Abstract
Traffic congestion has become increasingly prominent. Effective prediction of road congestion will provide a great reference for urban road planning and residents’ travel. Taxi trajectory data objectively reflect the travel routes of the residents of a city. With good temporal and spatial characteristics and high timeliness, these data have become important in the study of urban spatio-temporal characteristics. In this paper, the PageRank-K clustering algorithm is used to analyse hotspots and cluster hotspots of a region. The shortest-distance matching algorithm based on the transition probability is used for map matching; then, the average speed of the road sections can be calculated, and the direction of taxis can be determined. The Dual_XGBoost model is used for traffic congestion prediction. Finally, we compared our model with similar models. We take roads in Shanghai as the test object. The results show that our method is faster in training, can predict congestion at any time and is more sensitive to long-term features than the other models.
Highlights
The expansion of urban space and the increase in population density make traffic congestion a urgent problem
A traffic congestion prediction algorithm based on multi-period hotspot clustering is proposed
Two or more cars on a certain section of the road have a large deviation in the driving angle, and these cars stay on the road for more than 15 minutes
Summary
The expansion of urban space and the increase in population density make traffic congestion a urgent problem. An intelligent transportation system is a comprehensive traffic management system proposed in recent years It can accurately detect a large range of road network situations in real time and can learn independently according to historical data; the real-time and predicted road network situations are communicated to users. Toon Bogaerts et al proposed a deep neural network that simultaneously extracts the spatial features of traffic using a graph convolution network and its temporal features by means of LSTM to make both short-term and long-term predictions [10]. They did not combine time-influencing factors, spatial-influencing factors and other factors to predict traffic congestion. (3) Dual_XGBoost is applied to traffic congestion prediction for the first time
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