What if technology could predict traffic patterns the same way meteorologists predict the weather? That is a question graduate student, Hao Chen, has answered through his research under mentor Hesham Rakha, Director of the Center for Sustainable Mobility within the Virginia Tech Transportation Institute and College of Engineering.
At the 2013 Intelligent Transportation Systems World Congress in Japan, Chen won the Best Scientific Paper Award for this research. In his paper, he was not only able to show that this is possible, but also developed a very accurate algorithm for practical applications.
To develop this algorithm Chen used temporal and spatial information to match analogous traffic patterns with real-time and historical traffic data.
Dr. Rakha indicated, “the research that Hao conducted is cutting edge because it not only predicts what will happen on average, but also, the likelihood of certain events. For example the likelihood that your trip will be longer today.” In addition, Dr. Rakha mentioned that “another unique aspect of Hao’s research is the forecast period. Specifically, Hao is predicting traffic conditions up to four hours into the future, something that is far longer than what is reported in the literature (less than an hour). Clearly the longer you predict into the future the more complicated the problem is.”
With the advancement of technology among vehicles and infrastructure, the ability to collect data associated with real time vehicular behavior is becoming a reality. Connected vehicle technology will allow vehicles and roadways to communicate instantaneous information with each other; for example information about vehicle speed, vehicle location, number of vehicles on roadways, changing roadway conditions (such as ice or fog) etc. can become connected or shared.
Therefore, once data from present conditions and historical traffic patterns are measured, the implementation of Chen’s algorithm can predict traffic the same way weather is predicted. Much like a cloud’s anticipated route can be seen through radar, traffic patterns of the past can inform future ones. They can even be represented through a colorful map, similar to the radar map on weather forecast.
This historical traffic data set of roadways in the United States is stored by a company called INRIX. Chen utilized this information to test the accuracy of his algorithm. He looked back at high-traffic scenarios along interstates I-64 and I-264 in Virginia from June and July of 2010 to predict the travel times of the same route on August of 2010. The information of the predicted travel time was updated every five minutes and involved various traffic scenarios. Using his algorithm Chen was able to predict travel time with 96 percent accuracy two to four hours in the future.
Current systems used by most traffic management centers employ only historical data, making them less accurate. While, Chen’s model has the potential to improve accuracy, because it adapts as the environment adapts; as time lapses it increases the amount of historical data collected continually improving. For Chen’s study only two months of traffic data were used, in the future, a growing data set could further narrow the accuracy gap from 96 to 100 percent.
Chen’s algorithm may one day be used to assist drivers in planning their day. This technology has the potential to save drivers a lot of time and money.
The Virginia Tech Transportation Institute conducts research to save lives, time, money, and protect the environment. One of the seven university level research institutes created by Virginia Tech to answer national challenges, the Virginia Tech Transportation Institute is continually advancing transportation through innovation and has impacted public policy on the national and international level.