Understanding complex traffic dynamics, characterized by the interplay between vehicle automation technologies, human drivers, and diverse transportation modes, presents significant challenges for transportation research. The integration of autonomous vehicles with human-driven vehicles introduces new dynamics into traffic flow, necessitating a comprehensive understanding of their coexistence. Additionally, the heterogeneity of various vehicle classes further complicate traffic patterns. Advancements in traffic data collection, particularly camera-based trajectory data, provide a wealth of information for analyzing microscopic details and macroscopic trends in traffic behavior. However, challenges in modeling and estimation techniques, such as accuracy, robustness and scalability need to be addressed. Furthermore, data quality issues need to be tackled in a systematic manner. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">This dissertation addresses the overarching question of developing comprehensive and efficient methods to understand complex traffic dynamics. It solves a series of reconstruction problems, involving parameter estimation, state estimation, and data reconciliation. Parameter estimation focuses on inferring unknown parameters of microscopic traffic models from on-board vehicle sensor data. State estimation involves estimating current or past states of macroscopic traffic dynamics using fixed-location measurements. Data reconciliation processes incomplete and noisy video tracking data while adhering to dynamical constraints. The integration of these tools provides insights into complex traffic patterns at various scales.

During emergency response in high-speed railway operations, dispatchers and drivers currently rely on individual experience for decision making, lacking effective coordination between train timetable rescheduling and trajectory control. This results in a train timetable with insufficient precision, failing to provide a global train trajectory, and ultimately making it difficult to handle train delays caused by emergencies promptly. To address this practical problem, this article constructs a novel architecture and proposes a real-time algorithm for coordinated decision making for train timetable rescheduling and trajectory control (CDM-TTRTC). The simulation and application experiments show that compared to the noncoordinated method using manual experience, the proposed CDM-TTRTC algorithm can reduce the total train delay by 17.18% and total energy consumption by 5.82%. In addition, it can generate a train timetable and multitrain trajectory within 3.49 s, providing dispatchers and drivers with more reasonable and efficient decision support.

Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.

Traffic congestion is a problem that burdens urban cities daily. Despite the development of numerous research methodologies and technological solutions, the problem still persists. This article presents a system that combines traffic and demand management mechanisms to avoid the emergence of congestion by sustaining the occupancy of each road in the network up to a threshold, using a reservation scheme and keeping track of the future states of the network. Traffic management is responsible for navigating vehicles through congestion-free paths, while demand management is responsible for identifying the time when each vehicle will enter the network. Considering the size of actual urban networks and the number of vehicles utilizing the infrastructure, this is not an easy task. This work designs and validates a responsive and scalable real-life system for online traffic and demand management, while addressing the challenge of managing and processing large numbers of requests and data. The complexity and feasibility of the system are evaluated through microsimulations of real and artificial road networks. Its traffic efficiency is also evaluated by running extensive simulations of a real city, emulating realistic conditions in different scenarios.

Unmanned aerial vehicles (UAV) can capture multiview images of large-scale scenes, and then using aerial light field (ALF)-rendering technology, they can render high-quality, large-scale 3D scenes. However, the reconstruction of large-scale scenes poses challenges, such as rendering distortion and high memory consumption. In this article, we study the multiview image-capturing and novel view-rendering method of UAV sampling to address these issues. First, we present an ALF sampling model using the spectral analysis of light field and obtain an exact spectrum expression of the ALF. Through the spectral support of ALF, we determine the bandwidth and calculate the minimum required UAV sampling rate. Finally, we demonstrate that our sampling and rendering methods can improve the rendering quality of UAV 3D reconstruction and reduce the minimum sampling rate.