Part Of Intelligent Traffic System Research Articles

Intelligent license plate recognition system based on digital image processing

With the rapid development of China’s economy, intelligent transportation system has gradually become an inevitable trend in the development of modern traffic management. AS an important part of intelligent traffic system, license plate recognition system plays an important role in the management and control of modern traffic. This paper uses digital image processing technology to study the license plate positioning technology of color automobile images. Through image preprocessing, license plate positioning, character segmentation and character recognition, the image is processed to realize the recognition of license plate under different environments.

BP-Neural Network for Plate Number Recognition

The License Plate Recognition (LPR) as one crucial part of intelligent traffic systems has been broadly investigated since the boosting of computer vision techniques. The motivation of this paper is to probe in plate number recognition which is an important part of traffic surveillance events. In this paper, locating the number plate is based on edge detection and recognizing the plate numbers is worked on Back-Propagation (BP) Artificial Neural Network (ANN). Furthermore, the authors introduce the system implementation and take advantage of the well-known Matlab platform to delve how to accurately recognize plate numbers. There are 80 samples adopted to test and verify the proposed plate number recognition method. The experimental results demonstrate that the accuracy of the authors' character recognition is above 70%.

Video Based Intelligent Transportation Systems – State of the Art and Future Development

Video based sensor systems can play a key role in delivering data for better road planning and traffic management. Smart road technologies will largely depend on data quality and quantity in the future. Video based detection systems, being an indispensable part of intelligent traffic systems (ITS), show huge potentials as they do not only offer a flexible way of data acquisition but are also being developed at a huge pace due to recent evolutions in hardware and software technology. In order to give a better understanding on the methods and potentials of this technology, a structured review is presented which not only includes current applications but also shows future use cases by analyzing the techniques of image processing and extrapolating their results to the future requirements of traffic engineering.

Research on Vehicle Networking Clustering Routing Algorithm Based on Subtractive Clustering

Vehicle networking is an important part of intelligent traffic system. It combines wireless sensor network and mobile autonomous network, which is better for drivers to obtain road condition information to ensure driving safety. This paper focuses on the analysis of subtractive clustering and proposed clustering routing algorithm based on subtractive clustering in order to reduce communication of wireless sensor network and redundant flooding and routing expanse. In the algorithm, cluster head selection adopts subtractive clustering to produce cluster node in node intensive place. Cluster forms by adopting current non cluster head node mechanism which reduced energy consumption. Then the maintenance method of cluster is proposed. At last, it introduces routing protocol of cluster which is easier for clustering structure to manage and synchronize network.

Real-time information feedback based on a sharp decay weighted function

Information feedback strategy, serving as the critical part of intelligent traffic systems, has been treated with growing emphasis. In recent years, a variety of feedback strategies have been proposed. Despite the fact that these strategies have been proved to enhance the traffic efficiency, we find that the road capacity has not been saturated and there is still plenty of room for improvement. Based on the analytic approximations, we found the reason why corresponding angle feedback strategy is superior to weighted congestion coefficient feedback strategy. Given that the sharp decay of the weighted coefficient is the key point, we proposed an efficient feedback strategy called the exponential function feedback strategy (EFFS). We applied it to both the symmetrical two-route model with two exits and that with a single exit. The simulation results indicate that, compared with other strategies, EFFS has decided numerical advantages in average flow, a physical quantity used for depicting the road capacity. Even more importantly, EFFS stands out for its convenient application as well as its fitness for modeling the rugged roads.