COVID-19紧急情况下的店内购物行程预测和影响因素

IF 1.3 4区 工程技术 Q3 TRANSPORTATION SCIENCE & TECHNOLOGY Transportation Planning and Technology Pub Date : 2023-11-12 DOI:10.1080/03081060.2023.2282059
Md Ashraful Imran, Kate Hyun
{"title":"COVID-19紧急情况下的店内购物行程预测和影响因素","authors":"Md Ashraful Imran, Kate Hyun","doi":"10.1080/03081060.2023.2282059","DOIUrl":null,"url":null,"abstract":"ABSTRACTDespite the rapid growth of online shopping during COVID-19, a significant number of consumers still prefer in-store shopping. This study leverages two years (i.e. pre-pandemic and pandemic) of smartphone location data to develop machine learning (ML) models, specifically Random Forest (RF) and Extreme Gradient Boosting (XGBoost), for predicting community (e.g. block group (BG)) level in-store shopping trips for department stores, shopping malls, supermarkets, and wholesale stores. This study identifies that temperature, accessibility to stores, and the number of online shopping last-mile delivery are the three most important factors influencing shopping trips; specifically, the extent of online shopping is a critical determinant for supermarkets and wholesale store trip-makings before and during the pandemic. The models developed and important determinants of shopping trips will provide useful insight for shopping trip demand forecasting as well as impact assessments of relevant policies on in-store shopping demand during emergencies.KEYWORDS: COVID-19shopping tripse-commercemachine learningshopping facilities AcknowledgementsThis project was funded by the TranSET (21ITSUTA03), a U.S. DOT University Transportation Center.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by Tran-SET – Transportation Consortium of South-Central States [grant number 21ITSUTA03].","PeriodicalId":23345,"journal":{"name":"Transportation Planning and Technology","volume":"29 10","pages":"0"},"PeriodicalIF":1.3000,"publicationDate":"2023-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"In-store shopping trip predictions and impact factors during COVID-19 emergencies\",\"authors\":\"Md Ashraful Imran, Kate Hyun\",\"doi\":\"10.1080/03081060.2023.2282059\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACTDespite the rapid growth of online shopping during COVID-19, a significant number of consumers still prefer in-store shopping. This study leverages two years (i.e. pre-pandemic and pandemic) of smartphone location data to develop machine learning (ML) models, specifically Random Forest (RF) and Extreme Gradient Boosting (XGBoost), for predicting community (e.g. block group (BG)) level in-store shopping trips for department stores, shopping malls, supermarkets, and wholesale stores. This study identifies that temperature, accessibility to stores, and the number of online shopping last-mile delivery are the three most important factors influencing shopping trips; specifically, the extent of online shopping is a critical determinant for supermarkets and wholesale store trip-makings before and during the pandemic. The models developed and important determinants of shopping trips will provide useful insight for shopping trip demand forecasting as well as impact assessments of relevant policies on in-store shopping demand during emergencies.KEYWORDS: COVID-19shopping tripse-commercemachine learningshopping facilities AcknowledgementsThis project was funded by the TranSET (21ITSUTA03), a U.S. DOT University Transportation Center.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by Tran-SET – Transportation Consortium of South-Central States [grant number 21ITSUTA03].\",\"PeriodicalId\":23345,\"journal\":{\"name\":\"Transportation Planning and Technology\",\"volume\":\"29 10\",\"pages\":\"0\"},\"PeriodicalIF\":1.3000,\"publicationDate\":\"2023-11-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Transportation Planning and Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/03081060.2023.2282059\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"TRANSPORTATION SCIENCE & TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transportation Planning and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/03081060.2023.2282059","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"TRANSPORTATION SCIENCE & TECHNOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

摘要尽管2019冠状病毒病疫情期间网络购物快速增长,但仍有相当一部分消费者更喜欢实体店购物。本研究利用两年(即流行病前和流行病前)的智能手机位置数据来开发机器学习(ML)模型,特别是随机森林(RF)和极端梯度增强(XGBoost),用于预测百货商店、购物中心、超市和批发商店的社区(例如街区组(BG))水平的店内购物之行。该研究发现,温度、商店的可达性和网上购物的最后一英里配送数量是影响购物之旅的三个最重要的因素;具体而言,网上购物的程度是大流行之前和期间超市和批发商店出行的关键决定因素。所建立的模型和购物行程的重要决定因素将为紧急情况下的购物行程需求预测以及相关政策对店内购物需求的影响评估提供有用的见解。本项目由TranSET (21ITSUTA03)资助,该TranSET是美国交通部大学交通中心。披露声明作者未报告潜在的利益冲突。本研究得到了中南部各州trans - set - Transportation Consortium的支持[拨款号21ITSUTA03]。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
In-store shopping trip predictions and impact factors during COVID-19 emergencies
ABSTRACTDespite the rapid growth of online shopping during COVID-19, a significant number of consumers still prefer in-store shopping. This study leverages two years (i.e. pre-pandemic and pandemic) of smartphone location data to develop machine learning (ML) models, specifically Random Forest (RF) and Extreme Gradient Boosting (XGBoost), for predicting community (e.g. block group (BG)) level in-store shopping trips for department stores, shopping malls, supermarkets, and wholesale stores. This study identifies that temperature, accessibility to stores, and the number of online shopping last-mile delivery are the three most important factors influencing shopping trips; specifically, the extent of online shopping is a critical determinant for supermarkets and wholesale store trip-makings before and during the pandemic. The models developed and important determinants of shopping trips will provide useful insight for shopping trip demand forecasting as well as impact assessments of relevant policies on in-store shopping demand during emergencies.KEYWORDS: COVID-19shopping tripse-commercemachine learningshopping facilities AcknowledgementsThis project was funded by the TranSET (21ITSUTA03), a U.S. DOT University Transportation Center.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by Tran-SET – Transportation Consortium of South-Central States [grant number 21ITSUTA03].
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Transportation Planning and Technology
Transportation Planning and Technology 工程技术-运输科技
CiteScore
3.40
自引率
6.20%
发文量
24
审稿时长
12 months
期刊介绍: Transportation Planning and Technology places considerable emphasis on the interface between transportation planning and technology, economics, land use planning and policy. The Editor welcomes submissions covering, but not limited to, topics such as: • transport demand • land use forecasting • economic evaluation and its relationship to policy in both developed and developing countries • conventional and possibly unconventional future systems technology • urban and interurban transport terminals and interchanges • environmental aspects associated with transport (particularly those relating to climate change resilience and adaptation). The journal also welcomes technical papers of a more narrow focus as well as in-depth state-of-the-art papers. State-of-the-art papers should address transport topics that have a strong empirical base and contain explanatory research results that fit well with the core aims and scope of the journal.
期刊最新文献
Access to green and gray urban nature amenities: exploring equity in Montreal's built environment Estimating the willingness-to-pay of local residents and tourists for shared demand responsive transit services Effect of side road junction design enhancements and flows on priority for crossing pedestrians and cyclists Measuring the social effects of urban logistics facilities development, the case of New York city Impacts of automated trucks on U.S. freight movements: application and enhancement of the random-utility-based multiregional input-output model
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1