We develop a Markovian traffic equilibrium model for ride-hailing in which vehicles, whether empty or hired, make sequential order-acceptance and link-choice decisions over a traffic network to maximize total discounted return in an infinite-horizon semi-Markov decision process. The model endogenizes both competition among empty vehicles for passenger demand and traffic congestion arising from road usage at the link level. We characterize equilibrium as the solution to a fixed-point system, establish its existence, and develop relaxed fixed-point iteration algorithms for equilibrium computation, with convergence results for specialized network structures. Computational experiments on realistic networks demonstrate the model's practical value for transportation planning. Ablation analyses reveal that ignoring either traffic congestion or drivers' forward-looking behavior can lead to potentially substantial biases in policy evaluation.

翻译：我们针对网约车服务构建了一个马尔可夫交通均衡模型，在该模型中，车辆（无论是空车还是被雇佣状态）在交通网络上依次做出订单接受和路径选择决策，以在无限时域的半马尔可夫决策过程中最大化总折扣回报。该模型内生化了空车之间对乘客需求的竞争，以及由路段使用导致的交通拥堵。我们将均衡状态刻画为一个不动点系统的解，证明了其存在性，并开发了用于均衡计算的松弛不动点迭代算法，同时给出了针对特定网络结构的收敛性结果。在真实网络上的计算实验证明了该模型在交通规划中的实用价值。消融分析表明，忽视交通拥堵或司机的前瞻性行为都可能导致政策评估出现潜在的重大偏差。