Automatic perception method of urban road conditions based on mobile adaptive clustering

Abstract

The invention discloses an automatic sensing judging method for the urban road condition based on mobile adaptive clustering. The problem that as the to-be-collected data quantity and the to-be-processed data quantity are large in the prior art, the urban road traffic condition is difficult to be judged in real time is mainly solved. The method includes the steps that 1, an urban road network diagram is initialized through cartographic information; 2, periphery vehicles are communicated with the short-range wireless communication technology, local mobile adaptive clustering is carried out, and the average movement speed of the vehicles in clustering is calculated; 3, the average movement speed of the vehicles in road sections is calculated through merging between sub-clustering; 4, the traffic condition of the road sections is subjected to automatic sensing judgment according to the average movement speed of the vehicles in the road sections and a road congestion threshold value. By means of the automatic sensing judging method, the road condition can be subjected to automatic sensing judgment in the remote-server-free and internet-free supporting environment, and the automatic sensing judging method has the advantages of being simple, easy to conduct and high in real-time performance, and can be used for solving the congestion problem of urban roads.

Description

technical field [0001] The invention belongs to the technical field of mobile sensor networks, in particular to an automatic sensing method for urban road conditions, which can be used for mobile terminals. [0002] It mainly uses short-distance wireless communication technology (Bluetooth) to form a mobile ad hoc network among vehicles driving on urban roads. Through the information dissemination between vehicles, it can automatically perceive and judge the road congestion situation according to the vehicle's position, moving direction and average moving speed. The results are then propagated across the road network using the mobility of the vehicles. Background technique [0003] With the continuous progress of society and the rapid development of the automobile industry, as the main means of transportation in daily life, the number of automobiles on the road is increasing rapidly, which makes us face more and more serious urban road traffic problems. Due to the advancem...

AI Technical Summary

Problems solved by technology

However, these traditional methods will lead to problems such as large data transmission volume, high computational time complexity, and poor real-time performance of the results. In addition, the requirements for network and hardware conditions are relatively high, and it is difficult to meet the needs of the real environment.

Images