摘要翻译：
综合移动按需服务的设计需要综合考虑出行者的选择行为和运营商的运营策略之间的相互作用来设计财务上可持续的定价方案。然而，现有的研究大多集中在供应方的角度，忽略了存在共存运输网络时顾客选择行为的影响。我们提出了一个动态集成移动性随需应变服务运营策略评估的建模框架，该模型包含两个服务选项：门到门rideshare和带中转的rideshare。考虑不同运输方式的相关结构，提出了一种新的约束动态定价模型，该模型以运营商利润最大化为目标。将用户付费意愿视为一个随机约束，使得票价设置更加符合实际，同时使运营商利润最大化。与大多数研究不同，这些研究假设旅行需求是已知的，我们提出了一个需求学习过程，根据客户的历史购买数据来校准客户随时间的需求。我们通过在一个测试网络上的不同场景下的模拟，考虑了多模态市场中供需的相互作用，对所提出的方法进行了评估。在客户到达强度、车辆容量、用户支付意愿方差等方面对不同场景进行了测试。结果表明，所提出的机会约束分类价格优化模型在保证票价可接受的前提下，提高了运营商的利润。
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英文标题：
《Integrated ridesharing services with chance-constrained dynamic pricing
  and demand learning》
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作者：
Tai-Yu Ma, Sylvain Klein
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最新提交年份：
2020
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分类信息：

一级分类：Economics        经济学
二级分类：General Economics        一般经济学
分类描述：General methodological, applied, and empirical contributions to economics.
对经济学的一般方法、应用和经验贡献。
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一级分类：Quantitative Finance        数量金融学
二级分类：Economics        经济学
分类描述：q-fin.EC is an alias for econ.GN. Economics, including micro and macro economics, international economics, theory of the firm, labor economics, and other economic topics outside finance
q-fin.ec是econ.gn的别名。经济学，包括微观和宏观经济学、国际经济学、企业理论、劳动经济学和其他金融以外的经济专题
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英文摘要：
  The design of integrated mobility-on-demand services requires jointly considering the interactions between traveler choice behavior and operators' operation policies to design a financially sustainable pricing scheme. However, most existing studies focus on the supply side perspective, disregarding the impact of customer choice behavior in the presence of co-existing transport networks. We propose a modeling framework for dynamic integrated mobility-on-demand service operation policy evaluation with two service options: door-to-door rideshare and rideshare with transit transfer. A new constrained dynamic pricing model is proposed to maximize operator profit, taking into account the correlated structure of different modes of transport. User willingness to pay is considered as a stochastic constraint, resulting in a more realistic ticket price setting while maximizing operator profit. Unlike most studies, which assume that travel demand is known, we propose a demand learning process to calibrate customer demand over time based on customers' historical purchase data. We evaluate the proposed methodology through simulations under different scenarios on a test network by considering the interactions of supply and demand in a multimodal market. Different scenarios in terms of customer arrival intensity, vehicle capacity, and the variance of user willingness to pay are tested. Results suggest that the proposed chance-constrained assortment price optimization model allows increasing operator profit while keeping the proposed ticket prices acceptable.
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PDF链接：
https://arxiv.org/pdf/2001.09151