Optimizing route planning for Mobility-as-a-Service platforms, such as carpooling, involves solving combinatorial problems under precedence and multi-candidate constraints. Traditional methods struggle to balance real-time requirements and solution quality due to computational complexity. We introduce DeepQUBO, a quantum-compatible framework that reformulates the Precedence-Constrained Generalized Traveling Salesman Problem (GTSP-PC) into a sparse Quadratic Unconstrained Binary Optimization (QUBO) model. The framework employs auxiliary variables that decouple complex constraints, maintaining optimality while reducing QUBO elements by 51% for connections and 76% for constraints relative to classical encodings. Experiments on Chengdu road networks and a Coherent Ising Machine (CIM) demonstrate 338 \(\times \) faster convergence than conventional approaches. Frustration analysis confirms DeepQUBO’s enhanced suitability for quantum optimization, achieving lower constraint-frustration ratios across benchmark datasets. Our work bridges service orchestration with quantum-ready optimization, offering practical value for large-scale transportation systems.