Traffic　prediction　plays　a　significant　role　in　Intelligent　Transportation　Systems　(ITS).　Although　many　datasets　have　been　introduced　to　support　the　study　of　traffic　prediction,　most　of　them　only　provide　time-series　traffic　data.　However,　urban　transportation　systems　are　always　susceptible　to　various　factors,　including　unusual　weather　and　traffic　accidents.　Therefore,　relying　solely　on　historical　data　for　traffic　prediction　greatly　limits　the　accuracy　of　the　prediction.　In　this　paper,　we　introduce　Beijing　Text-Traffic　(BjTT),　a　large-scale　multimodal　dataset　for　traffic　prediction.　BjTT　comprises　over　32,000　time-series　traffic　records,　capturing　velocity　and　congestion　levels　on　more　than　1,200　roads　within　the　5th　ring　area　of　Beijing.　Meanwhile,　each　piece　of　traffic　data　is　coupled　with　a　text　describing　the　traffic　system　(including　time,　location,　and　events).　We　detail　the　data　collection　and　processing　procedures　and　present　a　statistical　analysis　of　the　BjTT　dataset.　Furthermore,　we　conduct　comprehensive　experiments　on　the　dataset　with　state-of-the-art　traffic　prediction　methods　and　text-guided　generative　models,　which　reveal　the　unique　characteristics　of　the　BjTT.　The　dataset　is　available　at　https://github.com/ChyaZhang/BjTT.