We generalized Wilson’s classic gravity model to a multilayer one that jointly distributes passenger ODs as well as mobility service vehicle ODs. This was tested on Chicago Uber data and shown to distinguish between shared and solo rides. The work is meant for planning agencies working with limited, privacy-protected data from fleet-based mobility companies.

The work was funded by C2SMART (USDOT #69A3551747124) and NSF CMMI-1652735. Link to paper here.

Transportation network inference can be statistically costly, but if we can reverse engineer network congestion levels to fit route observations, that can produce a statistically cheaper approach to monitor traffic in cities. This method was retroactively tested assuming a network monitoring system based on taxi data from Wuhan as mobile sensors, showing that even increasing monitoring of just one OD pair’s routes to two OD pairs can improve the correlation coefficient between predicted and observed from 0.23 to 0.56. Should be of interest to city monitoring agencies and companies like Google, Uber, and DiDi.

Funding from NSF CMMI-1652735 with collaboration from Dr. Xintao Liu at Hong Kong Polytechnic University. The paper can be found here.