Safety-driven road width estimations from vector data

Abstract

In recent decades, it is well known that transportation systems have a crucial role in the economic development and social prosperity of modern societies. While road width can be considered as one of the most important factors of the road environments, there is no clear definition of it. To better understand this, we need to reconsider the width of a road not as a single numerical value but as a complex concept that can be interpreted in different ways. Different road users may refer to different width values for the same road. Thus, while estimating road width seems to come with great benefits for different real-world cases, a fundamental rethinking of the purpose of this entire process is necessary.
Road safety management is an application whose overall process is strongly affected by the width of the road. While there are conflicting theories about the effect that road width can have on road safety, we may need to reconsider a few things before exploring their relation. In this thesis, we introduce a novel approach for estimating road width and linking it with roads in such a way that the overall process of road safety management application could benefit.
Moreover, even road width estimation is not a new topic most of the studies so far use LiDAR point cloud or satellite images as input. In our approach, vector data coming from open-sources are used. Different inputs come with different limitations. The advantages, as well as the drawbacks of the different inputs, will be discussed.
Finally, one of the main objectives of this project is to develop a methodology that will be generally applicable. Vector data that can be found in different datasets would be used. Some additional deliverables will result from our efforts to overcome some of the challenges that have arisen in achieving this goal. A methodology that standardizes road vector data and a methodology that identifies the location and the type of the different intersections will be developed