Public　transportation　agencies　call　for　an　intuitive,　interactive,　and　reusable　visualization　tool　to　detect　patterns　of　crime　(i.e.　pickpockets　and　gangs)　or　missing　commuters　on　public　transportation　systems.　Few　existing　visualization　techniques　have　visually　explored　mobility　correlations　of　targets　and　their　companions,　who　are　characterized　in　diverse　mobility　types,　by　using　discrete　travel　hints　extracted　from　a　massive　amount　of　data.　To　fill　this　gap,　a　visual　analytical　system　is　provided　to　conduct　a　group-based　and　individual-based　exploration　of　mobility　correlations　of　passengers　of　interest,　based　on　an　auto　integration　of　multiple　queries.　How　passengers　differ　from　or　correlate　with　each　other　are　further　examined　based　on　their　spatiotemporal　distributions　in　trajectories　and　ODs.　Real-world　case　studies,　as　well　as　user　feedback　made　by　30　participants,　demonstrate　the　effectiveness　of　the　system　in　detecting　specific　targets　and　their　companions　featured　in　diverse　mobility　types,　or　in　characterizing　their　spatiotemporal　aggregation　patterns　for　a　further　tracking　on　public　transportation　systems.