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Traffic state estimation plays a significant role in intelligent transportation systems. For a specific road, traffic state varies at different times of a day. Therefore, to estimate real-time traffic state is difficult. This paper presents a probabilistic approach for traffic state estimation. In this approach, traffic state distribution and data point distribution are used to describe the pattern...
Autonomous car is an important tool for transportation and military in the future, and its precise positioning is the basis of autonomous navigation. Most positioning algorithms based on map-matching for autonomous car make little use of environmental perception information. To solve this problem, a positioning algorithm is proposed in this paper, which is based on map-matching and environmental perception...
This paper, based on the Kalman filter theory, established short-term traffic volume model. Kalman filter model has a good static stability, and adopts iterative method for optimal estimation of traffic. But the regular Kalman filtering traffic volume prediction established without considering the influencing factors on traffic and time-lag. Analyzing the influence factors of traffic volume based...
Displacement is a significant operator to solve spatial conflicts of road elements. At present, detection and adjustment of spatial conflicts are still in the manual stage, requiring a lot of manpower, resources and time. Therefore, it is urgently needed to study displacement operation thoroughly, whether for auto map production or the actual needs of mapping applications. Fortunately, structure mechanics...
This paper puts forward a hybrid spatio-temporal method of short-term traffic forecasting, i.e., dynamic space-time autoregressive integrated moving average model (dynamic STARIMA). This method combines STARIMA model and dynamic turn ratio prediction model (DTRP) to enhance the forecasting performance and efficiency on urban intersections. To verify the dynamic-STARIMA modeling method in real situation,...
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