Generating a Computationally Representative Road Skeleton

This project was a part of my MS thesis. This work was accepted at the the DEXA 2018.


Road networks are the lifeline of a city and understanding its usage has a number of potential applications from transportation planning and engineering aspects to environmental management. While the full network is important to be analyzed for such applications, prudent planning needs one to identify the significant sections of the road network and prioritize them. Annual Average Daily Traffic (AADT), an estimate of the average daily traffic along a defined road segment, is one such data that helps in such endeavors. But, roads are also about connectivity and accessibility across different regions. Hence, this paper proposes a study that integrates the AADT data with implicit information derived from the road network to generate a computationally representative (CoRe) well connected sub-network, significantly smaller than the original network. While the AADT data analysis looks for road segments with high traffic, this paper proposes and evaluates a graph theory based approach for calculating road priorities purely based on the topological structure of the road network. The work further demonstrates the utility of the CoRe sub-network in terms of both, achieving gains in path computation and capturing the behavioral pattern of travelers. A case study of the Melbourne, Australia supports the feasibility and applicability of this knowledge integration approach.