Given a set <inline-formula><tex-math notation="LaTeX">$O$</tex-math></inline-formula> of data objects, a set <inline-formula><tex-math notation="LaTeX">$Q$</tex-math></inline-formula> of query points, a positive integer <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> , and an aggregate function <inline-formula><tex-math notation="LaTeX">$f$</tex-math></inline-formula> (e.g., <i>sum, max</i> , and <i>min</i> ), an aggregate <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> nearest neighbor query finds the <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> data objects from <inline-formula><tex-math notation="LaTeX">$O$</tex-math></inline-formula> that have the smallest aggregate distances with respect to the query points in <inline-formula><tex-math notation="LaTeX">$Q$</tex-math></inline-formula> . This query has a large application base such as location-based services, transportation scheduling, traffic monitoring, emergency management, etc. With the rapid development of positioning technologies, many real-life applications appeal to the continuous aggregate <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> nearest neighbor monitoring in road networks, where both the data objects and query points move along the networks, and the edge weights (e.g., the driving time) fluctuate over time. In this paper, we study the problem of <i>continuous aggregate <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="gao-ieq10-2911950.gif" xmlns:xlink="http://www.w3.org/1999/xlink"/></alternatives></inline-formula> nearest neighbor monitoring</i> (CA <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> NN monitoring for short) <i>in road networks</i> . We propose an efficient generic CA <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> NN monitoring framework, termed as <inline-formula><tex-math notation="LaTeX">$\mathsf{GMF}$</tex-math></inline-formula> , which is capable of processing three types of update, including data object update, query point update, and edge weight update. We introduce an essential concept, i.e., <i>safe distance</i> , into this framework, which helps to boost the update efficiency for CA <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> NN monitoring problem. Using an effective structure, termed as <i>partial distance matrix</i> , we identify the safe distance and form the candidate object set for CA <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> NN monitoring efficiently. Extensive experimental evaluation on real road networks demonstrates that, our proposed CA <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> NN monitoring framework is superior to the state-of-the-art method.