Traffic jam prediction control system based on big data

Abstract

The invention discloses a traffic jam prediction control system based on big data. The system comprises a central processing unit, a big data processing center, a traffic flow monitoring system, a GPS navigation system, a road condition acquisition module, a route acquisition module, a route congestion time calling module, a route congestion analysis module, a replacement route planning module, a control system, a wireless transmission module and a display terminal. Road conditions of an original route are comprehensively mastered through the GPS navigation system, the traffic flow monitoring system and the road condition acquisition module, the road condition information is acquired through the road condition acquisition module, and whether a road section is congested or not is analyzed. According to the comparison between the predicted congestion duration of the congested road section and the time required for the vehicle to arrive at the congested road section, whether the vehicle encounters a traffic jam condition when arriving at the congested road section is predicted, so that a replacement route is selected according to a prediction result, and the time wasted when the vehicle encounters the traffic jam during driving is reduced; and dredging efficiency and the dredging effect of the congested road section are improved to a certain extent.

Description

technical field [0001] The invention relates to the technical field of big data, in particular to a big data-based traffic jam prediction control system. Background technique [0002] With the development and expansion of cities, road congestion is becoming more and more serious. The main contradiction is the contradiction between the supply and demand of transportation. In other words, the supply of existing urban transportation cannot meet the current demand for transportation in the city. To solve this contradiction, there are many ways: expand the existing roads to increase traffic supply; reduce traffic demand through policies to reduce the expansion of traffic scale; use intelligent traffic to coordinate the relationship between supply and demand, and improve the efficiency and service level of the traffic system. Several methods are parallel, and some improvement is still needed in the aspect of intelligent transportation. The existing intelligent traffic management s...

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Problems solved by technology

[0002] With the development and expansion of cities, road congestion is becoming more and more serious. The main contradiction is the contradiction between the supply and demand of transportation. In other words, the supply of existing urban transportation cannot meet the current demand for transportation in the city. To solve this contradiction, there are many ways: expand the existing roads to increase traffic supply; reduce traffic demand through policies to reduce the expansion of traffic scale; use intelligent traffic to coordinate the relationship between supply and demand, and improve the efficiency and service level of the traffic system. Several methods are parallel, and some improvement is still needed in the aspect of intelligent transportation. The existing intelligent traffic management system obtains traffic information through various measurement and collection devices, and conducts traffic broadcast based on the analysis of big data, which can help vehicles plan routes Avoid congestion and provide more convenient traffic management. However, when abnormal traffic events have caused traffic congestion, there are still problems that cannot be managed and cleared as soon as possible due to the large number of vehicles. If traffic congestion is predicted when abnormal traffic events occur And notify the vehicle that has not arrived to change the route, which can improve the efficiency and effect of dredging in traffic jams to a certain extent

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