Bike-sharing　systems　have　been　deployed　globally.　One　of　the　key　issues　for　high-quality　bike-sharing　systems　is　to　rebalance　city-wide　stations　to　maintain　bike　availability.　Traditional　strategies,　such　as　repositioning　bikes　by　trucks　and　volunteers　based　on　historical　riding　records,　usually　operate　in　fixed　paths　and　limited　capacities,　lacking　the　flexibility　to　cope　with　the　highly　dynamic　and　context　dependent　riding　demands,　and　usually　suffer　from　high　costs　and　long　delays.　In　this　work,　we　propose　RedPacketBike,　an　incentive-driven,　crowd-based　station　rebalancing　framework　to　effectively　recruit　participants　from　hybrid　fleets　(e.g.,　volunteer　riders　and　hired　trucks)　based　on　the　accurate　forecast　of　bike　demand　leveraging　deep　learning　techniques.　First,　we　propose　a　spatiotemporal　clustering　method　to　extract　bike　demand　hotspots　from　fluctuating　bike　usage　data.　Then,　we　build　a　context-aware　deep　neural　network　named　BikeNet　to　forecast　the　trends　of　bike　demand　hotspots,　simultaneously　modeling　the　spatial　correlations　by　graph　convolution　networks　(GCN),　the　temporal　dependencies　by　long　short-term　memory　networks　(RNN),　and　the　contextual　factors　by　autoencoders　(AE).　Finally,　we　propose　a　reinforcement-learning-based　method　to　find　optimal　station　rebalancing　schemes　by　generating　station　rebalancing　tasks　with　an　integer　linear　programming　(ILP)　algorithm　and　allocating　tasks　to　participants　from　hybrid　fleets　with　dynamic　incentive　designs　and　reward　expectations.　Experiments　using　real-world　bike-sharing　system　data　collected　from　Citi　Bike　in　New　York　City　and　Mobike　in　Xiamen　City　validate　the　performance　of　our　framework,　achieving　a　demand　forecast　error　below　4.171　measured　in　MAE,　and　a　17.2%　improvement　of　station　availability　by　simulations　with　real-world　parameter　settings,　outperforming　the　state-of-the-art　baselines.