A traffic guidance-oriented road traffic data intelligent perception and distribution network architecture

Abstract

The invention discloses a road traffic data intelligent perception and distribution network architecture oriented to traffic dredging. The system predicts and analyzes the road conditions according to the current real-time local data of each road section, the global data of the whole city and historical records, so that users I can intuitively understand the surrounding road conditions, and complete traffic guidance more predictably, more efficiently and accurately according to the needs of users themselves. And the invention proposes a storage unit RSSU, by adding a camera and a low-cost storage function, it can mark bad users who maliciously drive to a blocked route or do not drive a diversion route, and combine these bad user information with the collected local data, video Upload the data preliminary processing module together, and store the historical records fed back by the data preliminary processing module daily as an important parameter for the entire architecture to estimate the road condition status.

Description

technical field [0001] The invention relates to the technical field of the Internet of Vehicles, in particular to a network architecture for intelligent perception and distribution of road traffic data oriented to traffic dredging. Background technique [0002] At present, the Internet of Vehicles has penetrated into various industries and business functions, and has become an inseparable part of the information age. Regardless of how the rise of the Internet has changed the automotive landscape, the fundamental nature of a car is a vehicle whose most tangible goal is to efficiently and safely transport users to their destinations. However, due to the constraints of the actual weather environment, traffic congestion and frequent traffic accidents, we cannot realize this most basic function. Existing traffic control methods mainly have the following drawbacks: 1. A relatively complete and systematic framework is not formed, but road guide poles, intelligent traffic lights, s...

AI Technical Summary

Problems solved by technology

However, due to the constraints of the actual weather environment, traffic congestion and frequent traffic accidents, we cannot realize this most basic function.

Existing traffic control methods mainly have the following disadvantages: 1. A relatively complete and systematic framework is not formed, and road guide poles, intelligent traffic lights, sensors are only installed, or GPS, GPRS, etc. are introduced

These devices and technologies are expensive, and even some have little effect. It is unrealistic to install a large number of them to achieve traffic diversion

2. The traditional traffic diversion method is to divert traffic only when congestion occurs. If the congestion state of the road section can be estimated, the loss will obviously be reduced a lot

3. Most of the traditional traffic diversion methods are to divert vehicle users to other slightly distant roads, but the congestion situation may not be very serious, and the waiting time at the congested place may be shorter than the time of detouring the long road, which causes loss of user time

4. Most of the collected data needs to be transmitted to the remote control center for data analysis and processing, and then fed back to the user. This mechanism can be said to be very time-consuming and inefficient

Moreover, all massive data must be stored in the control center. Disadvantages such as network congestion and high latency are inevitable. Once the central network collapses, the consequences will be disastrous

Images