Stochastic Origin-Destination Matrix Forecasting Using Dual-Stage Graph Convolutional, Recurrent Neural Networks

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6 Citations (Scopus)


Origin-destination (OD) matrices are used widely in transportation and logistics to record the travel cost (e.g., travel speed or greenhouse gas emission) between pairs of OD regions during different intervals within a day. We model a travel cost as a distribution because when traveling between a pair of OD regions, different vehicles may travel at different speeds even during the same interval, e.g., due to different driving styles or different waiting times at intersections. This yields stochastic OD matrices. We consider an increasingly pertinent setting where a set of vehicle trips is used for instantiating OD matrices. Since the trips may not cover all OD pairs for each interval, the resulting OD matrices are likely to be sparse. We then address the problem of forecasting complete, near future OD matrices from sparse, historical OD matrices. To solve this problem, we propose a generic learning framework that (i) employs matrix factorization and graph convolutional neural networks to contend with the data sparseness while capturing spatial correlations and that (ii) captures spatio-temporal dynamics via recurrent neural networks extended with graph convolutions. Empirical studies using two taxi trajectory data sets offer detailed insight into the properties of the framework and indicate that it is effective.

Original languageEnglish
Title of host publication2020 IEEE 36th International Conference on Data Engineering
Number of pages12
Publication date2020
Article number9101647
ISBN (Print)978-1-7281-2904-4
ISBN (Electronic)9781728129037
Publication statusPublished - 2020
EventInternational Conference on Data Engineering - Dallas, United States
Duration: 20 Apr 202024 Apr 2020
Conference number: 36th


ConferenceInternational Conference on Data Engineering
CountryUnited States
SeriesProceedings of the International Conference on Data Engineering

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