2020 TRB Annual Meeting

I am going to be attending the Transportation Research Board (TRB)’s Annual Meeting in January 2020 at the Walter E. Washington Convention Center, in Washington, D.C. This year I have one accepted paper as the corresponding author. TRB’s Annual Meeting is one of the largest gatherings of transportation professionals and is expected to attract more than 13,000 participants from around the world.


Sahin, O., Cetin, M., Ustun, I. (2020). Empty Platform Semi-Trailer classification using side-fire LIDAR data for supporting Freight Analysis and Planning

Lectern Session:

  • Innovations in Data Collection, Analysis, and Fusion to Address Persistent Freight Data Gaps
  • Standing Committee on Freight Transportation Data (ABJ90)
  • Tuesday, 1-14-2020 – 1:30 PM - 3:15 PM
  • Convention Center, 144A


Empty truck trips constitute an important aspect of commodity-based freight planning and modelling. But this information is generally not available to State DOTs or Metropolitan Planning Organizations (MPOs) since detecting empty trips is a challenge with traditional vehicle sensors. In this study, we propose a method for detecting empty and loaded platform semi-trailers using data from a multi-array LIDAR sensor. From the LIDAR cloud points, 3D profiles of trucks can be generated, and these profiles allow extracting useful information (e.g. body type, empty and loaded platforms). Since only platform semi-trailers’ load is observable from their 3D profiles, we only consider open platform trailers which constitute 20% of the truck trailer population in the USA. This paper shows how point-cloud data from a 16-beam LIDAR sensor are processed to extract useful information and features to distinguish between empty and loaded platform semi-trailers versus all other major truck body types (e.g. dry van, container, tank, automobile transport, etc.). Several machine learning (ML) models, in particular, K-Nearest Neighbors (KNN), Multilayer Perceptron (MLP), Adaptive Boosting (AdaBoost.M2), and Support Vector Machines (SVM) are implemented on the field data collected on a freeway segment that includes over nine-thousand trucks. The results show that all major semi-trailers and empty platform semi-trailers can be distinguished with very high level of accuracies of 99% and 97% respectively.