Pavement　roughness　monitoring　has　always　been　a　concern　in　the　field　of　road　asset　management.　However,　the　utilization　of　laser　profilometers　to　measure　pavement　roughness　is　costly　and　inconvenient.　To　address　these　challenges,　this　study　developed　a　low-cost,　lightweight,　and　rapid　approach　for　pavement　roughness　estimation　using　smartphone　sensor　data.　Firstly,　the　feasibility　of　the　chosen　smartphone　in　vibration　acceleration　acquisition　was　examined　through　micro-scale　shaking　table　tests.　Then,　the　Categorical　Boosting　(CatBoost)　model　with　sequential　model-based　optimization　(SMBO)　approach　was　developed　for　smartphone-based　pavement　roughness　estimation.　Finally,　the　feature　importance　and　feature　interaction　effects　in　the　pavement　roughness　estimation　task　were　interpreted　using　Shapley　Additive　exPlanations　(SHAP)　and　Partial　Dependence　Plots　(PDP),　respectively.　The　results　show　that　the　chosen　smartphone　has　great　potential　in　vibration　data　acquisition,　and　the　recorded　data　are　highly　consistent　with　the　results　obtained　by　the　professional　accelerometer.　The　CatBoost　model　outperforms　the　other　reference　models　in　terms　of　pavement　roughness　estimation　accuracy,　with　coefficient　of　determination　(R2)　of　0.807,　root　mean　square　error　(RMSE)　of　0.553,　and　mean　absolute　error　(MAE)　of　0.433.　Furthermore,　the　feature　interpretation　results　indicate　that　the　smartphone-based　pavement　roughness　estimation　approach　relies　on　the　coupling　effects　of　vibration　and　velocity　features.　This　study　provides　a　novel　insight　into　the　lightweight　pavement　roughness　estimation　approach,　which　has　potential　implications　for　assisting　pavement　maintenance　decision-making　in　road　asset　management.