With　the　continuous　increase　of　constructions　in　highway,　road　maintenance　has　become　more　and　more　important.　Thus,　it　is　of　great　significance　to　develop　the　rapid,　intelligent　and　real-time　detection　technologies　for　road　surface　conditions.　This　paper　used　the　self-developed　driving　data　acquisition　APP　to　collect　the　vibration　acceleration　data　during　driving,　and　carried　out　the　feasibility　study　on　the　evaluation　method　of　pavement　rutting　using　smartphones.　Firstly,　the　collected　vibration　acceleration　data　are　de-noised,　and　the　vibration　characteristics　under　different　working　conditions　are　analyzed.　Secondly,　seven　time-domain　vibration　acceleration　indexes　with　high　correlation　with　pavement　rutting　are　extracted,　and　the　dimensions　of　seven　primary　indexes　are　reduced　to　two　independent　principal　components　by　principal　component　analysis.　Finally,　the　rutting　evaluation　model　based　on　convolutional　neural　network　is　established　and　compared　with　the　results　of　back　propagation　neural　network　and　multilayer　perceptron　neural　network.　The　results　show　that　the　average　relative　error　of　the　rutting　evaluation　model　based　on　the　convolutional　neural　network　is　16.6%,　which　is　lower　than　the　other　two　models.　It　indicates　that　the　pavement　rutting　can　be　evaluated　satisfactorily　by　smartphones.　In　addition,　this　paper　divided　the　evaluation　results　of　rutting　into　four　grades　(Excellent,　Good,　Medium　and　Poor)　and　displayed　them　in　different　colors　on　the　map.　This　study　is　of　great　significance　to　improve　the　level　of　intelligent　detection　of　road　rutting　and　road　maintenance　management.　©　2022,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Switzerland　AG.