About
Areas of research:
- Knowledge discovery
- Supervised learning
- Multiple predictive models
- Applied knowledge discovery
- Intelligent transportation systems
- Planning and operations of public transports
Areas of research: - Knowledge discovery Supervised learning Multiple predictive models Applied knowledge discovery - Intelligent transportation systems Planning and operations of public transports
Areas of research:
- Knowledge discovery
- Intelligent transportation systems
2022
Authors
Bhanu, M; Kumar, R; Roy, S; Mendes-Moreira, J; Chandra, J;
Publication
ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PAKDD 2022, PT I
Abstract
Capturing complex spatio-temporal features of thousands of correlated taxi-demand time-series in the city makes the traffic flow prediction problem a challenging task. Hence, several Deep Neural Network (DNN) models have been developed to mimic the latent spatio-temporal behaviour of taxi-demand time-series in a city to improve the prediction results. Despite, good performance of recent DNN based traffic prediction techniques, such models can only identify either adjacent or connected regions with direct or transitive connection; hence they fail to capture spatio-temporal correlation among regions that exhibit implicit or latent connection. Additionally, the dependency of the recent DNN models on recursive components facilitates error propagation during feature aggregation without any counter strategy for it. In view of these existing glitches, we introduce a novel DNN model, graph Multi-Head Convolution for Spatio-Temporal Aggregation (gMHC-STA) which supports capturing spatio-temporal correlation among regions with explicit and implicit connection both. Moreover, gMHC-STA aggregates both spatial and temporal characteristics using multi-head attention; thus overriding recursive RNN or its variant approach to prevent noise propagation. The experimental results of gMHC-STA on two real-world city taxi-demand datasets report minimum of 6.5–10% improvement over the best state-of-the-art on standard benchmark metric in varying experimental conditions. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
2022
Authors
Pedroto, M; Jorge, A; Mendes Moreira, J; Coelho, T;
Publication
PROGRESS IN ARTIFICIAL INTELLIGENCE, EPIA 2022
Abstract
2022
Authors
Strecht, P; Mendes-Moreira, J; Soares, C;
Publication
Advanced Data Mining and Applications - Lecture Notes in Computer Science
Abstract
2021
Authors
Bhanu, M; Mendes Moreira, J; Chandra, J;
Publication
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
Abstract
2021
Authors
Leite, B; Abdalrahman, A; Castro, J; Frade, J; Moreira, J; Soares, C;
Publication
ICAART: PROCEEDINGS OF THE 13TH INTERNATIONAL CONFERENCE ON AGENTS AND ARTIFICIAL INTELLIGENCE - VOL 2
Abstract
Supervised Thesis
2021
Author
André Fernandes dos Santos
Institution
UP-FCUP
2021
Author
Joana Sofia Campos Santos
Institution
UP-FEUP
2021
Author
Joana Maria Lima Valente
Institution
UP-FCUP
2021
Author
João Miguel Mendes Ribeiro Agulha
Institution
UP-FEUP
2021
Author
André Oliveira Sousa
Institution
UP-FEUP
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