This　research　intends　to　explore　external　factors　affecting　driving　safety　and　fuel　consumption,　and　build　a　risk　and　fuel　consumption　prediction　model　for　individual　drivers　based　on　natural　driving　data.　Based　on　120　taxi　drivers＇　natural　driving　data　during　4　months,　driving　behavior　data　under　various　conditions　of　the　roadway,　traffic,　weather,　and　time　of　day　are　extracted.　The　driver＇s　fuel　consumption　is　directly　collected　by　the　on-board　diagnostics　(OBD)　unit,　and　safety　index　is　calculated　based　on　Data　Threshold　Violations　(DTV)　and　Phase　Plane　Analysis　with　Limits　(PPAL)　considering　speed,　longitudinal　and　lateral　acceleration.　By　using　a　linear　mixed　model　explaining　the　fixed　effect　of　the　external　conditions　and　the　random　effect　of　the　driver,　the　influences　of　various　external　factors　on　fuel　consumption　and　safety　are　analyzed　and　discussed.　The　prediction　model　lays　a　foundation　for　drivers＇　fuel　consumption　and　risk　prediction　in　different　external　conditions,　which　could　help　improve　individual　driving　behavior　for　the　benefit　of　both　fuel　consumption　and　safety.