The　bicycle　is　a　healthy　and　sustainable　transport　mode　due　to　its　emission-free　characteristics.　To　increase　bicycle　use,　it　is　fundamental　to　provide　bicycle-friendly　environments.　To　better　monitor　bicycle　environments,　this　study　proposed　the　concept　of　bicycling　environment　quality　(BEQ),　which　was　defined　by　perceived　satisfaction　and　conflict　level.　Data　collection　was　conducted　at　19　road　segments　in　five　sites　located　in　Beijing,　China.　Then,　speed-related　and　acceleration-related　bicycling　behavior　indicators　(BBIs)　were　extracted　from　data　collected　using　sensors　on　mobile　phones,　while　bicycling　environment　indicators　(BEIs),　such　as　bicycle　flow,　were　extracted　from　recorded　data.　Taking　the　BBIs　and　BEIs　as　input　attributes,　a　two-level　BEQ　classification　assessment　model　based　on　a　random　forest　(RF)　algorithm　was　constructed.　The　proposed　RF-based　classification　assessment　model　was　able　to　produce　approximately　77.35%　overall　correct　classification.　The　results　demonstrate　the　feasibility　of　using　GPS　data　in　evaluating　BEQ.　In　addition,　a　novel　dockless　bicycle-sharing　system　(DBS)-based　framework　for　bicycle　traffic　monitoring　is　discussed,　which　is　of　great　significance　in　the　sustainable　development　of　bicycles.　This　study　provides　a　theoretical　method　for　objective　BEQ　assessment.　It　can　further　be　used　by　planners　and　road　administrators　to　monitor　and　improve　BEQ　and　by　individual　cyclists　for　optimal　route　choice.