An Improved Traffic Congestion Monitoring System Based on Federated Learning

This study introduces a software-based traffic congestion monitoring system. The transportation system controls the traffic between cities all over the world. Traffic congestion happens not only in cities, but also on highways and other places. The current transportation system is not satisfactory in the area without monitoring. In order to improve the limitations of the current traffic system in obtaining road data and expand its visual range, the system uses remote sensing data as the data source for judging congestion. Since some remote sensing data needs to be kept confidential, this is a problem to be solved to effectively protect the safety of remote sensing data during the deep learning training process. Compared with the general deep learning training method, this study provides a federated learning method to identify vehicle targets in remote sensing images to solve the problem of data privacy in the training process of remote sensing data. The experiment takes the remote sensing image data sets of Los Angeles Road and Washington Road as samples for training, and the training results can achieve an accuracy of about 85%, and the estimated processing time of each image can be as low as 0.047 s. In the final experimental results, the system can automatically identify the vehicle targets in the remote sensing images to achieve the purpose of detecting congestion.

[1]  Gang Wang,et al.  Learning fine-grained features via a CNN Tree for Large-scale Classification , 2015, Neurocomputing.

[2]  H. Zackor,et al.  Prediction of congestion due to road works on freeways , 2001, ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585).

[3]  David Taniar,et al.  Spatial Network RNN Queries in GIS , 2011, Comput. J..

[4]  Julien Brajard,et al.  Classification of Sea Ice Types in Sentinel-1 SAR Data Using Convolutional Neural Networks , 2020, Remote. Sens..

[5]  Gérard Dedieu,et al.  Assessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas , 2016 .

[6]  Bhaskaran Raman,et al.  RoadSoundSense: Acoustic sensing based road congestion monitoring in developing regions , 2011, 2011 8th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks.

[7]  Somprakash Bandyopadhyay,et al.  Road traffic congestion monitoring and measurement using active RFID and GSM technology , 2011, 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[8]  Eduardo Morgado,et al.  Vehicular Sensor Networks in congested traffic: Linking STV field reconstruction and communications channel , 2011, 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[9]  Kai Zhang,et al.  Evaluation of traffic congestion degree: An integrated approach , 2017, Int. J. Distributed Sens. Networks.

[10]  Robert Sablatnig,et al.  Detection of Parking Cars in Stereo Satellite Images , 2020, Remote. Sens..

[11]  S. Travis Waller,et al.  An Internet-based geographic information system that integrates data, models and users for transportation applications , 2000 .

[12]  Amir Hossein Alavi,et al.  Machine learning in geosciences and remote sensing , 2016 .

[13]  Enrique Onieva,et al.  A graph CNN-LSTM neural network for short and long-term traffic forecasting based on trajectory data , 2020 .

[14]  Yao Tang,et al.  Improving on MODIS MCD64A1 Burned Area Estimates in Grassland Systems: A Case Study in Kansas Flint Hills Tall Grass Prairie , 2020, Remote. Sens..

[15]  Zhen Qian,et al.  Road Traffic Congestion Monitoring in Social Media with Hinge-Loss Markov Random Fields , 2014, 2014 IEEE International Conference on Data Mining.

[16]  Abdul Hanan Abdullah,et al.  Road Aware Geographical Routing Protocol Coupled with Distance, Direction and Traffic Density Metrics for Urban Vehicular Ad Hoc Networks , 2017, Wirel. Pers. Commun..