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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. [ABSTRACT FROM AUTHOR]

Travel time is one of the most critical indexes to describe urban traffic operating states. How to obtain accurate and robust travel time estimates, so as to facilitate to make traffic control decision-making for administrators and trip-planning for travelers, is an urgent issue of wide concern. This paper proposes a reliable estimation method of urban link travel time using multi-sensor data fusion. Utilizing the characteristic analysis of each individual traffic sensor data, we first extract link travel time from license plate recognition data, geomagnetic detector data and floating car data, respectively, and find that their distribution patterns are similar and follow logarithmic normal distribution. Then, a support degree algorithm based on similarity function and a credibility algorithm based on membership function are developed, aiming to overcome the conflicts among multi-sensor traffic data and the uncertainties of single-sensor traffic data. The reliable fusion weights for each type of traffic sensor data are further determined by integrating the corresponding support degree with credibility. A case study was conducted using real-world data from a link of Jingshi Road in Jinan, China and demonstrated that the proposed method can effectively improve the accuracy and reliability of link travel time estimations in urban road systems. [ABSTRACT FROM AUTHOR]

Traffic lights have been used for decades to control and manage traffic flows crossing road intersections to increase traffic efficiency and road safety. However, relying on fixed time cycles may not be ideal in dealing with the increasing congestion level in cities. Therefore, we propose a new Adaptive Traffic Light Control System (ATLCS) to assist traffic management authorities in efficiently dealing with traffic congestion in cities. The main idea of our ATLCS consists in synchronizing a number of traffic lights controlling consecutive junctions by creating a delay between the times at which each of them switches to green in a given direction. Such a delay is dynamically updated based on the number of vehicles waiting at each junction, thereby allowing vehicles leaving the city centre to travel a long distance without stopping (i.e., minimizing the number of occurrences of the 'stop and go' phenomenon), which in turn reduces their travel time as well. The performance evaluation of our ATLCS has shown that the average travel time of vehicles traveling in the synchronized direction has been significantly reduced (by up to 39%) compared to non-synchronized fixed time Traffic Light Control Systems. Moreover, the overall achieved improvement across the simulated road network was 17%. [ABSTRACT FROM AUTHOR]