Dynamic Pricing Analysis under Demand-Supply Equilibrium of Autonomous-Mobility-on-Demand Services

Networks and Spatial Economics Pub Date : 2024-09-07 DOI:10.1007/s11067-024-09648-w

Ta-Yin Hu, Yu-Chun Hung

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Abstract

Autonomous Mobility-on Demand (AMoD) combines self-driving and Mobility-on-Demand (MoD) services, allowing passengers to enjoy the last mile service. Due to the success of Uber, Lift, and other ride-sourcing companies, previous research has discussed pricing strategies in the ride-sourcing market. This study combines ridesharing and dynamic pricing strategy in the AMoD system, building models to maximize social welfare. Shared Autonomous Vehicles (SAV) ridesharing and dispatching models are constructed, and numerical experiments are conducted on a real road network using different factors. Linear regression models based on the simulation data from the dispatching model are established to predict the average waiting time and meeting rate. The regression models are applied to the ride-sourcing market model to conduct dynamic pricing experiments. We use approximate dynamic programming (ADP) to solve the dynamic pricing multiplier for each time interval. The experimental results show that ridesharing can improve the service rate of rides, and the dynamic pricing strategy achieves higher social welfare by balancing supply and demand compared to the fixed pricing strategy.

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按需自主移动服务供需平衡下的动态定价分析

自主按需移动（AMoD）将自动驾驶与按需移动（MoD）服务相结合，让乘客享受最后一英里服务。由于 Uber、Lift 和其他共享出行公司的成功，以往的研究对共享出行市场的定价策略进行了讨论。本研究结合了 AMoD 系统中的共享出行和动态定价策略，建立了社会福利最大化模型。本研究构建了共享自动驾驶汽车（SAV）的搭乘和调度模型，并在真实道路网络上使用不同因素进行了数值实验。根据调度模型的模拟数据建立线性回归模型，预测平均等待时间和见面率。将回归模型应用于乘车外包市场模型，进行动态定价实验。我们使用近似动态编程（ADP）来求解每个时间间隔的动态定价乘数。实验结果表明，共享乘车可以提高乘车服务率，与固定定价策略相比，动态定价策略通过平衡供需实现了更高的社会福利。

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Networks and Spatial Economics

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