Traffic modeling is important in modern society. In this work we propose a new model on the optimal network pricing (Onp) with the assumption of oblivious users, in which the users remain oblivious to real-time traffic conditions and others' behavior. Inspired by works on transportation research and network pricing for selfish traffic, we mathematically derive and prove a new formulation of Onp with decision-dependent modeling that relax certain existing modeling constraints in the literature. Then, we express the Onp formulation as a constrained nonconvex stochastic quadratic program with uncertainty, and we propose an efficient algorithm to solve the problem, utilizing graph theory, sparse linear algebra and stochastic approximation. Lastly, we showcase the effectiveness of the proposed algorithm and the usefulness of the new Onp formulation. The proposed algorithm achieves a 5x speedup by exploiting the sparsity structure of the model.

翻译：交通建模在现代社会中具有重要意义。本文提出了一种基于无感知用户假设的最优网络定价新模型，其中用户对实时交通状况及他人行为保持无感知状态。受交通研究及自私流量网络定价相关工作的启发，我们通过数学推导证明了一种具有决策依赖建模特性的最优网络定价新表述，该表述放宽了现有文献中的某些建模约束。随后，我们将该最优网络定价问题表述为带不确定性的约束非凸随机二次规划，并综合运用图论、稀疏线性代数及随机逼近理论，提出一种高效求解算法。最后，我们通过实验验证了所提算法的有效性及新最优网络定价模型的实用性。该算法通过利用模型的稀疏结构特性，实现了五倍的加速效果。