Abstract
The rapid increase of traffic data generated by different sensing systems opens many opportunities to improve transportation services. An important opportunity is to enable stochastic routing that computes the arrival time probabilities for each suggested route instead of only the expected travel time. However, traffic datasets typically have many missing values, which prevents the construction of stochastic speeds. To address this limitation, we propose the Stochastic Spatio-Temporal Graph Convolutional Network (SST-GCN) architecture that accurately imputes missing speed distributions in a road network. SST-GCN combines Temporal Convolutional Networks and Graph Convolutional Networks into a single framework to capture both spatial and temporal correlations between road segments and time intervals. Moreover, to cope with datasets with many missing values, we propose a novel self-adaptive context-aware diffusion process that regulates the propagated information around the network, avoiding the spread of false information. We extensively evaluate the effectiveness of SST-GCN on real-world datasets, showing that it achieves from 4.6% to 50% higher accuracy than state-of-the-art baselines using three different evaluation metrics. Furthermore, multiple ablation studies confirm our design choices and scalability to large road networks.
Original language | English |
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Title of host publication | 30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2022 |
Editors | Matthias Renz, Mohamed Sarwat, Mario A. Nascimento, Shashi Shekhar, Xing Xie |
Publisher | Association for Computing Machinery (ACM) |
Publication date | 1 Nov 2022 |
Pages | 1-12 |
Article number | 14 |
ISBN (Electronic) | 9781450395298 |
DOIs | |
Publication status | Published - 1 Nov 2022 |
Event | 30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL GIS 2022 - Seattle, United States Duration: 1 Nov 2022 → 4 Nov 2022 |
Conference
Conference | 30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL GIS 2022 |
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Country/Territory | United States |
City | Seattle |
Period | 01/11/2022 → 04/11/2022 |
Sponsor | Apple, Esri, Google, Oracle, Wherobots |
Series | GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems |
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Bibliographical note
Publisher Copyright:© 2022 Owner/Author.
Keywords
- data imputation
- graph convolutional networks
- spatio-temporal