Unjamming the truth about the hidden impacts of traffic

Picture of Saif Kakakhel

Researchers have been utilizing location data to produce actionable intelligence that allows governments to save and improve the quality of citizens’ lives. Mobility-based studies continue to gain traction in academia and are being leveraged to unearth the hidden effects of various urban issues. One such problem is traffic congestion.  
As the size of the global population increases, so does vehicle ownership. As a result, traffic congestion is only going to get worse with time. Policymakers must have a thorough understanding of the true impact of traffic jams so that they can undertake suitable interventions to address them.  
Minoru Higa, a scholar from Simon Fraser – one of Canada’s leading research universities – used Quadrant’s mobile location data to determine if traffic congestion influenced the time people spent at work. This is a unique research focus that has not been explored in existing studies. Here, we discuss how they utilized our data alongside other third-party data to answer this question. 
The researcher chose to conduct his analysis on Mexico City because it is one of the most congested cities in the world (even more so than Mumbai, New Delhi, and New York). Additionally, Mexico City is ideal for this study since it is witnessing growing population and vehicle ownership rates – patterns that are common across most cities in the developing world.  
The timeframe of the analysis is almost the entirety of 2019. Data is analyzed for weekdays across the year (except for the first week of July and the last week of December because these are holiday periods). Moreover, the analysis specifically focuses on the impact of traffic congestion on office and factory workers.  

In the past, academics did not have a concrete means of exploring the relationship between traffic congestion and work-time allocation. That is no longer the case. High-quality, raw location data gives researchers information with the accuracy and granularity required to do this. Anonymized human mobility data sheds light on where people are, where they are commuting to, and how long they spend there – which is why it is the only reliable way to assess time spent at work.  
To account for potential bias in the mobile location dataset, the researcher collected self-reported working hours from Mexico’s National Occupation and Employment Survey (ENEO). It is important to note that while findings from survey data can be skewed, those from location data cannot.  
The figure below showcases the distribution of daily hours worked according to both sources. The results prove that mobility data is a precise way to gauge work-time allocation. The location data was complemented with geo-coded commercial information to identify workplaces and census data to distinguish homes.

Fig 3 SF blog
Description: The figure shows the number of hours people reported working each day, based on a survey called ENOE (shown in yellow), and the number of hours they spent at their workplace according to mobile location data (shown as a dashed line).
Traffic congestion was measured by obtaining GPS data from vehicles. The author was able to use this data to assess daily fluctuation in traffic levels, as well as congestion at the district level - as illustrated in the figures below.   
Fig 1 SF Blog

Description: The figure above illustrates the distribution of traffic congestion per hour. Both morning (6-8AM) and afternoon (5-8PM) rush hours are highlighted in yellow. 
Fig 2 SF blog

Description: The figure above is a map of Mexico City that represents the variation in average annual traffic congestion (by district) in 2019. Darker areas represent more congested districts. 

The research worker also incorporated other variables into his analysis to account for alternative factors that could impact the study. One of these variables was information on road accidents – events that influence congestion levels.  

The author found that people spend more time at work to avoid the afternoon rush hours. On average, twice the traffic congestion increases time at work by 1 hour. The location intelligence he generated further demonstrated that employees are not able to adjust their work-time allocation to compensate for spending more time at offices or factories. Building on these findings, he drew on self-reported income data and discovered something even more concerning: although people were working longer hours to avoid traffic congestion, the additional labor did not translate into higher wages.  

These are the hidden impacts of traffic congestion that go unnoticed. Congestion deals workers a two-fold blow: it robs them of precious time that could be spent on leisure activities and compels them to work longer without additional pay.  


It is imperative for governments to take concerted action to tackle problems that impact the quality of their citizens’ lives. Using Quadrant’s industry-leading location data, Minoru Higa has shown that traffic congestion has dire consequences beyond air pollution and inconveniencing workers. Although additional research is needed to investigate this relationship across all modes of transport and other sectors of the economy, the findings in this study alone underscore the need for immediate solutions to mitigate congestion. The best remedy to this problem is to improve the accessibility and effectiveness of public transit networks – feats that governments are already achieving with our high-quality human mobility data.  

The figures in this blog post are from the research paper, which can be found here.

Interested in leveraging high-quality raw location data to enhance your research? Fill out the form below to speak with one of our data consultants! 



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