Recent Work

  1. Time-Headway Distributions--Traffic flow modeling is one of the fundamental keys to solving a traffic engineering problem. Among many parameters, time headway is frequently used to model traffic flow characteristics. A statistical analysis of time headways is immensely important to both theoretical traffic modeling and simulation-based traffic modeling. Basically, it allows researchers to describe an inherently random pattern of traffic flows. Past studies have mainly focused on the time headways of vehicles on highways, freeways, and arterials. However, studies of time headways on urban expressways are rather limited and still need further investigation. In this work, we investigate and characterize the time-headway distributions of vehicles traveling on an urban expressway in Bangkok, Thailand. Particularly, the exponential distribution, the lognormal distribution, and the generalized extreme value (GEV) distribution are used to model the time headways. It is found that the GEV distribution is most effective in modeling time headways. In fact, the GEV distribution can describe more than 90% of the empirical distributions on most lanes and sections of the expressway. 

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  1. Mobile Traffic Sensing--Smartphones are a great choice for traffic sensing devices as they are now equipped with a variety of sensors such as global positioning system (GPS) receiver, accelerometer, gyroscope, camera, and microphone. These sensors can be exploited to collect traffic data. Although there are many types of sensors available for traffic sensing, past studies have mainly focused on a GPS receiver. However, a GPS receiver consumes a lot of power, and hence it can significantly shorten the battery life. In this work, we explore a possibility of using other types of sensors on a smartphone for traffic sensing. Particularly, we investigate whether it is possible and how accurate it is to estimate the average speed of a vehicle from the data sensed by an accelerometer. Two estimation methods are introduced and their accuracy are evaluated.


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Sooksan Panichpapiboon, Ph.D.

Associate Professor

Faculty of Information Technology

King Mongkut’s Institute of Technology Ladkrabang (KMITL)

About Me

I am an Associate Professor (early promoted) in the Faculty of Information Technology (FIT), King Mongkut’s Institute of Technology Ladkrabang (KMITL), Bangkok Thailand. I received the B.S., M.S., and Ph.D. degrees from Carnegie Mellon University, Pittsburgh, PA, USA in 2000, 2002, and 2006, respectively, all in electrical and computer engineering.

Research Interests

  1. Intelligent Transportation Systems (ITS)

  2. Vehicular Networks

  3. Ad Hoc Wireless Networks

  4. Performance Modeling

Paris, France 2009

Last update: 30-Jun-2015

Recent News

  1. New Book on Self-Organizing Traffic Information Systems

Dr. Panichpapiboon has authored a new book, entitled “Self-Organizing Traffic Information Systems: Challenges and Design Issues.”


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  1. IEEE Top Accessed Article (Oct 2012)

Dr. Panichpapiboon’s article, entitled “Effects of Intervehicle Spacing Distributions on Connectivity of VANET: A Case Study from Measured Highway Traffic,” was rank in the 3rd place on the top 25 most accessed articles of IEEE Communications Magazine in the month of October 2012.


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My Bibliometric

  1. Google Scholar Citations