This　paper　presents　a　novel　method　for　mining　the　individual　travel　behavior　regularity　of　different　public　transport　passengers　through　constructing　travel　behavior　graph　based　model.　The　individual　travel　behavior　graph　is　developed　to　represent　spatial　positions,　time　distributions,　and　travel　routes　and　further　forecasts　the　public　transport　passenger＇s　behavior　choice.　The　proposed　travel　behavior　graph　is　composed　of　macronodes,　arcs,　and　transfer　probability.　Each　macronode　corresponds　to　a　travel　association　map　and　represents　a　travel　behavior.　A　travel　association　map　also　contains　its　own　nodes.　The　nodes　of　a　travel　association　map　are　created　when　the　processed　travel　chain　data　shows　significant　change.　Thus,　each　node　of　three　layers　represents　a　significant　change　of　spatial　travel　positions,　travel　time,　and　routes,　respectively.　Since　a　travel　association　map　represents　a　travel　behavior,　the　graph　can　be　considered　a　sequence　of　travel　behaviors.　Through　integrating　travel　association　map　and　calculating　the　probabilities　of　the　arcs,　it　is　possible　to　construct　a　unique　travel　behavior　graph　for　each　passenger.　The　data　used　in　this　study　are　multimode　data　matched　by　certain　rules　based　on　the　data　of　public　transport　smart　card　transactions　and　network　features.　The　case　study　results　show　that　graph　based　method　to　model　the　individual　travel　behavior　of　public　transport　passengers　is　effective　and　feasible.　Travel　behavior　graphs　support　customized　public　transport　travel　characteristics　analysis　and　demand　prediction.