Science Foundation of China Natural Science Foundation of Chongqing Key scientific and technological research and development plan of Hunan Province
Subject
Artificial Intelligence,Computer Science Applications,Aerospace Engineering,Information Systems,Control and Systems Engineering
Reference41 articles.
1. A Survey on Operation Concept, Advancements, and Challenging Issues of Urban Air Traffic Management;Shrestha;Front. Future Transp.,2021 2. Davies, L., Vagapov, Y., Grout, V., Cunningham, S., and Anuchin, A. (2021, January 27–29). Review of Air Traffic Management Systems for UAV Integration into Urban Airspace. Proceedings of the 2021 28th International Workshop on Electric Drives: Improving Reliability of Electric Drives (IWED), Moscow, Russia. 3. Kopardekar, P., Rios, J., Prevot, T., Johnson, M., Jung, J., and Robinson, J.E. (2016, January 13–17). Unmanned aircraft system traffic management (UTM) concept of operations. Proceedings of the AIAA Aviation and Aeronautics Forum (Aviation 2016), Washington, DC, USA. 4. A Comparison of Detrending Models and Multi-Regime Models for Traffic Flow Prediction;Li;IEEE Intell. Transp. Syst. Mag.,2014 5. Macioszek, E., and Kurek, A. (2021). Extracting Road Traffic Volume in the City before and during COVID-19 through Video Remote Sensing. Remote Sens., 13.
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|