Abstract
With the urbanization, urban transportation has become a key factor restricting the development of a city. In a big city, it is important to improve the efficiency of urban transportation. The key to realize short-term traffic flow prediction is to learn its complex spatial correlation, temporal correlation and randomness of traffic flow. In this paper, the convolution neural network (CNN) is proposed to deal with spatial correlation among different regions, considering that the large urban areas leads to a relatively deep Network layer. First three gated recurrent unit (GRU) were used to deal with recent time dependence, daily period dependence and weekly period dependence. Considering that each historical period data to forecast the influence degree of the time period is different, three attention mechanism was taken into GRU. Second, a two-layer full connection network was applied to deal with the randomness of short-term flow combined with additional information such as weather data. Besides, the prediction model was established by combining these three modules. Furthermore, in order to verify the influence of spatial correlation on prediction model, an urban functional area identification model was introduced to identify different functional regions. Finally, the proposed model was validated based on the history of New York City taxi order data and reptiles for weather data. The experimental results show that the prediction precision of our model is obviously superior to the mainstream of the existing prediction methods.
This work was supported in part by the Natural Science Foundation of China grant 61672128, 61702076; the Fundamental Research Funds for the Central Universities DUT18JC39.
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Xu, X., Xu, L., Bai, Y., Xu, Z., Zhao, X., Liu, Y. (2020). MCA-TFP Model: A Short-Term Traffic Flow Prediction Model Based on Multi-characteristic Analysis. In: Qin, P., Wang, H., Sun, G., Lu, Z. (eds) Data Science. ICPCSEE 2020. Communications in Computer and Information Science, vol 1258. Springer, Singapore. https://doi.org/10.1007/978-981-15-7984-4_21
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