More　and　more　people　in　mega　cities　are　choosing　to　travel　by　public　transportation　due　to　its　convenience　and　punctuality.　It　is　widely　acknowledged　that　there　may　be　some　potential　associations　between　passengers.　Their　travel　behavior　may　be　working　together,　shopping　together,　or　even　some　abnormal　behaviors,　such　as　stealing　or　begging.　Thus,　analyzing　association　between　passengers　is　very　important　for　management　departments.　It　is　very　helpful　to　make　operational　plans,　provide　better　services　to　passengers　and　ensure　public　transport　safety.　In　order　to　quickly　explore　the　association　between　passengers,　we　propose　a　multi-view　interactive　exploration　method　that　provides　five　interactive　views?passenger　3D　travel　trajectory　view,　passenger　travel　time　pixel　matrix　view,　passenger　origin-destination　chord　view,　passenger　travel　vehicle　bubble　chart　view　and　passenger　2D　travel　trajectory　view.　It　can　explore　the　associated　passengers　from　multiple　aspects　such　as　travel　trajectory,　travel　area,　travel　time,　and　vehicles　used　for　travel.　Using　Beijing　public　transportation　data,　the　experimental　results　verified　that　our　method　can　effectively　explore　the　association　between　passengers　and　deduce　the　relationship.