Nav: Home

Getting from here to there

March 02, 2016

Intelligent transportation systems enable people to make smart travel choices, whether it's selecting an alternate route to avoid a minor traffic backup or figuring out the safest evacuation path during a hurricane.

But massive amounts of data are challenging the ability of these systems to provide accurate, real-time information to users.

"We now have new data streams about traffic dynamics such as vehicle speed, the number of vehicles, the location of accidents, and so on, resulting in huge amounts of connected data," says Lena Mashayekhy, assistant professor of computer science at the University of Delaware.

A research team that includes Mashayekhy, along with other academic researchers and a senior technical leader from Ford Motor Company, has come up with a way to reduce that data so that it can be used in intelligent transportation systems (ITS) applications.

Their work has been published as a paper, "Hierarchical Time-Dependent Shortest Path Algorithms for Vehicle Routing Under ITS," in the February issue of IIE Transactions, and it also has been selected as a January 2016 featured article in Industrial Engineer magazine.

Known as HTGD (hierarchical time-dependent goal directed), the approach involves identifying similar "communities" in the traffic data and then finding the shortest route at the highest level, effectively reducing the search space by eliminating entire communities that would not be traversed by the optimal path.

"Our method strikes a good balance between efficiency, or search cost, and effectiveness, or path optimality," Mashayekhy says.

"We believe that the significant reduction in memory requirements of HTGD compared with those of other current methods makes it suitable to be incorporated into vehicle routing navigation systems. It will be especially valuable for determining which routes are available -- and which are not -- in routing emergency vehicles and organizing natural disaster evacuations."

Extensive experimental evaluations of the proposed approach on Detroit, New York, and San Francisco road networks have demonstrated the computational efficiency and accuracy of the proposed method.
About the journal

IIE Transactions, the Institute of Industrial Engineers' flagship research journal, is published monthly. It aims to foster exchange among researchers and practitioners by publishing papers that are grounded in science and mathematics and motivated by engineering applications. Industrial Engineer is the Institute of Industrial Engineers' monthly magazine.

About the research team

The paper was co-authored by Mark Nejad, Lena Mashayekhy, Ratna Babu Chinnam and Anthony Phillips.

Nejad is an assistant professor in the School of Industrial and Systems Engineering at the University of Oklahoma.

Mashayekhy is an assistant professor in the Department of Computer and Information Sciences at the University of Delaware.

Chinnam is a professor in the Department of Industrial and Systems Engineering at Wayne State University in Detroit.

Phillips is a senior technical leader in research and advanced engineering at Ford Motor Company.

University of Delaware

Related Data Articles:

Discrimination, lack of diversity, & societal risks of data mining highlighted in big data
A special issue of Big Data presents a series of insightful articles that focus on Big Data and Social and Technical Trade-Offs.
Journal AAS publishes first data description paper: Data collection and sharing
AAS published its first data description paper on June 8, 2017.
73 percent of academics say access to research data helps them in their work; 34 percent do not publish their data
Combining results from bibliometric analyses, a global sample of researcher opinions and case-study interviews, a new report reveals that although the benefits of open research data are well known, in practice, confusion remains within the researcher community around when and how to share research data.
Designing new materials from 'small' data
A Northwestern and Los Alamos team developed a novel workflow combining machine learning and density functional theory calculations to create design guidelines for new materials that exhibit useful electronic properties, such as ferroelectricity and piezoelectricity.
Big data for the universe
Astronomers at Lomonosov Moscow State University in cooperation with their French colleagues and with the help of citizen scientists have released 'The Reference Catalog of galaxy SEDs,' which contains value-added information about 800,000 galaxies.
What to do with the data?
Rapid advances in computing constantly translate into new technologies in our everyday lives.
Why keep the raw data?
The increasingly popular subject of raw diffraction data deposition is examined in a Topical Review in IUCrJ.
Infrastructure data for everyone
How much electricity flows through the grid? When and where?
Finding patterns in corrupted data
A new 'robust' statistical method from MIT enables efficient model fitting with corrupted, high-dimensional data.
Big data for little creatures
A multi-disciplinary team of researchers at UC Riverside has received $3 million from the National Science Foundation Research Traineeship program to prepare the next generation of scientists and engineers who will learn how to exploit the power of big data to understand insects.

Related Data Reading:

Best Science Podcasts 2019

We have hand picked the best science podcasts for 2019. Sit back and enjoy new science podcasts updated daily from your favorite science news services and scientists.
Now Playing: TED Radio Hour

Failure can feel lonely and final. But can we learn from failure, even reframe it, to feel more like a temporary setback? This hour, TED speakers on changing a crushing defeat into a stepping stone. Guests include entrepreneur Leticia Gasca, psychology professor Alison Ledgerwood, astronomer Phil Plait, former professional athlete Charly Haversat, and UPS training manager Jon Bowers.
Now Playing: Science for the People

#524 The Human Network
What does a network of humans look like and how does it work? How does information spread? How do decisions and opinions spread? What gets distorted as it moves through the network and why? This week we dig into the ins and outs of human networks with Matthew Jackson, Professor of Economics at Stanford University and author of the book "The Human Network: How Your Social Position Determines Your Power, Beliefs, and Behaviours".