The　condition　assessment　of　in-service　asphalt　pavement　plays　a　key　role　in　pavement　maintenance　and　reha-bilitation.　Driven　by　historical　data,　the　Random　Forests　algorithm　with　the　Gini　importance　measurement　was　employed　to　identify　and　extract　key　detection　indicators　from　the　pavement　assessment　standards.　On　top　of　that,　a　cost-effective　method　for　pavement　condition　assessment　based　on　the　key　detection　indicators　was　proposed　by　decreasing　unnecessary　data　dimensions.　A　comparison　between　the　proposed　method　and　the　traditional　method　has　been　made　to　verify　the　feasibility　of　pavement　condition　assessment.　The　results　show　that　the　pavement　assessment　results　based　on　the　proposed　method　matched　well　with　those　based　on　the　traditional　method,　which　achieved　a　more　than　90%　consistency　of　overall　assessment　results　in　validation　samples.　Hence,　they　demonstrated　that　the　proposed　method　utilized　fewer　pavement　detection　indicators　to　reduce　the　burden　of　data　collection　and　improve　the　cost-effectiveness　of　pavement　condition　assessment　tasks.　In　the　future,　it　will　be　a　promising　alternative　to　assist　pavement　maintenance　and　rehabilitation.