Vehicular networks are a promising technology to help solve complex problems of modern society, such as urban mobility. However, the vehicular environment has some characteristics that pose challenges for wireless communication in vehicular networks not usually found in traditional networks. Therefore, the scientific community is yet investigating alternative techniques to improve data delivery in vehicular networks. In this context, the recent and increasing availability of trajectory data offers us valuable information in many research areas. These data comprise the so-called "big trajectory data" and represent a new opportunity for improving vehicular networks. However, there is a lack of specific data mining techniques to extract the hidden knowledge from these data.
This thesis explores vehicle trajectory data mining to design intelligent vehicular networks. In the first part of this thesis, we deal with errors intrinsic to vehicle trajectory data that hinder their applicability. We propose a trajectory reconstruction framework composed of several preprocessing techniques to convert flawed GPS-based data to road-network constrained trajectories. This new data representation reduces trajectory uncertainty and removes problems such as noise and outliers compared to raw GPS trajectories. After that, we develop a novel and scalable cluster-based trajectory prediction framework that uses enhanced big trajectory data. Besides the prediction framework, we propose a new hierarchical agglomerative clustering algorithm for road-network constrained trajectories that automatically detects the most appropriate number of clusters. The proposed clustering algorithm is one of the components that allow the prediction framework to process large-scale datasets.
The second part of this thesis applies the enhanced trajectory representation and the prediction framework to improve the vehicular network. We propose the VDDTP algorithm, a novel vehicle-assisted data delivery algorithm based on trajectory prediction. VDDTP creates an extended trajectory model and uses predicted road-network constrained trajectories to calculate packet delivery probabilities. Then, it applies the predicted trajectories and some proposed heuristics in a data forwarding strategy, aiming to improve the vehicular network's global metrics (i.e., delivery ratio, communication overhead, and delivery delay). In this part, we also propose the DisTraC protocol to demonstrate the applicability of vehicular networks to detect traffic congestion and improve urban mobility. DisTraC uses V2V communication to measure road congestion levels cooperatively and reroute vehicles to reduce travel time.
We evaluate the proposed solutions through extensive experiments and simulations. For that, we prepare a new large-scale and real-world dataset based on the city of Rio de Janeiro, Brazil. We also use other real-world datasets publicly available. The results demonstrate the potential of the proposed data mining techniques (i.e., trajectory reconstruction and prediction frameworks) and vehicular networks algorithms.
| Identifer | oai:union.ndltd.org:uottawa.ca/oai:ruor.uottawa.ca:10393/44229 |
| Date | 02 November 2022 |
| Creators | Soares de Sousa, Roniel |
| Contributors | Boukerche, Azzedine |
| Publisher | Université d'Ottawa / University of Ottawa |
| Source Sets | Université d’Ottawa |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
Page generated in 0.0023 seconds