Street-level Travel-time Estimation via Aggregated Uber Data
release_67mimrnksveynjnoseoa6roxr4
by
Kelsey Maass, Arun V Sathanur, Arif Khan, Robert Rallo
2020
Abstract
Estimating temporal patterns in travel times along road segments in urban
settings is of central importance to traffic engineers and city planners. In
this work, we propose a methodology to leverage coarse-grained and aggregated
travel time data to estimate the street-level travel times of a given
metropolitan area. Our main focus is to estimate travel times along the
arterial road segments where relevant data are often unavailable. The central
idea of our approach is to leverage easy-to-obtain, aggregated data sets with
broad spatial coverage, such as the data published by Uber Movement, as the
fabric over which other expensive, fine-grained datasets, such as loop counter
and probe data, can be overlaid. Our proposed methodology uses a graph
representation of the road network and combines several techniques such as
graph-based routing, trip sampling, graph sparsification, and least-squares
optimization to estimate the street-level travel times. Using sampled trips and
weighted shortest-path routing, we iteratively solve constrained least-squares
problems to obtain the travel time estimates. We demonstrate our method on the
Los Angeles metropolitan-area street network, where aggregated travel time data
is available for trips between traffic analysis zones. Additionally, we present
techniques to scale our approach via a novel graph pseudo-sparsification
technique.
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