• RESEARCH
• News
• Research Efforts
• Academic Departments
• Research Partners
• How to Apply
• Contact Us

Award Received by Joshua Auld, CTS-IGERT alumnus.

read more...

Congratulations to James Biagioni, CTS Fellow and CS PhD candidate, winner of the Dean's Scholar award.

read more...

James Biagioni, CTS Fellow, receives "Best Presentation Award" at SenSys2011

read more...

Dr. Ouri Wolfson, Dr. Phillip Yu, and Leon Stenneth, CS student and CTS Associate, recently had a paper accepted to the 6th IEEE International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob 2010).

read more...

Computational Transportation Science

Click here to download the program brochure (.pdf format, Adobe Reader required)

In the near future, vehicles, travelers, and the infrastructure will collectively have billions of sensors that can communicate with each other. This environment will enable numerous novel applications and order of magnitude improvement in the performance of existing applications (see Figure 1). These applications take advantage of the information rich environment, and building them necessitates appropriate data models, communication protocols, software architectures, and interfaces. These must adapt to changing environments, have appropriate latency for time-critical applications, enable resource sharing, safeguard security and privacy, and support multiple levels of precision/accuracy.

The systems need to support real-time, continuous, and distributed/mobile operation of unprecedented scale and mobility. This in turn has fundamental implications for data management, communications, and sensor network research. Recent trends in sensor databases, spatio-temporal information, and mobile databases provide a starting point for the work. But extant systems cannot cope with the dynamism of transportation environments. E.g., there are still no modeling abstractions for integrating distinct views of the same scene (e.g., an intersection) or representing transportation systems at granularities that range from individual travelers to metropolitan-wide regions. Yet all these views and granularities are interrelated, since a single airbag deployment can signal an accident that may have congestion implications throughout a metropolitan area. So transportation data mining and analysis will require zoom-in/zoom-out capabilities through multiple levels of granularity and aggregation. But how then do these capabilities and their performance relate to interfaces between systems at different granularities?

High mobility has fundamental implications for information and stream data management, communications, and sensor network research. Existing data management paradigms originated in business data processing and then evolved to the Internet. Many of these paradigms are inappropriate for computational transportation. For example, existing state-of-the-art research assumes that data resides at a static site. How then should we process a query directed to the travelers dispersed along a bus route?

Recent trends focusing on sensor networks, spatio-temporal information, and mobile databases provide the starting point for our education and research program. However, this research is still in its infancy, as existing systems cannot cope with the dynamism and scale of transportation environments.

The Computational Transportation Scientists will develop the basis for the necessary technology components. We will develop a hierarchy of protocols, communications systems, data models, and interfaces that scale gracefully over individual travelers and the infrastructure; and algorithms for collecting the appropriate data, abstracting it, sharing it, and retrieving it as needed.