[Scientific Research Workshop Session 47] Transportation Management in the Age of Big Data

14:00, June 4, 2021~16:00, June 4, 2021

821, Siyuan East Building, Haidian District, Beijing (Tencent Conference ID: 181 748 551)

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Wang Ye

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Registration deadline: 2021-06-04 14:00
Cancellation time: 2021-06-04 14:00
Lecturer: Li Xiang
Organizer: School of Economics and Management
intended for: Undergraduate, Master of Science, Postgraduate, Master of Science [MBA, EMBA, Master of Audit (MAud), Master of Applied Statistics (MAS), Master of Engineering Management (MEM), International Master of Entrepreneurship and Innovation (EIV), Master of Accounting (MPAcc), Master of Asset Appraisal (MV), Master of Finance (MF), Master of Engineering (ME)] Postdoctoral faculty alumni
Expense description: Free Admission
Activity description
[Scientific Research Workshop 47]
Transportation management in the era of big data
Time: 14:00-16:00, June 4, 2021 (Friday)
Location: 821, Siyuan East Building (Tencent Conference ID: 181 748 551)
Briefing: Li Xiang, professor of School of Economics and Management, Beijing University of Chemical Technology, doctoral supervisor, director of Big Data Transportation and Logistics Laboratory. Research fields include transportation management, hazardous chemicals transportation management, big data optimization, etc. At present, he is the chief editor of Springer Journal of Data, Information and Management, the deputy chief editor/editorial board member of many SCI/SSCI journals such as Information Sciences, the editorial board member of Operations Research and Management, the editorial board member of Journal of Systems Engineering, the director of the Chinese Society of Management Science and Engineering, the director of the Chinese Association of Optimal Methods and Economic Mathematics, and the director of the Chinese Association of Operations Research.
Report summary: The lecture focuses on the big data and energy conservation management of urban rail transit, the big data and passenger flow management of ground public transport, the big data of shared transport and scheduling optimization, as well as several problems in the collaborative management of urban comprehensive transport and shared transport.
Welcome all teachers and students!

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