通过自适应学习乘客价格接受度，实现拼车动态定价优化
Adaptive Optimisation of Ride-Pooling Personalised Fares in a Stochastic Framework
- 基于每日反馈迭代优化个性化定价策略
- 10天内对拼车乘客价格接受度预测准确率超90%
- 适合关注出行平台收益与用户体验平衡的研究者
拼车系统要成功，必须提供有吸引力的服务，即以合理价格补偿乘客的时间成本。由于乘客对时间价值的差异性极大，其可接受价格对运营商而言是未知的。本文表明，运营商可在10天内以超过90%的准确率学习个体乘客的价格接受水平，从而优化个性化定价。我们提出一种自适应定价策略：每天构建逐步贴近乘客期望的报价，吸引持续增长的需求。结果表明，通过学习乘客行为特征，运营商不仅能提升乘客效用，还能增加自身利润。此外，该知识使运营商可剔除低效拼车组合，聚焦于高吸引力且盈利的配对方案。
原文摘要 · Abstract (English)
Ride-pooling systems, to succeed, must provide an attractive service, namely compensate perceived costs with an appealing price. However, because of a strong heterogeneity in a value-of-time, each traveller has his own acceptable price, unknown to the operator. Here, we show that individual acceptance levels can be learned by the operator (over $90\%$ accuracy for pooled travellers in $10$ days) to optimise personalised fares. We propose an adaptive pricing policy, where every day the operator constructs an offer that progressively meets travellers' expectations and attracts a growing demand. Our results suggest that operators, by learning behavioural traits of individual travellers, may improve performance not only for travellers (increased utility) but also for themselves (increased profit). Moreover, such knowledge allows the operator to remove inefficient pooled rides and focus on attractive and profitable combinations.
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