Using Incomplete Information for Complete Weight Annotation of Road Networks -- Extended Version
Bin Yang, Manohar Kaul, Christian S. Jensen

TL;DR
This paper proposes a framework to annotate all edges in a road network with travel cost weights using limited GPS data, leveraging network topology and PageRank, to improve routing models.
Contribution
It introduces a novel regression-based approach that utilizes network topology and PageRank to infer weights for unobserved edges from sparse GPS data.
Findings
Effective weight annotation achieved on two Danish road networks.
The approach accurately estimates travel time and GHG emissions.
Network topology and PageRank enhance annotation quality.
Abstract
We are witnessing increasing interests in the effective use of road networks. For example, to enable effective vehicle routing, weighted-graph models of transportation networks are used, where the weight of an edge captures some cost associated with traversing the edge, e.g., greenhouse gas (GHG) emissions or travel time. It is a precondition to using a graph model for routing that all edges have weights. Weights that capture travel times and GHG emissions can be extracted from GPS trajectory data collected from the network. However, GPS trajectory data typically lack the coverage needed to assign weights to all edges. This paper formulates and addresses the problem of annotating all edges in a road network with travel cost based weights from a set of trips in the network that cover only a small fraction of the edges, each with an associated ground-truth travel cost. A general framework…
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Taxonomy
TopicsData Management and Algorithms · Transportation Planning and Optimization · Traffic Prediction and Management Techniques
