With the rapid development of urban construction in China, the number of private cars has sharply increased. This growth has led to transportation problems becoming one of the main problems plaguing many large and medium-sized cities in China. With the intensification of traffic problems, people's demand for real-time and accurate traffic information has become more urgent. When choosing travel destinations and modes, travel time becomes an important consideration for people. In order to provide timely and effective traffic information and path planning for travelers, it is necessary to accurately predict travel time. This study aims to explore travel time prediction methods. Firstly, fixed monitor technology and floating vehicle technology are used to obtain traffic data separately. Perform preprocessing work on the obtained data, including data recognition, data repair, etc. Secondly, for fixed monitor data and floating car data, travel time predictions are made separately. By analyzing and processing data from fixed monitors, the travel time of road sections can be predicted. At the same time, using floating car data can obtain more detailed travel time information. Finally, a data fusion method based on BP neural network is proposed, which combines fixed monitor data and floating car data to establish a travel time prediction model. Prove the effectiveness of data fusion through experiments, providing scientific basis for providing accurate traffic information and path planning.
Design of Travel Time Prediction System Based on Multi-Source Data Fusion
2024-12-06
668155 byte
Conference paper
Electronic Resource
English
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