The second-best congestion pricing (SBCP) problem is one of the most challenging problems in transportation because of its two-level hierarchical structure. In spite of various intriguing attempts at solving SBCP, existing solution methods are either heuristic without a convergence guarantee or suitable for solving SBCP on small networks only. In this paper, we first reveal some convexity-based structural properties of the marginal value function reformation of SBCP, and then, by effectively exploiting these structural properties, we propose two dedicated decomposition methods for solving SBCP on large-scale networks, which are different from existing methods in that they avoid linearizing nonconvex functions. We establish the convergence of the two decomposition methods under commonly used conditions and provide the maximum number of iterations for deriving an approximate stationary solution. The computational experiments based on a collection of real road networks show that in comparison with three existing popular methods, the two proposed methods are capable of solving SBCP on larger-scale networks, and for instances that can be solved by existing methods, the two proposed methods are substantially faster. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods and Analysis. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72032001, 72431007, 72131007, 72021002, and 12271161]. L. Guo was also supported by the Natural Science Foundation of Shanghai [Grant 22ZR1415900]. X. Wang was also supported by the Fundamental Research Funds for the Central Universities and CCF-DiDi GAIA Collaborative Research Funds for Young Scholars. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0144 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0144 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

For on-demand dynamic ride-pooling services, e.g., Uber Pool and DiDi Pinche, a well-designed vehicle dispatching strategy is crucial for platform efficiency and passenger experience. Most existing dispatching strategies overlook incoming pairing opportunities, therefore suffer from short-sighted limitations. In this paper, we propose a forward-looking vehicle dispatching strategy, which first predicts the expected distance saving that could be brought about by future orders and then solves a bipartite matching problem based on the prediction to match passengers with partially occupied or vacant vehicles or keep passengers waiting for next rounds of matching. To demonstrate the performance of the proposed strategy, a number of simulation experiments and comparisons are conducted based on the real-world road network and open trip data from Haikou, China. Results show that the proposed strategy outperforms the baseline strategies by generating approximately 31% more distance saving and 18% less average passenger detour distance. It indicates the significant benefits of considering future pairing opportunities in dispatching, and highlights the effectiveness of our innovative forward-looking vehicle dispatching strategy in improving system efficiency and user experience for dynamic ride-pooling services.