[{"data":1,"prerenderedAt":374},["ShallowReactive",2],{"publication-2019\u002Ftravel-time-prediction-in-transport-and-logistics-en":3,"publication-members":62},{"_path":4,"_dir":5,"_draft":6,"_partial":6,"_locale":7,"title":8,"description":9,"_hidden":6,"authors":10,"authors_orcid":15,"year":20,"doi":21,"openalex_id":22,"venue":23,"abstract_screenshot":24,"keywords":25,"body":42,"_type":55,"_id":56,"_source":57,"_file":58,"_stem":59,"_extension":60,"locale":61},"\u002Fpublications\u002F2019\u002Ftravel-time-prediction-in-transport-and-logistics","2019",false,"","Travel time prediction in transport and logistics","Purpose Many transport and logistics companies nowadays use raw vehicle GPS data for travel time prediction. However, they face difficult challenges in terms of the costs of information storage, as well as the quality of the prediction. This paper aims to systematically investigate various meta-data (features) that require significantly less storage space but provide sufficient information for high-quality travel time predictions. Design\u002Fmethodology\u002Fapproach The paper systematically studied the combinatorial effects of features and different model fitting strategies with two popular decision tree ensemble methods for travel time prediction, namely, random forests and gradient boosting regression trees. First, the investigation was conducted using pseudo travel time data that were generated using a pseudo travel time sampling algorithm, which allows generating travel time data using different noise processes so that the prediction performance under different travel conditions and noise characteristics can be studied systematically. The results and findings were then further compared and evaluated through a real-life case. Findings The paper provides empirical insights and guidelines about how raw GPS data can be reduced into a small-sized feature vector for the purposes of vehicle travel time prediction. It suggests that, add travel time observations from the previous departure time intervals are beneficial to the prediction, particularly when there is no other types of real-time information (e.g. traffic flow, speed) are available. It was also found that modular model fitting does not improve the quality of the prediction in all experimental settings used in this paper. Research limitations\u002Fimplications The findings are primarily based on empirical studies on limited real-life data instances, and the results may lack generalisabilities. Therefore, the researchers are encouraged to test them further in more real-life data instances. Practical implications The paper includes implications and guidelines for the development of efficient GPS data storage and high-quality travel time prediction under different types of travel conditions. Originality\u002Fvalue This paper systematically studies the combinatorial feature effects for tree-ensemble-based travel time prediction approaches.",[11,12,13,14],"Li, Xia","Bai, Ruibin","Siebers, Peer‐Olaf","Wagner, Christian",[16,17,18,19],"0000-0003-1561-3590","0000-0003-1722-568X","0000-0002-0603-5904","0000-0002-6121-9722",2019,"https:\u002F\u002Fdoi.org\u002F10.1108\u002Fvjikms-11-2018-0102","W2983926835","VINE Journal of Information and Knowledge Management Systems",null,[26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41],"Computer science","Decision tree","Global Positioning System","Data mining","Ensemble learning","Random forest","Gradient boosting","Predictive modelling","Sampling (signal processing)","Travel time","Raw data","Real-time data","Quality (philosophy)","Machine learning","Engineering","Transport engineering",{"type":43,"children":44,"toc":52},"root",[45],{"type":46,"tag":47,"props":48,"children":49},"element","p",{},[50],{"type":51,"value":9},"text",{"title":7,"searchDepth":53,"depth":53,"links":54},2,[],"markdown","content:publications:2019:travel-time-prediction-in-transport-and-logistics.md","content","publications\u002F2019\u002Ftravel-time-prediction-in-transport-and-logistics.md","publications\u002F2019\u002Ftravel-time-prediction-in-transport-and-logistics","md","en",[63,76,81,92,99,107,113,122,129,135,140,150,157,166,172,184,193,202,208,216,221,229,235,243,247,257,264,272,277,285,291,299,305,313,318,324,334,342,348,356,361,369],{"_path":64,"title":65,"name":66,"role":67,"email":24,"image":68,"category":69,"interests":70,"order":53,"_id":75},"\u002Fmembers\u002Fstaff\u002Falain-chong","Vice President for Global Affairs and Partnerships · Professor of Information Systems and Digital Innovation","Alain Chong","Deputy Director of Lab","assets\u002F8.png","staff",[71,72,73,74],"信息系统与运作管理","计算机科学与运筹学","Information Systems and Operations Management","Computer Science and Operations Research","content:members:staff:alain-chong.md",{"_path":64,"title":77,"role":78,"interests":79,"_id":80},"全球事务与合作副校长 · 信息系统与数字创新教授","实验室副主任",[71,72],"content:members:staff:alain-chong.zh-CN.md",{"_path":82,"title":83,"name":84,"role":85,"email":24,"image":86,"category":69,"interests":87,"order":90,"_id":91},"\u002Fmembers\u002Fstaff\u002Fanthony-belloti","Professor","Anthony Belloti","Core Member","assets\u002F41.png",[88,89],"Machine Learning and Credit Risk Model","Model Risks",9,"content:members:staff:anthony-belloti.md",{"_path":82,"title":93,"role":94,"interests":95,"_id":98},"计算机科学系教授","核心成员",[96,97],"机器学习与信用风险模型","模型风险","content:members:staff:anthony-belloti.zh-CN.md",{"_path":100,"title":101,"name":101,"role":85,"email":24,"image":102,"category":69,"interests":103,"order":105,"_id":106},"\u002Fmembers\u002Fstaff\u002Fboon-giin-lee","Boon Giin Lee","assets\u002F31.jpg",[104],"Intelligent Sensor and Extended Reality",11,"content:members:staff:boon-giin-lee.md",{"_path":100,"title":108,"role":94,"interests":109,"_id":112},"人机交互实验室负责人 · 计算机科学系副教授",[110,111],"人机交互 HCI","智能传感与扩展现实技术","content:members:staff:boon-giin-lee.zh-CN.md",{"_path":114,"title":115,"name":115,"role":116,"email":24,"image":117,"category":69,"interests":118,"order":120,"_id":121},"\u002Fmembers\u002Fstaff\u002Fcong-cao","Cong Cao","Direction Leader","assets\u002FCC.png",[119],"Science and technology policy and institutional reform",7,"content:members:staff:cong-cao.md",{"_path":114,"title":123,"name":124,"role":125,"interests":126,"_id":128},"宁波诺丁汉大学商学院创新学教授","曹聪","方向带头人",[127],"科技政策与体制改革","content:members:staff:cong-cao.zh-CN.md",{"_path":130,"title":131,"name":131,"role":85,"email":24,"image":132,"category":69,"order":133,"_id":134},"\u002Fmembers\u002Fstaff\u002Fdave-towey","Dave Towey","assets\u002F32.jpg",8,"content:members:staff:dave-towey.md",{"_path":130,"title":136,"role":94,"interests":137,"_id":139},"计算机科学系教授 · 计算机科学系主任",[138],"计算机科学与语言学","content:members:staff:dave-towey.zh-CN.md",{"_path":141,"title":142,"name":142,"role":85,"email":24,"image":143,"category":69,"interests":144,"order":148,"_id":149},"\u002Fmembers\u002Fstaff\u002Ffazl-ullah-khan","Fazl Ullah Khan","assets\u002F44.png",[145,146,147],"Computer Network","Computer Architecture and Network Security","Software Engineering",12,"content:members:staff:fazl-ullah-khan.md",{"_path":141,"title":151,"role":94,"interests":152,"_id":156},"计算机科学系助理教授 · IEEE 高级会员",[153,154,155],"计算机网络","计算机和网络安全","软件工程","content:members:staff:fazl-ullah-khan.zh-CN.md",{"_path":158,"title":159,"name":160,"role":85,"email":24,"image":161,"category":69,"interests":162,"order":164,"_id":165},"\u002Fmembers\u002Fstaff\u002Fheng-yu","Associate Professor","Heng Yu","assets\u002FHENGYU.png",[163],"Embedded Systems Design",17,"content:members:staff:heng-yu.md",{"_path":158,"title":167,"name":168,"role":94,"interests":169,"_id":171},"计算机科学系副教授","于恒",[170],"嵌入式系统设计","content:members:staff:heng-yu.zh-CN.md",{"_path":173,"title":159,"name":174,"role":85,"email":24,"image":175,"category":69,"interests":176,"order":182,"_id":183},"\u002Fmembers\u002Fstaff\u002Fheshan-du","Heshan Du","assets\u002Fhesahndu.png",[177,178,179,180,181],"Logic, Knowledge Representation and Reasoning","Geographic Information Systems","Operations Research","Machine Learning","Reinforcement Learning",20,"content:members:staff:heshan-du.md",{"_path":173,"title":167,"name":185,"role":94,"interests":186,"_id":192},"杜何珊",[187,188,189,190,191],"逻辑与知识表示","地理信息系统","运筹学","机器学习","强化学习","content:members:staff:heshan-du.zh-CN.md",{"_path":194,"title":195,"name":196,"role":85,"email":24,"image":197,"category":69,"interests":198,"order":200,"_id":201},"\u002Fmembers\u002Fstaff\u002Fhuan-jin","Assistant Professor","Huan Jin","assets\u002Fhuanjin.png",[199,180],"Optimisation",21,"content:members:staff:huan-jin.md",{"_path":194,"title":203,"name":204,"role":94,"interests":205,"_id":207},"计算机科学系助理教授","靳欢",[206,190],"优化","content:members:staff:huan-jin.zh-CN.md",{"_path":209,"title":159,"name":210,"role":116,"email":24,"image":211,"category":69,"interests":212,"order":214,"_id":215},"\u002Fmembers\u002Fstaff\u002Fjianfeng-ren","Jianfeng Ren","assets\u002F42.jpg",[180,213],"Computer Vision",3,"content:members:staff:jianfeng-ren.md",{"_path":209,"title":167,"name":217,"role":125,"interests":218,"_id":220},"任剑锋",[190,219],"计算机视觉","content:members:staff:jianfeng-ren.zh-CN.md",{"_path":222,"title":223,"name":223,"role":116,"email":24,"image":224,"category":69,"interests":225,"order":227,"_id":228},"\u002Fmembers\u002Fstaff\u002Fjiawei-li","Jiawei Li","assets\u002F11.png",[226],"Computer Science and Artificial Intelligence",15,"content:members:staff:jiawei-li.md",{"_path":222,"title":230,"name":231,"role":125,"interests":232,"_id":234},"计算机科学系助理教授 · 英国诺丁汉大学博士后","李家炜",[233],"计算机与人工智能","content:members:staff:jiawei-li.zh-CN.md",{"_path":236,"title":159,"name":237,"role":85,"email":24,"image":238,"category":69,"interests":239,"order":241,"_id":242},"\u002Fmembers\u002Fstaff\u002Fmatthew-pike","Matthew Pike","assets\u002F43.jpg",[240],"Digitalised Learning",16,"content:members:staff:matthew-pike.md",{"_path":236,"title":167,"role":94,"interests":244,"_id":246},[245],"数字化学习","content:members:staff:matthew-pike.zh-CN.md",{"_path":248,"title":195,"name":249,"role":85,"email":24,"image":250,"category":69,"interests":251,"order":255,"_id":256},"\u002Fmembers\u002Fstaff\u002Fning-xue","Ning Xue","\u002Fimages\u002Fuon-logo.png",[252,253,254],"Artificial Intelligence","Computational Intelligence","Combinatorial Optimization",13,"content:members:staff:ning-xue.md",{"_path":248,"title":203,"name":258,"role":94,"interests":259,"_id":263},"薛宁",[260,261,262],"人工智能","计算智能","组合优化","content:members:staff:ning-xue.zh-CN.md",{"_path":265,"title":195,"name":266,"role":85,"email":24,"image":267,"category":69,"interests":268,"order":270,"_id":271},"\u002Fmembers\u002Fstaff\u002Fqian-zhang","Qian Zhang","assets\u002Fqz.png",[269,213,180],"Image Processing",14,"content:members:staff:qian-zhang.md",{"_path":265,"title":203,"name":273,"role":94,"interests":274,"_id":276},"张茜",[275,219,190],"图像处理","content:members:staff:qian-zhang.zh-CN.md",{"_path":278,"title":83,"name":279,"role":280,"email":24,"image":281,"category":69,"interests":282,"orcid":17,"order":283,"_id":284},"\u002Fmembers\u002Fstaff\u002Fruibin-bai","Ruibin Bai","Director of Lab","assets\u002F38.png",[74],1,"content:members:staff:ruibin-bai.md",{"_path":278,"title":286,"name":287,"role":288,"interests":289,"_id":290},"教授","白瑞斌","实验室主任",[72],"content:members:staff:ruibin-bai.zh-CN.md",{"_path":292,"title":293,"name":293,"role":116,"email":24,"image":294,"category":69,"interests":295,"order":297,"_id":298},"\u002Fmembers\u002Fstaff\u002Fsean-he","Sean He","assets\u002F39.png",[213,296,180],"Data Analytics",5,"content:members:staff:sean-he.md",{"_path":292,"title":300,"name":301,"role":125,"interests":302,"_id":304},"计算机科学系教授 · 国家级讲席学者","何祥健",[219,303,190],"数据分析","content:members:staff:sean-he.zh-CN.md",{"_path":306,"title":307,"name":307,"role":85,"email":24,"image":308,"category":69,"interests":309,"order":311,"_id":312},"\u002Fmembers\u002Fstaff\u002Ftianxiang-cui","Tianxiang Cui","assets\u002Ftianxiangcui.png",[253,310,180,181],"Operation Research",19,"content:members:staff:tianxiang-cui.md",{"_path":306,"title":151,"name":314,"role":94,"interests":315,"_id":317},"崔天翔",[261,316,190,191],"运筹研究","content:members:staff:tianxiang-cui.zh-CN.md",{"_path":319,"title":83,"name":320,"role":85,"email":24,"image":321,"category":69,"order":322,"_id":323},"\u002Fmembers\u002Fstaff\u002Fxiuping-hua","Xiuping Hua","assets\u002FxiupignHua.png",10,"content:members:staff:xiuping-hua.md",{"_path":319,"title":325,"name":326,"role":94,"interests":327,"_id":333},"金融、会计与经济系教授","华秀萍",[328,329,330,331,332,96],"资产定价","公司金融","衍生品","金融科技","创新金融和普惠金融","content:members:staff:xiuping-hua.zh-CN.md",{"_path":335,"title":83,"name":336,"role":85,"email":24,"image":337,"category":69,"interests":338,"order":340,"_id":341},"\u002Fmembers\u002Fstaff\u002Fying-weng","Ying Weng","assets\u002Fyingweng.png",[213,269,339],"IoT",4,"content:members:staff:ying-weng.md",{"_path":335,"title":93,"name":343,"role":94,"interests":344,"_id":347},"翁莹",[219,275,345,346],"物联网 IoT","无线网络安全与服务质量","content:members:staff:ying-weng.zh-CN.md",{"_path":349,"title":195,"name":350,"role":85,"email":24,"image":351,"category":69,"interests":352,"order":354,"_id":355},"\u002Fmembers\u002Fstaff\u002Fyuan-yao","Yuan Yao","assets\u002Fyuanyao.png",[353],"Autonomous Agents and Multi-Agent Systems",18,"content:members:staff:yuan-yao.md",{"_path":349,"title":203,"name":357,"role":94,"interests":358,"_id":360},"姚远",[359],"自主智能体与多智能体系统","content:members:staff:yuan-yao.zh-CN.md",{"_path":362,"title":195,"name":363,"role":116,"email":24,"image":364,"category":69,"interests":365,"order":367,"_id":368},"\u002Fmembers\u002Fstaff\u002Fzheng-lu","Zheng Lu","assets\u002F13.png",[366],"Computer Science",6,"content:members:staff:zheng-lu.md",{"_path":362,"title":203,"name":370,"role":125,"interests":371,"_id":373},"卢正",[372],"计算机科学","content:members:staff:zheng-lu.zh-CN.md",1782640034036]