A method for predicting traffic flow at single intersection with multiple lanes based on gernn
A traffic flow and prediction method technology, applied in traffic flow detection, road vehicle traffic control system, traffic control system, etc., can solve the problems of high computational complexity, short running time, low computational complexity, etc. Complexity, the effect of improving accuracy
Active Publication Date: 2021-02-26
ZHEJIANG UNIV OF TECH
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[0004] In order to overcome the disadvantages of long running time and high computational complexity of existing road traffic prediction methods, the present invention provides a GERNN-based multi-lane traffic flow at a single intersection with short running time and low computational complexity. method of prediction
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[0018] The present invention will be further described below in conjunction with the accompanying drawings.
[0019] refer to Figure 1 to Figure 6 , a GERNN-based single intersection multi-lane traffic flow prediction method, including the following steps:
[0020] Step 1. Build a road traffic network
[0021] Taking the road sections between intersections as nodes and the lanes in the intersections as edges, the urban road network model based on the loop detector is described as:
[0022] G=(V,E,H)
[0023] Among them, V={v1,v 2 ,...,v m} refers to the set of road sections between intersections, m is the number of road sections, E={e ij |i,j∈N} is the set of lanes in the intersection, where e ij ≠e ji , H:E->S is the mapping function of the traffic state data on the lane;
[0024] Step 2. Construct node mapping matrix based on road traffic network
[0025] Based on the effective construction of road traffic complex network G, its adjacency matrix A is obtained:
[...
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The invention discloses a GERNN-based single-intersection multi-lane traffic flow prediction method. Firstly, a road traffic network is constructed; based on the road traffic network, a node mapping matrix is constructed; based on the node mapping matrix, a road traffic input matrix of a GERNN model is constructed; based on the road traffic input matrix of the GERNN model, a single-intersection multi-lane traffic flow prediction model is constructed; and finally, the single-intersection multi-lane traffic flow prediction model is verified. The shortcomings of high-dimensional and sparse spatiotemporal features of the existing traffic flow data are solved, the node in the network is projected to low-dimensional and dense space for calculation, and the traffic flow prediction precision is thus effectively improved.
Description
technical field [0001] The invention belongs to the field of traffic forecasting, and relates to a single intersection multi-lane traffic flow forecasting method based on GERNN (Graph Embedding Recurrent Neural Network, graph embedding recurrent neural network). Background technique [0002] With the continuous improvement of the socio-economic level and the continuous acceleration of the pace of life, people's demand for vehicles is also increasing, followed by serious traffic jams. How to effectively alleviate traffic congestion and allocate traffic resources more efficiently has become a top priority. The emergence of intelligent transportation system has effectively solved these problems to a certain extent, and road traffic flow prediction as a part of intelligent transportation system plays an irreplaceable role in this process. [0003] The existing road traffic flow prediction methods mainly include: Markov prediction, Kalman filter method, support vector machine, c...
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IPC IPC(8): G08G1/01G06N3/04H04L12/24
CPCG06N3/049G08G1/0104H04L41/145H04L41/147
Inventor 徐东伟戴宏伟彭鹏王永东魏臣臣朱钟华宣琦
Owner ZHEJIANG UNIV OF TECH