Environment Beijing TrafficRS

Published on September 29th, 2013 | by Apuntes LJ


Phone Data Helps Pinpoint Source of Traffic Congestion

Abstract of a Research Study

About the Researcher
Gonzalez Marta C. González is a professor in the Department of Civil and Environmental Engineering at the Massachusetts Institute of Technology (MIT). A native of Venezuela, professor González received her undergraduate degree from the Universidad Simón Bolívar, and a Masters degree from Universidad Central in Caracas. She later obtained a PhD in Physics from Stuttgart Univarsität in Germany. Her current research at MIT explores human mobility patterns using mobile phone communication, propagation of mobile phone viruses and urban transportation models. Her work also addresses the analysis of networks’ organization in relation to their attributes in social systems and spreading dynamics.


Traffic congestion in urban areas around the world has become a problem of enormous proportions, increasing air pollution and wasting time, money and fuel. One way to mitigate congestion is through use of travel alternatives such as public transportation, carpooling and flex time. But decisions about how to apply such alternatives effectively are hampered by poor data about the origin and destination of travelers. Most origin-destination matrices used in traffic studies are based on survey data or travel diaries, methodologies that are prone to error because of long time lags between gathering and releasing data and a reliance on self-reporting. Fortunately, large amounts of data from cellphone and other mobile devices now exist. The question is how to use these data to extract information that will be useful to policymakers and traffic managers.


Professor Marta González and former MIT postdoc Pu Wang designed a novel study that uses cellphone data to trace drivers’ origins and destinations, and classifies roads based not just on their centrality within the road network, but on the diversity of origins of drivers accessing those roads. Using three weeks of cellphone data, which yielded information about anonymous drivers’ routes and the estimated traffic volume and speed on those routes, they inferred a driver’s home neighborhood from the regularity of the route traveled and from the locations of cell towers that handled calls made between 9 p.m. and 6 a.m. They combined this with information about population densities and the location and capacity of roads in the networks of two metropolitan areas — Boston and San Francisco — to determine which neighborhoods are the largest sources of drivers on each road segment, and which roads these drivers use to connect from home to highways and other major roadways.


To validate the study’s methodology, Professor Alexandre Bayen and graduate student Timothy Hunter of the University of California at Berkeley compared González and Wang’s estimations of travel time based on cellphone data with their own data obtained from GPS sensors in taxis in the San Francisco area. Using GPS data, Bayen and Hunter computed taxis’ speed based on travel time from one location to another; from that speed of travel, they then determined congestion levels. Their findings agreed with those of González and Wang.


The study showed that canceling or delaying the trips of 1 percent of all drivers across a road network would reduce delays caused by congestion by only about 3 percent. But canceling the trips of 1 percent of drivers from carefully selected neighborhoods would reduce the extra travel time for all other drivers in a metropolitan area by as much as 18 percent. In the Boston area, canceling 1 percent of trips by select drivers in the municipalities of Everett, Marlborough, Lawrence, Lowell and Waltham would cut all drivers’ additional commuting time caused by traffic congestion by 18 percent. In the San Francisco area, canceling trips by drivers from Dublin, Hayward, San Jose, San Rafael and parts of San Ramon would cut 14 percent from the travel time of other drivers. These percentages are averages based on a one-hour commute with additional minutes caused by congestion. Drivers stuck in the roads with worst congestion would see the greatest percentage of time savings, because the selective strategy can more efficiently decrease the traffic flows in congested roads.


This is the first large-scale traffic study to track travel using anonymous cellphone data rather than surveys or travel diaries, and to classify roads according to the sources of drivers. The new methodology makes it possible to accurately characterize traffic on a road network using only three types of data: population density, topological information about the road network and cellphone data. Consequently, it can be applied to almost any urban area, even in cities in the developing world where travel surveys don’t exist, to help control congestion and create road networks that fit a population’s needs.


The study was published in the Dec. 20 issue of Nature Publishing’s Scientific Reports. Katja Schechtner of the Austrian Institute of Technology is a co-author with González, Wang, Bayen and Hunter. González and Wang, who is a professor at Central South University in China, are now applying their methodology to studies of road use in five other countries.


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