Artificial Intelligence in Transportation

With rapid advances in modern technology, artificial intelligence has become a fast- growing area in the transportation research field. The massive data collected from advanced sensing technologies can assist model training for artificial intelligence and hence improve safety, equity and sustainability for transportation systems. The proposed session will introduce to the audience some recent advances in artificial intelligence methodologies in transportation research. The method development is highly motivated by the real problems raised by the automotive industry and transportation-related government agencies. The invited presentations are related to a few broader topics in artificial intelligence in transportation research. Dr. Naveen Eluru focuses on analyzing the decision process using real-world data. He develops econometric models to provide recommendations to policymakers for improving the transportation system. Dr. Wenlong Jin explores potential application of large language models in transportation systems trained by large-scale transportation datasets. Dr. Gaurav Pandey will present an emerging AI method to create accurate and visually plausible representations of the environment using LIDAR and camera data for 3D mapping and visual localization. Dr. Arpan Kusari develops a simulated physical process for generating broken lens as a class of physics- based adversarial samples. The objective is to provide a robust physics-based process for generating adversarial samples for safety in object detection. By appropriately leveraging the transportation data, these novel methods coupled with sound artificial intelligence methodologies provide great potentials for improvements in areas such as transportation policy making, sensing system design, vehicle relocation, and autonomous driving safety. This session will make a great opportunity to learn more about such advances in artificial intelligence in transportation research and initiate discussions for practitioners working in the transportation areas and/or with applied interests in both academia and industry.