Abstract
The premise of implementing an effective traffic control strategy is the accurate traffic state recognition. In the existing study, traffic state recognition methods were processed by using statistical characteristics and long-term scale detection of field traffic data. Hence, the dynamic characteristics and subtle changes in traffic flow were easy to overlook. At present, more and more advanced traffic detection technology provides reliable and accurate data for measuring and distinguishing the state of urban road traffic, such as the cooperative vehicle-infrastructure system, wide-area radar technology, and 5G technology. This study proposes a novel method called HTSI (High Precision Traffic State Identification Method), which is based on the advanced detection technology in traffic state recognition at the intersection: The raw data used for intersection traffic state recognition is high-precision detection data of tracking characteristics, which make the data look like a picture of the intersection at God’s perspective. To this end, we construct an image model for intersections and implement image feature extraction in a way that is different from traditional image processing. Then, the traffic state recognition problem at the intersection is translated into an image searching problem with tags. The image searching is realized by the hashing algorithm. Finally, the comprehensive experiments prove that the proposed method is more accurate and finer than other methods.
Highlights
Intersections are important nodes of urban road networks, and traffic demand often gathers at intersections. e multidimensional, complex, and time-varying characteristics of traffic demand would reflect on traffic states at intersections. erefore, the premise of implementing effective traffic control and preventing traffic congestion is that the accurate traffic states recognition at the intersection are prepared for recognition
The most widely used traffic signal control systems such as SCOOT [1, 2] and SCATS [3] need to accurately identify traffic conditions to ensure that the control strategy being implemented is effective
New technologies have been widely used in traffic control [11, 12], traffic guidance [13, 14], and autonomous driving [15,16,17], but are not used for traffic state recognition
Summary
Intersections are important nodes of urban road networks, and traffic demand often gathers at intersections. e multidimensional, complex, and time-varying characteristics of traffic demand would reflect on traffic states at intersections. erefore, the premise of implementing effective traffic control and preventing traffic congestion is that the accurate traffic states recognition at the intersection are prepared for recognition. The existing detection technology is mainly based on cross-sectional detection, and the output data are mainly statistical data These data can reflect the movement of some traffic flows, the details are often ignored. Is assumption would be realized by the proposed HTSI method rather than the existing method In this way, the research results in the field of image processing can be introduced into the study of traffic state recognition at intersections. To estimate the queue length in each direction of the intersection with single detector data [24,25,26,27,28,29,30] or multiple source-detector data [31, 32] or vehicle road collaboration data [33], the queue length is used as the basis for traffic state recognition. It is sometimes wrong to treat a traffic accident (As shown in Figure 1(b)) near an intersection as a state of oversaturation (as shown in Figure 1(a)), but this is not the case
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