Three-dimensional urban traffic road network global state prediction method under large data environment

A technology of urban road network and global state, applied in the direction of traffic flow detection, forecasting, data processing applications, etc., can solve the problem of not including the influence of traffic elements into the forecasting method

Active Publication Date: 2016-12-21
SHANGHAI JIAO TONG UNIV
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Problems solved by technology

Most of the current research methods only consider a single road section or intersection when p...

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  • Three-dimensional urban traffic road network global state prediction method under large data environment
  • Three-dimensional urban traffic road network global state prediction method under large data environment
  • Three-dimensional urban traffic road network global state prediction method under large data environment

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Embodiment Construction

[0064] The invention combines the road network data of Shanghai to realize the prediction of the state of the global traffic road network. The method used in the present invention is different from other single variable prediction methods. The present invention, from the perspective of the global state, defines the global characteristics including the coupling item and the average speed of each area for the coupling situation of multiple road networks in Shanghai, and the traffic The sequence of states is transformed into a sequence of traffic mode numbers, and the sequence of mode numbers is predicted.

[0065] The general methods of road network data collection include road fixed-point detectors, floating cars, mobile communication data, vehicle photo detection, etc. The example data of the present invention is collected by coil sensors, which belongs to a kind of road fixed-point detectors. The collection interval is two minutes, including The types of roads include trunk r...

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Abstract

A three-dimensional urban traffic road network global state prediction method under a large data environment is disclosed. Aiming at a coupling multi-layer large-scale urban traffic road network, a global traffic mode containing coupling information is defined, a method of extracting a main road network characteristic is provided, a high dimension state time sequence is converted into a discrete state type sequence, and difficulties of high dimension data in processing and prediction aspects are simplified. From aspects of traffic periodicity, delay performance and other characteristics, a historical state transfer information database containing a multielement traffic characteristic is constructed, mass data information and a data driving idea are fully used and a Markov transition probability theory is applied so as to realize multi-step global traffic state prediction. Through the global traffic prediction, a traffic state of each area can be acquired one time. Compared to an existing traffic prediction method, by using the method of the invention, a long-term prediction result is acquired rapidly and a good prediction output effect is possessed.

Description

technical field [0001] The invention relates to the field of urban traffic road network traffic state prediction, in particular to a method for predicting the overall state of a three-dimensional urban traffic road network in a big data environment. Background technique [0002] Urban traffic status can not only affect personal travel experience, but also an important indicator of healthy urban development. The realization of urban intelligent transportation system (Intelligent Transportation System) can improve the efficiency of traffic operation, improve the quality of urban environment, and improve the quality of life of citizens. Therefore, intelligent transportation is the long-term focus of relevant researchers and urban management departments. Accurate and reliable traffic state definition and prediction provide the core foundation for intelligent transportation systems. [0003] After searching the literature of the prior art, it was found that early researchers use...

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Application Information

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IPC IPC(8): G08G1/01G06Q10/04G06Q50/26
Inventor 袁铖珏李德伟席裕庚
Owner SHANGHAI JIAO TONG UNIV
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