The fields of pedestrian tracking, autonomous driving, and urban mobility are experiencing rapid growth, driven by the need for improved safety, efficiency, and sustainability. A common theme among these areas is the development of more robust and accurate models for predicting human behavior and trajectory planning. Recent research in pedestrian tracking has focused on creating new datasets and benchmarks, such as the Indian Driving Pedestrian Dataset and the CrowdTrack dataset, which provide challenging and realistic scenarios for testing and evaluating pedestrian tracking algorithms. Notable papers in this area include the introduction of the Indian Driving Pedestrian Dataset, which shows a significant performance drop of up to 15% for state-of-the-art intention prediction methods, and the proposal of a novel neural network architecture for online human action detection during escorting. In autonomous driving, researchers are exploring new methods for constructing high-definition maps, including the use of multi-modal fusion techniques that combine data from cameras and LiDAR sensors. The development of more accurate motion forecasting models is also a key area of focus, with models informed by multiple vector map elements, including lane boundaries and road edges. Noteworthy papers in this area include TopoStreamer, SafeMap, RTMap, and LANet, which demonstrate significant improvements in lane segment topology reasoning, HD map construction, and trajectory prediction. The field of trajectory prediction and analysis is also rapidly advancing, with a focus on improving the accuracy and consistency of predictions in complex scenarios. Researchers are exploring new methods for modeling trajectory data, including the use of multimodal retrieval frameworks and hierarchical self-supervised trajectory embedding frameworks. Notable papers in this area include Universal Retrieval for Multimodal Trajectory Modeling and STCLocker, which propose novel frameworks for trajectory modeling and deadlock avoidance testing. Additionally, the fields of topological data analysis and transportation research are witnessing significant developments, with a growing focus on adapting and extending existing frameworks to handle complex data structures and leveraging crowd-sourced data, advanced machine learning models, and high-resolution satellite imagery to improve our understanding of transportation phenomena. The field of urban mobility simulation is also undergoing significant advancements, with the integration of large language models, generative agents, and hybrid frameworks enabling more realistic and dynamic simulations. Notable developments include the use of recursive value-driven approaches, lifelong learning mechanisms, and transformer models to simulate traffic and agent behavior. Overall, these advancements have the potential to significantly improve the safety, efficiency, and sustainability of transportation systems, and are an important step towards the widespread adoption of autonomous driving technology and the development of more efficient and livable cities.