This talk presents two projects that build data-driven solutions for city operations planning and management.

The first part is devoted to the emerging food delivery operations. Working with a major food delivery service provider in China, we develop a data-driven optimization framework to minimize customers’ expected delivery delay. To capture the driver’s routing behaviors, we propose a machine learning approach that predicts travel time with covariate information. Combined with the travel time prediction, our optimization framework is robust and yields significant reductions in delay times.

In the second part, we present how the real-world bike trajectory data can be leveraged for bike lane network design.

We integrate the two objectives, coverage and continuity, into the bike lane planning model in view of the cyclists’ utility functions. Efficient model formulations and algorithms have been proposed to solve this large-scale planning problem.

Dr. Sheng Liu is an Assistant Professor of Operations Management and Statistics at the Rotman School of Management. His research interests lie in smart city operations, data analytics, and optimization. His research has been published in Management Science, Operations Research, Manufacturing & Service Operations Management, and INFORMS Journal on Computing.

He received a PhD in Operations Research from UC Berkeley in 2019, and a BSc in Industrial Engineering from Tsinghua University in 2014.

He has been working for leading tech companies such as Lyft and Amazon (as a Data/Research Scientist intern).

Presented by University of Toronto ITE Student Chapter, UT-ITE. Free. All are welcome.

If any specific accommodations are needed, please contact ite@utoronto.ca. Requests should be made as early as possible.

Join link: https://ca.bbcollab.com/guest/a6386cd7a2644ba9892ea076e8994bf8.