A traffic prediction model based on multiple factors

• Published in: The Journal of supercomputing 2021-03, Vol.77 (3), p.2928-2960
• Main Authors: Wang, Jingjuan, Chen, Qingkui
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Braking; Compilers; Computer Science; Convolution; Dashboards; Datasets; Embedded systems; Intelligent transportation systems; Interpreters; Object recognition; Prediction models; Processor Architectures; Programming Languages; Real time; Temporal distribution; Traffic accidents; Traffic flow; Traffic models; Traffic safety; Transportation networks; Video compression; Weather
• ISSN: 0920-8542; 1573-0484
• DOI: 10.1007/s11227-020-03373-0

Traffic prediction is a vital part of intelligent transportation systems. The ability of traffic risk prediction is of great significance to prevent traffic accidents and reduce the damages in a proactive way. Because of the complexity, uncertainty and dynamics of spatiotemporal dependence of traffic flow, accurate traffic state prediction becomes a challenging issue. Most neural networks are compute intensive and memory intensive, making them hard to deploy on embedded systems with limited hardware resources. A real-time and high-compressed video object detection structure is proposed. For traffic prediction, many previous studies only explore the utility of a single factor in their prediction and a few multi-factor researches are conducted. Other studies focus on the temporal distribution of traffic flow, ignoring the spatial correlation. And some methods based on graph convolutional networks (GCNs) do not consider the dynamics of graph structure which is a crucial factor to traffic prediction. In this paper, we analyze and process the onboard video captured by the dashboard camera real time. A high accurate deep learning model called varying spatiotemporal graph-based convolution model (VSTGC) is proposed to express the spatiotemporal structures and forecast future traffic safety trends from previous traffic flow. The traffic detailed features (such as vehicle type, braking state, whether changing lanes or not) and external variables (such as weather, time and road condition) are extracted from our big datasets. We conduct extensive experiments to evaluate the VSTGC model on real-world traffic datasets. Experiments on our real traffic dataset show that the proposed model performs competitive performances over the other state-of-the-art approaches.