August 18, 2019
Conference Paper

Graph Analytics and Optimization Methods for Insights from the Uber Movement Data

Abstract

In this work we leverage the Uber movement dataset for the Los Angeles (LA) area where partial TAZ to TAZ (Traffic Analysis Zone) trip time data is available. We first create a TAZ-TAZ network based on nearest neighbors and propose a model that allows us to complete the $(O-D)$ (Origin-Destination) travel time matrix, using optimization methods such as non-negative least squares. We apply these algorithms for several communities in the TAZ-TAZ network and present insights in the form of completed $(O-D)$ matrices and associated temporal trends. We qualify the error performance and scalability of our flows. We conclude by pointing out the directions in our ongoing work to improve the quality and scale of travel time estimation.

Revised: November 19, 2019 | Published: August 18, 2019

Citation

Visweswara Sathanur A., V.C. Amatya, M.H. Khan, R.J. Rallo Moya, and K.L. Maass. 2019. Graph Analytics and Optimization Methods for Insights from the Uber Movement Data. In Proceedings of the 2nd ACM/EIGSCC Symposium on Smart Cities and Communities (SCC 2019), September 10-12, 2019, Portland, OR, Article No. 2. New York, New York:ACM. PNNL-SA-146172. doi:10.1145/3357492.3358625