Deep learning-based modeling of human trajectory uncertainty in urban environments

This chapter introduces a deep learning-based framework to model the uncertainty of human trajectories within complex urban environments, where traditional methods often fail due to static assumptions and overly simplistic error models. We propose leveraging advanced neural architectures, notably long short-term memory (LSTM) and bidirectional LSTM (Bi-LSTM) networks, to more accurately capture the spatial and temporal variability of indoor and outdoor human movements. By integrating diverse features-such as stride length, heading angles, and global navigation satellite system-derived data-our approach dynamically adapts to evolving conditions, offering significantly improved uncertainty predictions. Through empirical validation using real-world datasets, we demonstrate that our Bi-LSTM-based model outperforms conventional methods, achieving higher coverage ratios and lower mean absolute errors. These results translate to more reliable uncertainty estimates, essential for applications ranging from urban planning to improved navigation services in smart cities. Beyond the immediate findings, this chapter envisions future directions, including seamless indoor-outdoor localization, richer feature incorporation, and the use of state-of-the-art transformer-based models. In doing so, we set the stage for more adaptive and data-driven uncertainty modeling frameworks that will be critical as urban environments continue to grow in complexity and dynamism.