Data-Driven Complex Travel Behavior Modeling Using Didi Chuxing Data

We are sorry, this position has been filled.

Uber, Didi (Uber in China) and other Transportation Network Companies (TNCs) are growing rapidly in recent years. This project aims to propose a data-driven reinforcement learning framework to understand how idle drivers search for passengers.
The student involved in this project will assist Ph.D students to visualize and analyze drivers’ travel patterns. Students with good computer and coding skills are preferred. Skill requirements are:
1. using a suitable coding tool (such as Python, MATLAB) to extract data in required format, process and analyze data;
2. using a visualization tool (such as Python, MATLAB, or Processing) to plot patterns;
3. generating figures, graphs, tables, or statistical models to present results.

Lab: DiTecT

Direct Supervisor: Sharon Di

Position Dates: 5/01/2018 - 8/31/2018

Hours per Week: 10

Paid Position: No

Credit: Yes

Positions Available: 1

Qualifications: Computer programming and coding

Eligibility: Freshman, Sophomore, Junior, Senior, Master's; SEAS only

Professor Sharon Di, xd2187@columbia.edu, Mudd 630, 2128530435