Big Data Brings Cleaner Air and Better Transportation to Mexico
Guest written by Sergio Castellanos
Admire The Sites … While Sitting In Traffic
For those who have visited Mexico City, you know the beauty of its architecture, cuisine, people and places. After all, it was ranked No. 1 of “52 places to go in 2016.”
Traffic congestion, however, is not one of the things to admire in this sprawling, Latin city. In fact, Mexico City is also ranked No. 1 on the worldwide traffic index, with drivers spending an average of 227 extra hours on the road each year, stuck in traffic. Given the nature of the predominantly combustion-based vehicles on the road, this traffic translates into millions of tons of greenhouse gas emissions that exacerbate climate change and degrade urban air quality.
During 2016, more than 20 million liters of fuel were used daily for transportation.1 These emissions exposed Mexico City’s population to more than twice the recommended levels of pollution for ozone and fine particulate matter (PM2.5).2 Estimates show that cleaning Mexico City’s air, and meeting the defined PM2.5 and ozone exposure health standards, could avoid more than 10,000 deaths annually due to poor air quality.2
Electrifying the transportation sector has gained recent attention given its potential to significantly reduce greenhouse gas (GHG) emissions. The Paris Declaration on Electro-Mobility and Climate Change and Call to Action is an example. Backed by several U.N. agencies, the International Energy Agency and several car manufactures, the document calls for at least a 20% electrification of all transport by 2030. Implementing policies to electrify vehicle fleets, however, requires tailored solutions that effectively address issues within a given city or region.
Government and Academia Meet
Instituto Nacional de Ecología y Cambio Climático (INECC, Mexico’s National Institute for Ecology and Climate Change) had been on a quest to develop climate-mitigation actions tailored to Mexico’s various cities and their unique needs for the past few years.
This is where Claudia Octaviano comes in. Claudia and I met when we were both doing our Ph.Ds. at MIT. Claudia was in the Engineering System Division (ESD) working on electricity storage and renewable energy models, and I was in the Mechanical Engineering department focusing on characterization methods for silicon-based solar cells. We became very good friends through activities organized by the MIT Mexican Students Association—a student-run club we were both members of.
Smog hanging over Mexico City. Photo Credit: Santiago Arau
After Claudia finished her Ph.D. in 2015, she returned to Mexico to become the head of the Climate Change Mitigation Division at INECC, coordinating Mexico’s national scientific and technology research agenda for low-carbon growth and GHG mitigation policy. At INECC, she had been conducting online searches for sources of traffic data, knowing that a detailed understanding of the urban vehicle transportation challenge would be required in order to develop solutions for any one of Mexico’s cities.
While searching for data sources online, she stumbled upon the Data for Climate Action (D4CA) Challenge—an unprecedented open innovation challenge aiming to connect researchers to novel data sources for the development of transformational climate action solutions. The Challenge had gathered industry stakeholders with data that fit exactly what Claudia was looking for to develop and strengthen INECCs policymaking.
Claudia had recently invited me to a talk she was giving at the University of California Berkeley, which is where I currently direct the Berkeley-Mexico Energy & Climate Change Initiative at the Berkeley Energy & Climate Institute. Shortly after her visit to Berkeley, we touched base again and she proposed we work together on D4CA. She felt my expertise in data-driven approaches to addressing energy and environmental research questions would greatly benefit the work. Prof. Dan Kammen, my research colleague and advisor at Berkeley, was also invited aboard, and so our D4CA Challenge journey began.
Waze Data Makes Less Pollution Possible
As the most congested city in the world, and a major problem for the health of the Mexican people, we decided to focus on the Mexico City megalopolis for our study. One of the datasets donated to the Challenge had come from Waze, who was granting access to their global, real-time feed of road conditions. This data was a crucial enabler, and Waze’s participation in the D4CA Challenge proved to be invaluable.
Claudia, Dan, and I pulled together a team of dedicated researchers to help leverage the tens of millions of data entries per month in Waze’s massive feed:
- From UC Berkeley: Apollo Jain, Pedro Sánchez and Hector Rincón had previously collaborated with Dan and I on a project related to Mexico’s renewable energy landscape, and they decided to engage in the Challenge, too.
- In parallel: Alex Gao, and Alan Xu responded to a call for applicants, and after demonstrating great interest in electro-mobility studies were also invited aboard.
- From INECC: Fabiola Ramirez, Oscar Araiza, Itzchel Nieto, Adolfo Contreras and Ulises Ruiz joined the team with experience on solutions developed in Mexico to mitigate climate change, including vehicle emissions reduction.
With our team of talented data researchers in place, we were able to collaborate across boundaries.
We began by creating visualizations of Mexico City’s traffic patterns—both geospatially and over time. We then layered on the emissions estimates using a publicly available emission modeling tool called MOVES-Mexico. This tool allowed us to map emissions at the street level—mirroring the traffic data visualizations. Finally, our team was able to cross-reference traffic and emissions with population movement data to derive promising locations for electro-mobility infrastructure development.
Visualizing Transformational Public Policies
To quantify the effect of electrification on urban air quality, three different policy scenarios were modeled:
After exploring the various options for Mexico City electro-mobility, it was determined that electrification of the rapid bus system offered the most immediate benefit to both population movement and health.
Our team again leveraged the Waze data to identify and prioritize routes that were most favorable for bus electrification to reduce traffic jams and pollution while considering the existing electrical grid design. By merging the various data sets, a fuller picture of traffic, pollution and social behaviors allowed us to propose a viable rapid transit electrification model and EV charging infrastructure for potential intermodal transit planning.
The traffic flow patterns also allowed us to understand the generalized direction of drivers in Mexico City throughout the day. This temporal breakdown becomes relevant when policies become time-resolved, and incentives can be applied at different times of the day to encourage driving patterns that minimize the pollution impact.
Just the Beginning
Both the INECC and UC Berkeley hope to continue strengthening this government-academic collaboration and create real change in Mexico. In fact, discussions are being held around drafting a national Mexican electro-mobility agenda, where our work is being considered. The model we developed also carries the potential to be replicated across multiple cities.
The Data for Climate Action Challenge may have been the first large scale public-private collaboration for data philanthropy, but it is only the start of a data revolution for social good that will open opportunities to inform policy and solutions that will help us act swiftly on climate change and other pressing global issues.
- Sistema de Información Energética. Petróleos Mexicanos. Volúmen de ventas internas de Petrolíferos por entidad federativa. (Energy Information System. Oil products sales by state) http://sie.energia.gob.mx/movil.do?action=cuadro&cvecua=PMXE2C03
- INECC/SEMARNAT (2017) Programa de Gestión Federal para Mejorar la Calidad del Aire de la Megalópolis. (Program for Air Quality Management in Mexico City’s Metropolitan Area)
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