{"title":"基于学习的开放式驾驶员指导和再平衡,以减少乘车平台上乘客的等待时间","authors":"Jie Gao, Xiaoming Li, C. Wang, Xiao Huang","doi":"10.1109/ISC251055.2020.9239059","DOIUrl":null,"url":null,"abstract":"We propose a learning-based approach for open driver guidance and rebalancing in ride-hailing platforms. The objective is to further enhance the wait time reduction benefit of batched matching by incorporating learning-based open driver guidance and rebalancing. By leveraging the rider demand data, the guidance solutions are computed through the integration of machine learning techniques with a two-stage stochastic programming model. To validate the performance of the proposed approach, we conduct numerical experiments using the New York taxi trip data sets. Our results show that the proposed approach outperforms the single value estimation model and the parametric model using Poisson distribution in terms of average wait time. When assuming the open drivers are randomly located before the batching time window, the proposed approach reduces more than 70% of average wait time compared to batched matching without guidance.","PeriodicalId":201808,"journal":{"name":"2020 IEEE International Smart Cities Conference (ISC2)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Learning-based open driver guidance and rebalancing for reducing riders’ wait time in ride-hailing platforms\",\"authors\":\"Jie Gao, Xiaoming Li, C. Wang, Xiao Huang\",\"doi\":\"10.1109/ISC251055.2020.9239059\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We propose a learning-based approach for open driver guidance and rebalancing in ride-hailing platforms. The objective is to further enhance the wait time reduction benefit of batched matching by incorporating learning-based open driver guidance and rebalancing. By leveraging the rider demand data, the guidance solutions are computed through the integration of machine learning techniques with a two-stage stochastic programming model. To validate the performance of the proposed approach, we conduct numerical experiments using the New York taxi trip data sets. Our results show that the proposed approach outperforms the single value estimation model and the parametric model using Poisson distribution in terms of average wait time. When assuming the open drivers are randomly located before the batching time window, the proposed approach reduces more than 70% of average wait time compared to batched matching without guidance.\",\"PeriodicalId\":201808,\"journal\":{\"name\":\"2020 IEEE International Smart Cities Conference (ISC2)\",\"volume\":\"56 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-09-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE International Smart Cities Conference (ISC2)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISC251055.2020.9239059\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE International Smart Cities Conference (ISC2)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISC251055.2020.9239059","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Learning-based open driver guidance and rebalancing for reducing riders’ wait time in ride-hailing platforms
We propose a learning-based approach for open driver guidance and rebalancing in ride-hailing platforms. The objective is to further enhance the wait time reduction benefit of batched matching by incorporating learning-based open driver guidance and rebalancing. By leveraging the rider demand data, the guidance solutions are computed through the integration of machine learning techniques with a two-stage stochastic programming model. To validate the performance of the proposed approach, we conduct numerical experiments using the New York taxi trip data sets. Our results show that the proposed approach outperforms the single value estimation model and the parametric model using Poisson distribution in terms of average wait time. When assuming the open drivers are randomly located before the batching time window, the proposed approach reduces more than 70% of average wait time compared to batched matching without guidance.