合乘出行是共享经济背景下的典型商业模式之一，以降低出行费用、减少碳排放等优势，在城市出行市场占有重要份额。车辆资源分配是优化合乘出行服务的核心环节。为充分分析车辆分配过程，本文综合考虑多种出行方式、乘客选择行为对分配方案的影响，着重探讨基于合乘出行的混合出行模式的供需匹配问题，解析乘客出行模式选择行为与区域间车辆资源分配之间的内部关联。首先引入乘客-司机匹配函数表述车辆供给与乘客需求之间的关联，然后通过Logit模型模拟乘客出行方式选择行为，从而构建区域间资源分配优化模型。在模型求解方面，将资源分配分为乘客出行方式分配和车辆供给分配两阶段，整合拉格朗日松弛算法和梯度下降算法针对每个阶段涉及的决策问题进行求解，通过乘客选择行为和车辆分配方案的反馈更新两阶段的决策方案，从而建立区域多时段交通资源分配算法。最后，在路网分区基础上，生成随机算例对资源分配算法进行测试。结果表明，本文提出的方法能够有效地均衡区域间不同出行模式的车辆供给及出行需求，随着合乘车辆数量的减少，总收益值减少比例由7.32%逐渐增至25.37%；与基于行驶里程的车辆分配方法相比，本文提出的车辆分配算法有助于提高总收益值。

Ride-sharing is one of the typical models in the context of the sharing economy. With the advantages of reducing travel costs and carbon emissions, it has occupied an important share in the urban travel market. Vehicle resource allocation is the core link in optimizing carpooling services. To realize the overall management of travel modes, this paper comprehensively considered the influence of multiple travel modes and passengers’ selection behavior on the allocation scheme. Focusing on the supply and demand matching of the mixed travel mode based on ride-sharing travel, the internal correlation between the passenger travel mode selection behavior and the allocation of vehicle resources between regions was analyzed. A passenger-to-driver matching function was introduced to describe the relationship between vehicle supply and passenger demands. Then the passengers’ travel selection behavior was simulated by the Logit model, and an inter-regional resource allocation optimization model was constructed. In solving the optimization model, the allocation problem was divided into two stages: passenger travel mode allocation and vehicle supply allocation. The Lagrangian relaxation and the gradient descent algorithms were integrated to solve the decision-making problems involved in each stage. The solutions of the two stages were updated based on the feedbacks from passenger selection preference and vehicle allocation, so as to establish the regional multi-period traffic resource allocation algorithm. Finally, the resource allocation algorithm was tested by random generation of examples on a road network with 100 sub-regions. Results show that the method proposed in this paper can effectively balance the mobility allocation among regions with mixed travel modes. As the number of ride-sharing vehicles decreased, the decreasing proportion of total revenue increased from 7.32 % to 25.37 %. Compared with the vehicle allocation method based on mileage, the vehicle allocation algorithm proposed in this paper helps to improve the total revenue.