MA2GCN: Multi Adjacency relationship Attention Graph Convolutional Networks for Traffic Prediction using Trajectory data

• URL: http://arxiv.org/abs/2401.08727v2
• Date: Thu, 18 Jan 2024 05:25:22 GMT
• Title: MA2GCN: Multi Adjacency relationship Attention Graph Convolutional Networks for Traffic Prediction using Trajectory data
• Authors: Zhengke Sun, Yuliang Ma
• Abstract summary: This paper proposes a new traffic congestion prediction model - Multi Adjacency relationship Attention Graph Convolutional Networks(MA2GCN) It transformed vehicle trajectory data into graph structured data in grid form, and proposed a vehicle entry and exit matrix based on the mobility between different grids. Compared with multiple baselines, our model achieved the best performance on Shanghai taxi GPS trajectory dataset.
• Score: 1.147374308875151
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The problem of traffic congestion not only causes a large amount of economic losses, but also seriously endangers the urban environment. Predicting traffic congestion has important practical significance. So far, most studies have been based on historical data from sensors placed on different roads to predict future traffic flow and speed, to analyze the traffic congestion conditions of a certain road segment. However, due to the fixed position of sensors, it is difficult to mine new information. On the other hand, vehicle trajectory data is more flexible and can extract traffic information as needed. Therefore, we proposed a new traffic congestion prediction model - Multi Adjacency relationship Attention Graph Convolutional Networks(MA2GCN). This model transformed vehicle trajectory data into graph structured data in grid form, and proposed a vehicle entry and exit matrix based on the mobility between different grids. At the same time, in order to improve the performance of the model, this paper also built a new adaptive adjacency matrix generation method and adjacency matrix attention module. This model mainly used gated temporal convolution and graph convolution to extract temporal and spatial information, respectively. Compared with multiple baselines, our model achieved the best performance on Shanghai taxi GPS trajectory dataset. The code is available at https://github.com/zachysun/Taxi_Traffic_Benchmark.

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