Smart urban transport and logistics: A business analytics perspective

• 成果类型: Article
• 署名作者: He, Long; Liu, Sheng; Shen, Zuo-Jun Max
• 署名单位: George Washington University; National University of Singapore; University of Toronto; University of California System; University of California Berkeley; University of Hong Kong
• 刊物名称: PRODUCTION AND OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1059-1478
• DOI: 10.1111/poms.13775
• 发表日期: 2022
• 页码: 3771-3787
• 关键词: Electric vehicles; MODEL; deployment; location; delivery; service; optimization; QUALITY; DESIGN; scale

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.