Key points are not available for this paper at this time.
ABSTRACT In order to make a good assessment of road traffic status, this paper proposes a comprehensive evaluation method of urban road network capacity based on soft computing under the background of Internet of Things. The focus of the research is to use a variety of soft computing technologies such as Hausdorff dimension simplification method, branch dimension measurement algorithm, multifractal method, etc., to analyze the overall traffic load and guide the traffic channelization in an optimal way, considering various factors comprehensively. The model helps to optimize infrastructure and reduce road traffic congestion. This avoids traffic congestion, ultimately reducing the carbon footprint and promoting a green environment. This method analyzes the network capacity, network load and corresponding conversion coefficient of all levels of highway. Based on the multi-component linear regression method, this paper evaluates the urban road network capacity, and constructs the urban road network capacity planning model according to the traffic volume constraints and travel time constraints. The test results show that under the application of this method, the road capacity of the planned route exceeds 4000 pcu. The pcu is a passenger vehicle unit that measures the extent to which different types of vehicles have an impact on road capacity. When the capacity of a planned road exceeds 4000 pcu, it means that the road can carry a large number of different types of vehicles at the same time, and the average operating speed of the line can reach 42.3 km/h. After 5 years of planning, the efficiency of road use is still above 75 %. It can be proved that this method can obtain the optimal traffic path through better route planning and avoid traffic congestion and pollution.
Wen et al. (Thu,) studied this question.