A Multi-objective Data Mining Approach for Road Traffic Prediction - Inria - Institut national de recherche en sciences et technologies du numérique Accéder directement au contenu
Communication Dans Un Congrès Année : 2018

A Multi-objective Data Mining Approach for Road Traffic Prediction

Ilias Kalamaras
  • Fonction : Auteur
  • PersonId : 1033548
Anastasios Drosou
  • Fonction : Auteur
  • PersonId : 991086
Konstantinos Votis
  • Fonction : Auteur
  • PersonId : 991094
Dionysios Kehagias
  • Fonction : Auteur
  • PersonId : 1033549
Dimitrios Tzovaras
  • Fonction : Auteur
  • PersonId : 929466

Résumé

Road traffic prediction for the efficient traffic control has lately been in the focus of the research community, as it can solve significant urban issues, such as city evacuation plans, increased concentration of CO2 emissions and delays caused by extended traffic jams. The current paper proposes a novel approach for multi-variate data mining from past traffic data (i.e. average speed values per road), so as to dynamically detect all significant correlations between the road network components (i.e. the segments of the roads) by mapping the latter onto a low dimensional embedding. Multiple traffic-related features (e.g. speed correlation, spatial proximity, phase difference, etc.) are utilized in a multi-objective optimization framework, producing all Pareto-optimal embeddings, each one corresponding to a different trade-off between the objectives. The operator is provided with the option to interactively select among these Pareto-optimal solutions, so as to explore the most descriptive sets of road influences. The proposed method has been evaluated on real traffic data, while the evaluation of the forecasting performance of the multi-objective approach exhibited accuracy improvement with respect single-objective approaches.
Fichier principal
Vignette du fichier
467708_1_En_36_Chapter.pdf (674.6 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01821080 , version 1 (22-06-2018)

Licence

Paternité

Identifiants

Citer

Ilias Kalamaras, Anastasios Drosou, Konstantinos Votis, Dionysios Kehagias, Dimitrios Tzovaras. A Multi-objective Data Mining Approach for Road Traffic Prediction. 14th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), May 2018, Rhodes, Greece. pp.425-436, ⟨10.1007/978-3-319-92007-8_36⟩. ⟨hal-01821080⟩
200 Consultations
30 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More