Predicting the price of taxicabs using Artificial Intelligence: A hybrid approach based on clustering and ordinal regression models

IF 8.3 1区 工程技术 Q1 ECONOMICS
Bhawana Rathore , Pooja Sengupta , Baidyanath Biswas , Ajay Kumar
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引用次数: 0

Abstract

With increasing popularity of ride-hailing services, it becomes important to build transparent and explainable pricing models using artificial intelligence (AI). While the literature on this domain is growing steadily, the application of AI in pricing prediction is relatively new. We drew upon the New York City Taxi dataset to build pricing prediction models to bridge this gap. Our contributions are as follows. First, we created unique clusters for yellow and app-based cabs, leading to a dynamic pricing mechanism across different zones in New York City. Second, we converted a prediction problem into a classification problem by transforming the prices into four distinct quartiles. Third, we applied variable importance schemes to generate top predictors in each cluster. Fourth, our study reveals that differential effects of each predictor for cab-pricing across different clusters exist. Fifth, the “congestion surcharge” is significant for only a few clusters, and imposing such surcharges could hurt the overall taxicab industry. In this manner, our study contributes to the academic literature on taxicab pricing by offering transparent and actionable insights for stakeholders and policymakers, informed by robust AI-driven pricing models and empirical analyses of real-world data.

利用人工智能预测出租车价格:基于聚类和序数回归模型的混合方法
随着打车服务的日益普及,利用人工智能(AI)建立透明、可解释的定价模型变得非常重要。虽然这一领域的文献正在稳步增长,但人工智能在定价预测中的应用却相对较新。我们利用纽约市出租车数据集建立定价预测模型,以弥补这一差距。我们的贡献如下。首先,我们为黄色出租车和基于应用程序的出租车创建了独特的集群,从而形成了纽约市不同区域的动态定价机制。其次,我们将价格转化为四个不同的四分位数,从而将预测问题转化为分类问题。第三,我们应用变量重要性方案来生成每个群组中的顶级预测因子。第四,我们的研究发现,每个预测因子对不同群组的出租车价格存在不同的影响。第五,"拥堵附加费 "仅对少数聚类有显著影响,而征收此类附加费可能会损害整个出租车行业。通过这种方式,我们的研究为利益相关者和政策制定者提供了透明、可行的见解,为有关出租车定价的学术文献做出了贡献,我们的研究以强大的人工智能驱动定价模型和对真实世界数据的实证分析为基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
16.20
自引率
16.00%
发文量
285
审稿时长
62 days
期刊介绍: Transportation Research Part E: Logistics and Transportation Review is a reputable journal that publishes high-quality articles covering a wide range of topics in the field of logistics and transportation research. The journal welcomes submissions on various subjects, including transport economics, transport infrastructure and investment appraisal, evaluation of public policies related to transportation, empirical and analytical studies of logistics management practices and performance, logistics and operations models, and logistics and supply chain management. Part E aims to provide informative and well-researched articles that contribute to the understanding and advancement of the field. The content of the journal is complementary to other prestigious journals in transportation research, such as Transportation Research Part A: Policy and Practice, Part B: Methodological, Part C: Emerging Technologies, Part D: Transport and Environment, and Part F: Traffic Psychology and Behaviour. Together, these journals form a comprehensive and cohesive reference for current research in transportation science.
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