Route selection based on performance measurements is an essential task in inter-domain Traffic Engineering. It can benefit from the detection of significant changes in RTT measurements and the understanding on potential causes of change. Among the extensive works on change detection methods and their applications in various domains, few focus on RTT measurements. It is thus unclear which approach works the best on such data.In this paper, we present an evaluation framework for change detection on RTT times series, consisting of: 1) a carefully labelled 34,008-hour RTT dataset as ground truth;2) a scoring method specifically tailored for RTT measurements. Furthermore, we proposed a data transformation that improves the detection performance of existing methods.Path changes are as well attended to. We fix shortcomings of previous works by distinguishing path changes due to routing protocols (IGP and BGP) from those caused by load balancing.Finally, we apply our change detection methods to a large set of measurements from RIPE Atlas. The characteristics of both RTT and path changes are analyzed; the correlation between the two are also illustrated. We identify extremely frequent AS path changes yet with few consequences on RTT, which has not been reported before.