Big　data　in　transportation　provide　a　basis　for　revealing　hidden　mobility　patterns　in　observable　travel　behaviors　of　moving　objects.　Understanding　these　patterns　may　provide　theoretical　foundations　for　practical　applications,　such　as　travel　demand　prediction,　passenger　flow　organization,　land　use　planning,　or　incident　management.　Despite　these　merits,　several　issues　are　encountered　in　these　applications,　as　big　data　have　high-dimensional　entities,　various　spatiotemporal　dynamics,　and　complex　mobility　connections.　These　issues　include　expressing　the　patterns　in　a　consistent　way,　detecting　multiple　outliers　among　them,　portraying　their　inner　complex　correlations,　modeling　their　spatiotemporal　polymorphism,　or　visualizing　them　in　an　integrated　manner.　To　deal　with　these　complex　characteristics,　previous　methods　adopted　in　the　era　of　small　data　have　to　be　upgraded.　Thus,　we　reviewed　3　747　academic　studies　published　between　2010　and　2020　and　explored　the　distributions　of　co-occurring　hot　keywords,　topic　variations,　and　publication　preferences　using　a　knowledge-mapping　tool.　A　systematic　summary　was　given　to　describe　the　progress　made　in　five　mobility-pattern-based　research　directions,　namely　normality　analysis,　abnormality　analysis,　correlation　analysis,　prediction　analysis,　and　visual　analytics.　In　particular,　in　the　field　of　normality　analysis,　the　progress　of　activity　pattern　analysis,　travel　category　segmentation,　and　special　group　analysis　was　first　reviewed.　Based　on　the　normality　analysis,　two　types　of　approaches　in　the　field　of　abnormality　analysis　were　further　summarized,　namely　module-based　and　data-driven　approaches.　With　the　knowledge　of　the　ways　identifying　normal　or　abnormal　behaviors,　correlation　analysis　was　further　reviewed　to　determine　the　developing　trends　of　detecting　mobility　correlations　based　on　different　data　sources.　All　of　the　above　research　results　serve　as　solid　bases　for　prediction　analysis　of　mobility　patterns,　which　was　also　reviewed　in　this　paper.　The　review　results　show　that　two　main　types　of　approaches　are　used　to　fulfill　the　task:　statistics-based　and　data-driven　estimations.　Finally,　previous　studies　on　visual　analytics　were　reviewed　to　determine　how　transportation　data　can　be　visualized　based　on　user　interaction,　macro　exploration,　micro　exploration,　and　overall　exploration.　Overall,　we　identified　several　potential　challenges　in　the　aforementioned　research　directions,　and　proposed　three　promising　development　trends　in　data　integration,　model　innovation,　and　scheme　revolution.　Our　ultimate　aim　is　to　provide　potential　guidance　for　future　studies　involving　mobility　pattern　analysis　based　on　new　theories　and　technologies.　©　2021,　Editorial　Department　of　China　Journal　of　Highway　and　Transport.　All　right　reserved.