
Research Article
Origin-Destination Convolution Recurrent Network: A Novel OD Matrix Prediction Framework
@INPROCEEDINGS{10.1007/978-3-031-54528-3_8, author={Jiayu Chang and Tian Liang and Wanzhi Xiao and Li Kuang}, title={Origin-Destination Convolution Recurrent Network: A Novel OD Matrix Prediction Framework}, proceedings={Collaborative Computing: Networking, Applications and Worksharing. 19th EAI International Conference, CollaborateCom 2023, Corfu Island, Greece, October 4-6, 2023, Proceedings, Part II}, proceedings_a={COLLABORATECOM PART 2}, year={2024}, month={2}, keywords={OD matrix prediction Graph diffusion convolution Spatial-temporal data}, doi={10.1007/978-3-031-54528-3_8} }
- Jiayu Chang
Tian Liang
Wanzhi Xiao
Li Kuang
Year: 2024
Origin-Destination Convolution Recurrent Network: A Novel OD Matrix Prediction Framework
COLLABORATECOM PART 2
Springer
DOI: 10.1007/978-3-031-54528-3_8
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-DestinationConvolutionRecurrentNetwork) 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 representorigin-destinationanddestination-origintravel 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 onTaxiNYCandTaxiCDdatasets, and experimental results demonstrate the superiority of our ODCRN model against the state-of-the-art approaches.