Biblio
The incidence of abnormal road traffic events, especially abnormal traffic congestion, is becoming more and more prominent in daily traffic management in China. It has become the main research work of urban traffic management to detect and identify traffic congestion incidents in time. Efficient and accurate detection of traffic congestion incidents can provide a good strategy for traffic management. At present, the detection and recognition of traffic congestion events mainly rely on the integration of road traffic flow data and the passing data collected by electronic police or devices of checkpoint, and then estimating and forecasting road conditions through the method of big data analysis; Such methods often have some disadvantages such as low time-effect, low precision and small prediction range. Therefore, with the help of the current large and medium cities in the public security, traffic police have built video surveillance equipment, through computer vision technology to analyze the traffic flow from video monitoring, in this paper, the motion state and the changing trend of vehicle flow are obtained by using the technology of vehicle detection from video and multi-target tracking based on deep learning, so as to realize the perception and recognition of traffic congestion. The method achieves the recognition accuracy of less than 60 seconds in real-time, more than 80% in detection rate of congestion event and more than 82.5% in accuracy of detection. At the same time, it breaks through the restriction of traditional big data prediction, such as traffic flow data, truck pass data and GPS floating car data, and enlarges the scene and scope of detection.
Recently Vehicular Cloud Computing (VCC) has become an attractive solution that support vehicle's computing and storing service requests. This computing paradigm insures a reduced energy consumption and low traffic congestion. Additionally, VCC has emerged as a promising technology that provides a virtual platform for processing data using vehicles as infrastructures or centralized data servers. However, vehicles are deployed in open environments where they are vulnerable to various types of attacks. Furthermore, traditional cryptographic algorithms failed in insuring security once their keys compromised. In order to insure a secure vehicular platform, we introduce in this paper a new decoy technology DT and user behavior profiling (UBP) as an alternative solution to overcome data security, privacy and trust in vehicular cloud servers using a fog computing architecture. In the case of a malicious behavior, our mechanism shows a high efficiency by delivering decoy files in such a way making the intruder unable to differentiate between the original and decoy file.
As the traffic congestion increases on the transport network, Payable on the road to slower speeds, longer falter times, as a consequence bigger vehicular queuing, it's necessary to introduce smart way to reduce traffic. We are already edging closer to ``smart city-smart travel''. Today, a large number of smart phone applications and connected sat-naves will help get you to your destination in the quickest and easiest manner possible due to real-time data and communication from a host of sources. In present situation, traffic lights are used in each phase. The other way is to use electronic sensors and magnetic coils that detect the congestion frequency and monitor traffic, but found to be more expensive. Hence we propose a traffic control system using image processing techniques like edge detection. The vehicles will be detected using images instead of sensors. The cameras are installed alongside of the road and it will capture image sequence for every 40 seconds. The digital image processing techniques will be applied to analyse and process the image and according to that the traffic signal lights will be controlled.
Internet of things has become a subject of interest across a different industry domain. It includes 6LoWPAN (Low-Power Wireless Personal Area Network) which is used for a variety of application including home automation, sensor networks, manufacturing and industry application etc. However, gathering such a huge amount of data from such a different domain causes a problem of traffic congestion, high reliability, high energy efficiency etc. In order to address such problems, content based routing (CBR) technique is proposed, where routing paths are decided according to the type of content. By routing the correlated data to hop nodes for processing, a higher data aggregation ratio can be obtained, which in turns reducing the traffic congestion and minimizes the energy consumption. CBR is implemented on top of existing RPL (Routing Protocol for Low Power and Lossy network) and implemented in contiki operating system using cooja simulator. The analysis are carried out on the basis average power consumption, packet delivery ratio etc.