Abstract
Origin-Destination (OD) Matrix Prediction is an important part of public transportation service which aims to predict the number of passenger demands from one region to another and capture the passengers’ mobility patterns. This problem is challenging because it requires forecasting not only the number of demands within a region, but the origin and destination of each trip as well. To address this challenge, we propose an effective model, ODCRN (Origin-Destination Convolution Recurrent Network) which incorporates traffic context and bi-directional semantic information. First, we obtain the semantic embedded features of the region as the static traffic context by the Node2vec algorithm, and the traffic flow of the region is counted as the dynamic traffic context. Second, we construct two adjacency matrices which represent origin-destination and destination-origin travel demands within urban areas respectively based on the OD matrices of each time slot, and use the graph convolutional network to aggregate traffic context information of the semantic neighbors in both directions. Then, we use a unit constructed by GRU and the graph convolution network to capture the spatial-temporal correlations of the input data. Finally, we use those correlations and traffic contexts to predict the OD matrix for the next time slot. Our model is evaluated on TaxiNYC and TaxiCD datasets, and experimental results demonstrate the superiority of our ODCRN model against the state-of-the-art approaches.
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This work was supported in part by the High Performance Computing Center of Central South University.
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Chang, J., Liang, T., Xiao, W., Kuang, L. (2024). Origin-Destination Convolution Recurrent Network: A Novel OD Matrix Prediction Framework. In: Gao, H., Wang, X., Voros, N. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 562. Springer, Cham. https://doi.org/10.1007/978-3-031-54528-3_8
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