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Road hotspots detection method is a key issue in the field of intelligent transportation research. Compared with normal hotspots caused by high traffic flow, abnormal hotspots, which are results of road accidents, perform an occurrence time random behavior and difficult to predict. In this paper, a region real-time congestion factor is constructed to realize road abnormal hotspots discovery. Based on taxi's GPS data of Hangzhou City, China, we analyze the relationship between proposed congestion factor and the real-time traffic data. Two accidental scenarios are built to verify the validity of the proposed method. The experiment results show that the proposed method performs well in real-time abnormal hotspot detection and analysis output could be useful in path planning and traffic management.

On-road large vehicles are always considered as potential danger, which could generate serious damage and strongly threaten road safety. Hence, large vehicle related accident forecasting becomes an important research topic in intelligence transportation area. In this paper, a large vehicle first clustering method (LVFC) is proposed to estimate the risk level of vehicle group. Composite performance index (CSPI), which is used to illustrate the risk level of hotspots area, has become a popular practice in the field of road safety. CSPI value is employed to illustrate environment condition of object road section. Combining CSPI with vehicle group's risk level, a comprehensive risk parameter is presented to represent real-time transportation condition, which illustrates the risk level of a road section with large vehicle group passing by. A vehicle following model is designed to simulate the generation of large vehicle first clusters and it's changing procedure, which provides justification for the proposed algorithm.

V2V transmission of Safety Warning Message (SWM) can provide warning before emergency occurs to avoid accident, and remind surrounding vehicles to avoid potential danger. There are two main problems that will affect the transmission performance of SWM, namely the high local flow and low efficiency caused by high probability of sub-health vehicles (SHV) and traditional single channel and flooding transmission mechanism. Therefore a novel channel allocation algorithm based on communication demand matrix has been proposed in this paper. A new parameter, communication demand relationship based on health evaluation results is defined and communication demand matrix (CDM) is constructed to measure the transmission demand between corresponding vehicles. Then intra-cluster channel allocation algorithm based on the CDM is studied to effectively allocate the channel resource and improve SWM dissemination performance. The simulation and experimental results show that the proposed mechanism has much higher ratio of fulfilled communication demand in the same time slots than flooding based on communication demand relationship (FD) methods in low, medium, and high vehicle density scenario. Beyond that, the proposed mechanism has better performance in receiving messages than FD and flooding.