This project will develop and test a portable single-lane traffic counting device based on LIDAR sensor and artificial intelligence video technologies. Work in Stage 1 of the project will focus on designing, developing, and testing core functions of the proposed device. The first version of the prototype with the capability of collecting LIDAR and video images will be developed. A neural network based on LIDAR and video images will be developed to detect entry of any new vehicle. A second neural network will be developed to classify vehicle based on collected videos. A second version of the prototype device will then be developed incorporating the two neural networks developed in the above tasks. Work Stage 2 will involve testing and refinement of the device under various weather and traffic conditions. Data will be collected for various traffic conditions. At least 20 such data sets will be collected. The duration of each data collection will be from 24 to 48 hours. Conditions and factors that could affect the vehicle counting and classification will be identified and strategies to address those conditions and factors will be developed. In the final stage, the mechanical design for housing and mounting will be finalized and production version of the prototype system will be built. Work on user interface and reporting will be completed. The developed prototype system will be provided to state DOTs and contractors for evaluation and their feedback will be sought for further improvement and refinement of the system. The final report will provide all the relevant data, findings, and conclusions along with recommendations on how to use the developed device for monitoring single-lane traffic.