How　to　stimulate　right　number　of　drivers　to　serve　customers　at　the　right　time　is　the　core　of　operations　for　ride-sourcing　platforms.　Based　on　the　time　expanded　network,　considering　the　labor　supply　heterogeneity　of　drivers　with　different　behavioral　characteristics,　this　paper　assumes　that　drivers’　utility　consists　of　consumption　utility　and　reference　utility.　A　unified　framework　reconciling　two　behavioral　theories　is　established　to　depict　drivers’　labor　supply　behaviors　for　working　and　corresponding　equilibrium　solution　algorithm　is　proposed　to　analyze　the　schedule　choices　of　drivers　with　different　behavioral　characteristics　in　an　equilibrium　market.　Furthermore,　two　kinds　of　incentive　modes,　namely　the　commission-based　one　and　the　threshold-based　one,　are　proposed,　and　corresponding　models　are　developed　to　study　the　responses　of　drivers　with　different　behavioral　characteristics　to　different　incentives.　The　optimal　incentive　strategies　for　the　platform　are　explored.　The　results　show　that　drivers　driven　by　neoclassical　behavior　are　more　likely　to　work　or　supply　more　labor　in　response　to　an　extra　incentive　than　drivers　driven　by　income-target　behavior.　Threshold-based　incentive　is　more　effective　for　full-time　drivers　who　prefer　to　work　long　hours,　while　part-time　drivers　prefer　to　the　commission-based　incentive　and　threshold-based　incentive　with　low　threshold.　This　paper　provides　a　theoretical　reference　and　practical　basis　for　platform　to　understand　drivers’　labor　supply　behaviors　and　further　manage　and　stimulate　labors.　©　2024　Systems　Engineering　Society　of　China.　All　rights　reserved.