Smart urban transport and logistics: A business analytics perspective

被引：20
作者：
He, Long [1 ,2 ]
Liu, Sheng [3 ]
Shen, Zuo-Jun Max [4 ,5 ]
机构：
[1] George Washington Univ, Sch Business, Washington, DC 20052 USA
[2] Natl Univ Singapore, NUS Business Sch, Singapore, Singapore
[3] Univ Toronto, Rotman Sch Management, Toronto, ON, Canada
[4] Univ Calif Berkeley, Coll Engn, Berkeley, CA USA
[5] Univ Hong Kong, Fac Business & Econ, Fac Engn, Hong Kong, Peoples R China
来源：
PRODUCTION AND OPERATIONS MANAGEMENT | 2022年 / 31卷 / 10期
基金：
国家重点研发计划;
关键词：
logistics; predictive analytics; prescriptive analytics; smart cities; transportation; ELECTRIC VEHICLES; MODEL; DEPLOYMENT; LOCATION; DELIVERY; SERVICE; OPTIMIZATION; QUALITY; DESIGN; SCALE;
D O I：
10.1111/poms.13775
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
New technologies and innovative business models are leading to connected, shared, autonomous, and electric solutions for the tomorrow of urban transport and logistics (UTL). The efficiency and sustainability of these solutions are greatly empowered by the capability of understanding and utilizing the tremendous amount of data generated by passengers, drivers, and vehicles. In this study, we first review the innovative applications in UTL and several related research areas in the operations management (OM)/operations research (OR) literature. We then highlight the sources, types, and uses of data in different applications. We further elaborate on business analytics techniques and software developed to facilitate the planning and management of UTL systems. Finally, we conclude the paper by reflecting on the emerging trends and potential research directions in data-driven decision making for smart UTL.
引用
收藏
页码：3771 / 3787
页数：17
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