Overconfident Coordinates: Quantifying Confidence in Traceroute Geolocation

Abstract

Studies of Internet paths often attach router locations to traceroute hops using commercial geolocation databases, rDNS labels, Geofeeds, and IXP metadata. These sources provide useful hints, but they report point locations without calibrated confidence, leaving researchers unable to tell whether a geographic path is trustworthy. We introduce Path Consistency Scoring (PCS), a passive framework that evaluates router geolocation as a path-level consistency problem. PCS models each traceroute as a sequence of candidate city-level locations and uses a Hidden Markov Model to fuse local evidence with speed-of-light constraints and empirical latency priors. PCS produces a path consistency score summarizing how well metadata and observed RTT increments support a coherent geographic interpretation. Because this score is only meaningful when latency proxies for geography, we also define a Path-Model Alignment metric that compares speed-of-light residual increments of the decoded path against a reference path. We evaluate on 413,354 RIPE Atlas traceroutes and a 6,555-path subset verified by active probing. On validated paths, 94.2% of decoded sequences achieve mean error below 200 km. PCS is largely GeoDB-agnostic; median scores vary by less than 5% across four commercial databases, while the alignment metric reveals that over half of DB-IP and IP2Location paths require substantial correction, compared with 15% for IPinfo. This lets downstream analyses quantify confidence in their geographic conclusions rather than inheriting database accuracy without qualification.