In　the　urban　traffic　system　with　ridesharing　programs,　we　develop　a　generalized　stochastic　user　equilibrium　model　to　formulate　travelers＇　mode　and　route　choice　behavior.　To　suit　more　general　scenarios,　the　proposed　model　takes　into　consideration　travelers＇　heterogeneity　in　terms　of　car　ownership　and　value　of　time,　and　travelers＇　limited　perceived　information　based　on　the　stochastic　user　equilibrium　principle　instead　of　Wardrop＇s　user　equilibrium　principle.　The　proposed　model　is　formulated　as　variational　inequalities　and　an　equivalent　nonlinear　mixed　complementarity　problem　due　to　the　inseparable　and　asymmetric　travel　cost　functions.　Furthermore,　we　address　the　decision-making　problem　of　ridesharing　compensation　from　the　perspective　of　traffic　managers　and　policy-makers　who　want　to　minimize　the　total　travel　cost　and　vehicular　air　pollution　emissions　simultaneously.　A　bi-objective　optimization　model　and　two　single-objective　optimization　models　are　proposed　to　formulate　this　decision-making　problem,　in　which　travelers＇　mode　and　route　choice　behavior　has　been　respected.　As　a　mathematical　problem　with　complementarity　constraints,　the　bi-objective　optimization　model　is　solved　by　an　improved　Non-Dominated　Sorting　Genetic　Algorithm　II　to　generate　a　set　of　Pareto-optimal　solutions　for　policy-makers　and　allow　them　to　choose　desired　solutions.　Finally,　several　numerical　experiments　based　on　two　different　scales　of　networks　are　conducted　to　demonstrate　the　properties　of　the　problem　and　the　performance　of　the　proposed　model　and　algorithm.　The　results　show　that　rational　pricing　of　ridesharing　compensation　can　indeed　mitigate　urban　traffic　congestion　and　pollution　emissions　simultaneously.　Moreover,　by　integrating　travelers＇　choice　behavior　based　on　the　stochastic　user　equilibrium　principle　instead　of　the　user　equilibrium　principle　in　the　ridesharing　compensation　optimization　model,　this　study　derives　a　series　of　more　effective　decision-making　strategies　for　ridesharing　compensation.