Data-Driven Classification in Road Networks

Sammanfattning: Connected and autonomous vehicles (CAVs) are an emerging trend in the transport sector and their impact on transportation, the economy, society and the environment will be tremendous. Much like the automobile shaped the way humans travelled, lived and worked during the 20th century, CAVs have yet again the potential to affect and reform all of these areas. Besides the imminent technological challenges on the robotic aspect of making CAVs become a market-ready reality, a plethora of ethical, social and legal questions will have to be addressed along the line. Knowledge of and interaction with the surrounding infrastructure and other actors in the system will be essential for CAVs in order to pave the way for progressive solutions to urgent sustainability and mobility issues in transportation.Road networks, i.e. the networks of roads and intersections, are the core infrastructure on which CAVs will operate. Thus, having detailed knowledge about them is key for CAVs in order to take the right decisions on both short-term actions that will affect individual traffic users in immediate situations and long-term actions that will affect entire transportation systems in the long run. Machine learning is nowadays a popular choice to extract and conglomerate knowledge from large amounts of data – and large amounts of data can be obtained about road networks. However, classical machine learning models are incapable of harnessing the graph-structured nature of road networks sufficiently.Graph neural networks (GNNs) are machine learning models of growing popularity that can explicitly leverage the complex topological structure of node dependencies in graphs, such as the ones observed in road networks. Road networks are sparse graphs that reside in a euclidean space, and therefore different to typical graphs studied in the literature. Also, crowd-sourced road network graphs often have incomplete attributes and are generally lacking the fine-grained level of detail in their encoded information that would be required for CAVs. Identifying the best representation of road network graphs and complementing their lacking detail with auxiliary data is therefore an important research direction.This thesis, therefore, addresses data-driven classification in road networks from two directions: A) the general approach of learning on spatial graphs of road network with GNNs, and B) complementing road network graphs with auxiliary data. Specifically, this thesis and the included papers address the exemplary task of road classifications and make the following contributions to the field:Paper A analyses how GNNs can be applied to road networks and how the networks are best represented. Different aggregator functions are compared on final classification performances. A novel aggregator and a neighbourhood sampling method are introduced, and the line graph transformation is identified as a suitable representation of road network graphs for GNNs.Paper B complements the road network graphs with mobility data from millions of GPS trajectories and introduces an equitemporal node spacing to create road segments of equal travel time. It further introduces remote sensing vision data as a potent complement to overcome shortcom-ings of the graph-based representation for road networks. Simple hand-crafted low-level vision features are used in this work. However, both the equitemporal node spacing and the simple vision features clearly exhibit improved classification performances.Finally, Paper C consolidates the complement of remote sensing data to the road network graphs. Through a general visual feature encoding of state-of-the-art pretrained vision back-bones that are carefully fine-tuned to the remote sensing domain, a further performance boost on the road classification task is achieved.