The　increasing　availability　of　location-acquisition　technologies　has　resulted　in　huge　volumes　of　trajectories.　The　sheer　volume　of　these　data　sets　prevents　their　processing　by　traditional　centralized　technologies.　In　this　paper,　we　propose　a　MapReduce-based　extraction-and-group　framework　to　locate　traffic　hot　routes　from　taxis　track.　In　the　proposed　framework,　massive　trajectory　data　are　partitioned　into　data　chunks　so　that　they　can　be　processed　in　parallel　on　multiple　machines.　Then　the　low　speed　parts　from　each　trajectory　are　extracted　by　a　speed　based　clustering.　Finally,　a　MapReduce　inner-function　based　grouping　method　is　used　to　locate　traffic　hot　routes　from　all　low　speed　parts.　Based　on　this　extraction-and-group　framework,　we　develop　a　traffic　hot　route　locating　algorithm.　The　algorithm　was　evaluated　through　experiments　on　real　life　data　sets,　and　was　shown　to　have　considerable　potential　to　promptly　and　accurately　locate　traffic　hot　routes　from　massive　trajectory　data　through　various　analyses　on　the　experimental　results.