A weighted travel time index based on data from Uber Movement

Renato S Vieira,Eduardo A Haddad

doi:10.1140/epjds/s13688-020-00241-y

Abstract

In this paper, we combine data from Uber Movement and from a representative household travel survey to constructs a weighted travel time index for the Metropolitan Region of São Paulo. The index is calculated based on the average travel time of Uber trips taken between each pair of traffic zone and in each hour between January 1st, 2016 to December 31, 2018. The index is weighted based on trips reported in a household travel survey that was designed to be statistically representative of all trips made in the city during a typical business day. We show that the index has a strong correlation with traditional measures of congestion, however, with a broader coverage of the road network. Finally, we use the index to run a multivariate ex-post analysis that estimates the effect of different events on traffic congestion in the city, including holidays, public transit strikes, road shutdowns, rain and major sport events.

Highlights

Traffic congestion is a major component of transport systems efficiency
We explore data from the Uber Movement (UM) website and combine it with a representative household travel survey for the Metropolitan Region of São Paulob (MRSP) to create a virtually cost-free traditionalc Travel Time Index (TTI) that estimates trip delays due to congestion experienced by residents of São Paulo in every hour throughout the last three years and in almost all neighborhoods of the city
We compare this index with a traditional congestion measure calculated by the government of São Paulo, and we show that while there is a strong correlation between the measurements, our index covers a broader set of roads and is more translated to actual travel time losses

Summary

Introduction

Traffic congestion is a major component of transport systems efficiency. With higher levels of congestion, more time is spent in traffic and less time is available for productive activities and for leisure [1]. Traditional methods for measuring congestion such as loop detectors are important tools for road segment monitoring and for traffic signal control. Their coverage is limited to the locations where they are placed, these are not ideal tools for tracking congestion with a highly granular temporospatial coverage [2]. Measuring congestion at the personal trip level requires other types of tools, such as probe vehicles or taxis. Such methods are costly and are not commonly available in developing country cities where traffic agencies face stricter financial constraints [3]

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Conclusion