With　expansion　of　city　scale,　the　issue　of　public　transport　systems　will　become　prominent.　For　single-swipe　buses,　the　traditional　method　of　obtaining　section　passenger　flow　is　to　rely　on　surveillance　video　identification　or　manual　investigation.　This　paper　adopts　a　new　method:　collecting　wireless　signals　from　mobile　terminals　inside　and　outside　the　bus　by　installing　six　Wi-Fi　probes　in　the　bus,　and　use　machine　learning　algorithms　to　estimate　passenger　flow　of　the　bus.　Five　features　of　signals　were　selected,　and　then　the　three　machine　learning　algorithms　of　Random　Forest,　K-Nearest　Neighbor,　and　Support　Vector　Machines　were　used　to　learn　the　data　laws　of　signal　features.　Because　the　signal　strength　was　affected　by　the　complexity　of　the　environment,　a　strain　function　was　proposed,　which　varied　with　the　degree　of　congestion　in　the　bus.　Finally,　the　error　between　the　average　of　estimation　result　and　the　manual　survey　was　0.1338.　Therefore,　the　method　proposed　is　suitable　for　the　passenger　flow　identification　of　single-swiping　buses　in　small　and　medium-sized　cities,　which　improves　the　operational　efficiency　of　buses　and　reduces　the　waiting　pressure　of　passengers　during　the　morning　and　evening　rush　hours　in　the　future.