In　this　paper,　a　deep　learning　(DL)　framework　was　proposed　to　predict　the　taxi-passenger　demand　while　the　spatial,　the　temporal,　and　external　dependencies　were　considered　simultaneously.　The　proposed　DL　framework　combined　a　modified　density-based　spatial　clustering　algorithm　with　noise　(DBSCAN)　and　a　conditional　generative　adversarial　network　(CGAN)　model.　More　specifically,　the　modified　DBSCAN　model　was　applied　to　produce　a　number　of　sub-networks　considering　the　spatial　correlation　of　taxi　pick-up　events　in　the　road　network.　And　the　CGAN　model,　fed　with　the　historical　taxi　passenger　demand　and　other　conditional　information,　was　capable　to　predict　the　taxi-passenger　demands.　The　proposed　CGAN　model　was　made　up　with　two　long　short-term　memory　(LSTM)　neural　networks,　which　are　termed　as　the　generative　network　G　and　the　discriminative　network　D,　respectively.　Adversarial　training　process　was　conducted　to　the　two　LSTMs.　In　the　numerical　experiment,　different　model　layouts　were　compared.　It　was　found　that　different　network　layouts　provided　reasonable　accuracy.　With　limited　training　data,　more　LSTM　layers　in　the　generator　network　resulted　in　not　only　higher　accuracy,　but　also　more　difficulties　in　training.　Comparisons　were　also　conducted　between　the　proposed　prediction　model　and　four　typical　approaches,　including　the　moving　average　method,　the　autoregressive　integrated　moving　method,　the　neural　network　model,　and　the　LSTM　neural　network　model.　The　comparison　results　showed　that　the　proposed　model　outperformed　all　the　other　methods.　And　the　repeated　experiment　indicated　that　the　proposed　CGAN　model　provided　significant　better　predictions　than　the　LSTM　model　did.　Future　research　was　recommended　to　include　more　datasets　for　testing　the　model　and　more　information　for　improving　predictive　performance.