{"title":"Predicting the price of taxicabs using Artificial Intelligence: A hybrid approach based on clustering and ordinal regression models","authors":"Bhawana Rathore , Pooja Sengupta , Baidyanath Biswas , Ajay Kumar","doi":"10.1016/j.tre.2024.103530","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"185 ","pages":"Article 103530"},"PeriodicalIF":8.3000,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1366554524001212/pdfft?md5=ed0aa5ccc527092ae428d764d64d154e&pid=1-s2.0-S1366554524001212-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transportation Research Part E-Logistics and Transportation Review","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1366554524001212","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
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.
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.