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Express way traffic state prediction method taking spatial-temporal correlation into account at different times

A traffic state, time-space correlation technology, applied in the field of intelligent transportation, can solve the problem that real-time data cannot effectively meet the needs of traffic management departments and travelers, and achieve the effect of improving prediction accuracy and overcoming the single prediction variable.

Active Publication Date: 2016-06-22
BEIHANG UNIV
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Problems solved by technology

[0002] The rapid development of ITS has made the traffic flow detection information of urban expressways more and more complete. However, due to the dynamic and time-varying characteristics of traffic conditions, real-time data cannot effectively meet the needs of traffic management departments and travelers.

Method used

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  • Express way traffic state prediction method taking spatial-temporal correlation into account at different times
  • Express way traffic state prediction method taking spatial-temporal correlation into account at different times
  • Express way traffic state prediction method taking spatial-temporal correlation into account at different times

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Embodiment Construction

[0016] The present invention will be further described in detail with reference to the accompanying drawings and embodiments.

[0017] The present invention is a method for predicting the traffic state of an expressway that considers time-space correlation by time period. The flow is as follows: figure 1 shown, including the following steps:

[0018] (1) Define the target area and adjacent road sections

[0019] Determine the target area of ​​the urban expressway to be studied, and define the surrounding road sections adjacent to the target road section, use the fixed-point coil detector to obtain the time series data of traffic flow and speed of all road sections in the target area, and preprocess the collected data, According to the confidence interval of the 95% confidence level of the traffic status data of each road section at each time of the day, the abnormal data is filtered out. For the missing data, according to the dynamic traffic flow characteristics, the weighted...

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Abstract

The present invention discloses an express way traffic state prediction method taking spatial-temporal correlation into account at different times. The method comprises: firstly, performing analysis period dynamic division of flow, speed and time sequences through adoption of a sequential cluster, and dividing the whole day into analysis periods with different traffic characteristics without disturbing traffic parameter time sequences; and selecting multivariable vector autoregression models aiming at different periods, comprehensively considering the spatial-temporal correlation of the upstream and downstream traffic flows, and predicting the flow or the speed of target places. The dynamic period division of the express way traffic state prediction method taking spatial-temporal correlation into account at different times provides a cheap, easy and substantially improved efficiency basic method for express way traffic state short-time prediction; and compared with a traditional method without considering the upstream and downstream traffic flow influence, the express way traffic state prediction method taking spatial-temporal correlation into account at different times considers the vector autoregression model of the spatial-temporal correlation after the periods are divided, so that the prediction results are obviously improved in precision.

Description

technical field [0001] The invention belongs to the field of intelligent transportation, and can be applied to accurately grasp the time-space correlation of urban expressway traffic flow, and accurately predict the short-term traffic flow and driving speed of the urban expressway by time intervals. Background technique [0002] The rapid development of ITS has made the traffic flow detection information of urban expressways more and more complete. However, due to the dynamic and time-varying characteristics of traffic conditions, real-time data cannot effectively meet the needs of traffic management departments and travelers. As the main skeleton of the urban road network, expressways can accurately grasp the time-varying characteristics of its traffic flow and predict its traffic status, which has important theoretical research value and practical significance for refined traffic management and improvement of travel services. [0003] Short-term traffic state prediction is...

Claims

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Application Information

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IPC IPC(8): G08G1/01
CPCG08G1/0104
Inventor 陈鹏王云鹏鲁光泉丁川鹿应荣
Owner BEIHANG UNIV
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