Traffic flow distribution estimation and camera layout optimization method in traffic big data collection

Abstract

The invention discloses a traffic flow distribution estimation and camera layout optimization method in traffic large data collection. By using a spatial constraint relation for traffic flow distribution among urban road network nodes, data of a monitoring blind point are reasonably deduced through already collected traffic flow data. The method comprises specific steps: according to a spatial topological relation between an observation point and a near road section or an intersection, a traffic flow correlation mode between each other is determined; with the help of traffic flow data provided by upstream and downstream adjacent nodes, a traffic flow parameter of the current observation node in a single hop link is calculated; with the current observation node as a starting point, observation nodes in a multi-hop link are tracked sequentially; a traffic flow parameter in the case of multi-hop link is calculated; and the traffic monitoring camera layout is optimized. The existing traffic collected data are made full use of, the spatial distribution density of the collected urban traffic information is improved, and a scientific basis is provided for reasonable layout of traffic data collection equipment.

Description

technical field [0001] The invention belongs to the technical field of intelligent transportation, relates to a method for processing big traffic data, and in particular to a method for estimating traffic flow distribution and optimizing camera layout in collecting big traffic data. technical background [0002] With the rapid development of the city, the problem of urban traffic congestion is becoming more and more serious, and the use of intelligent transportation systems to alleviate the problem of urban traffic congestion has attracted people's attention. As the basic technology of intelligent transportation system, the real-time detection of traffic flow plays an important role in improving the traffic capacity of the road. Traffic sensors and detectors are the main sources of urban traffic big data, including massive and rich information such as traffic flow, speed, travel time, vehicle category, location and trajectory, and are the mainstays for subsequent real-time t...

AI Technical Summary

Problems solved by technology

However, since traffic information video detection technology has just emerged, the layout of monitoring points cannot achieve full coverage, but only covers key road sections and intersections with dense traffic flow as much as possible. There are many monitoring blind spots in traffic information collection. Data collection points Spatial distribution is sparse, which cannot meet the needs of traffic situation assessment in cities with dense road networks

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