A novel real-time data driven method for floating vehicle speed trend prediction

Abstract

Traffic congestion in urban areas has become a major worldwide problem. As an important direction of the Intelligent Transportation System (ITS), traffic-speed prediction can help drivers better plan routes and shorten travel time according to IOT techniques, thereby effectively alleviating the problem of traffic congestion. Traffic speed changes dynamically over time, so forecasting using historical data may not be able to quickly adapt to sudden changes in traffic conditions, and predicted traffic conditions may lag behind. The use of real-time data can capture instantaneous changes in traffic conditions, which is more adaptable to different traffic scenarios. In order to further explore the advantages of using real-time data for traffic forecasting, a novel real-time Data Driven method for traffic Speed trend Prediction (2DSP) is proposed. The 2DSP method can predict the macro-level traffic speed trend (rising or falling) by using only near-real-time microscopic vehicle information, which effectively captures the dynamic changes in traffic speed. In addition, an adaptive time-slicing strategy based on traffic density is proposed. This strategy dynamically divides time slices based on traffic density, reducing the frequency of data processing and improving the user experience. The effectiveness of the 2DSP method is validated using two real traffic datasets of floating vehicles. The experimental results show that the 2DSP method has good potential for real-time traffic-speed trend prediction.