Under the background of demand-driven and passenger-cargo collaborative transportation, to alleviate the contradiction between the idle transportation capacity of urban rail transit during off-peak periods and the rapid growth of urban logistics demand, the study constructs a collaborative optimization framework centered on train timetables and rolling stock turnover without adding key resources, aiming to improve the efficiency of the comprehensive system. Taking a single bidirectional urban rail transit line as the research object, on the basis of a preset passenger train diagram, a mixed-integer linear programming model is established with the objectives of minimizing the total adjustment of passenger train arrival and departure times and the total running time of freight trains. The model comprehensively considers practical constraints such as inter-station train operation, in-station operations, safety intervals, return-to-depot, and rolling stock turnover connection. The absolute value terms in the objective function are processed through linearization technology, and the commercial solver Gurobi is used for solution. Two types of scenario parameters, the "full demand coverage (all-station stop)" and the "selective demand coverage (skip-stop)", are set to evaluate the system trade-offs and collaborative effects under different demand coverage intensities. A case study of Jinan Rail Transit Line 2 verifies the feasibility of the proposed method. The model converges within the limited computation time. Compared with the benchmark scheme with fixed rolling stock connection, the comprehensive objective is only 29.8% of that of the benchmark. Collaborative optimization significantly reduces the comprehensive objective value and passenger-side timetable disturbances. Under the same collaborative framework, selective coverage further reduces the comprehensive objective and shortens the computation time compared with full coverage. The research shows that the proposed demand-driven collaborative optimization method can effectively release redundant capacity during off-peak periods, balance passenger experience and freight efficiency, and provide an efficient and implementable solution for urban rail transit to carry out off-peak passenger-cargo collaborative transportation services.