TransitReID: Transit OD Data Collection with Occlusion-Resistant Dynamic Passenger Re-Identification

Transit Origin-Destination (OD) data are essential for transit planning, particularly in route optimization and demand-responsive paratransit systems. Traditional methods, such as manual surveys, are costly and inefficient, while Bluetooth and WiFi-based approaches require passengers to carry specific devices, limiting data coverage. On the other hand, most transit vehicles are equipped with onboard cameras for surveillance, offering an opportunity to repurpose them for edge-based OD data collection through visual person re-identification (ReID). However, such approaches face significant challenges, including severe occlusion and viewpoint variations in transit environments, which greatly reduce matching accuracy and hinder their adoption. Moreover, designing effective algorithms that can operate efficiently on edge devices remains an open challenge. To address these challenges, we propose TransitReID, a novel framework for individual-level transit OD data collection. TransitReID consists of two key components: (1) An occlusion-robust ReID algorithm featuring a variational autoencoder guided region-attention mechanism that adaptively focuses on visible body regions through reconstruction loss-optimized weight allocation; and (2) a Hierarchical Storage and Dynamic Matching (HSDM) mechanism specifically designed for efficient and robust transit OD matching which balances storage, speed, and accuracy. Additionally, a multi-threaded design supports near real-time operation on edge devices, which also ensuring privacy protection. We also introduce a ReID dataset tailored for complex bus environments to address the lack of relevant training data. Experimental results demonstrate that TransitReID achieves state-of-the-art performance in ReID tasks, with an accuracy of approximately 90\% in bus route simulations.

Enhancing Disaster Resilience with UAV-Assisted Edge Computing: A Reinforcement Learning Approach to Managing Heterogeneous Edge Devices

Edge sensing and computing is rapidly becoming part of intelligent infrastructure architecture leading to operational reliance on such systems in disaster or emergency situations. In such scenarios there is a high chance of power supply failure due to power grid issues, and communication system issues due to base stations losing power or being damaged by the elements, e.g., flooding, wildfires etc. Mobile edge computing in the form of unmanned aerial vehicles (UAVs) has been proposed to provide computation offloading from these devices to conserve their battery, while the use of UAVs as relay network nodes has also been investigated previously. This paper considers the use of UAVs with further constraints on power and connectivity to prolong the life of the network while also ensuring that the data is received from the edge nodes in a timely manner. Reinforcement learning is used to investigate numerous scenarios of various levels of power and communication failure. This approach is able to identify the device most likely to fail in a given scenario, thus providing priority guidance for maintenance personnel. The evacuations of a rural town and urban downtown area are also simulated to demonstrate the effectiveness of the approach at extending the life of the most critical edge devices.

Traffic Co-Simulation Framework Empowered by Infrastructure Camera Sensing and Reinforcement Learning

Traffic simulations are commonly used to optimize traffic flow, with reinforcement learning (RL) showing promising potential for automated traffic signal control. Multi-agent reinforcement learning (MARL) is particularly effective for learning control strategies for traffic lights in a network using iterative simulations. However, existing methods often assume perfect vehicle detection, which overlooks real-world limitations related to infrastructure availability and sensor reliability. This study proposes a co-simulation framework integrating CARLA and SUMO, which combines high-fidelity 3D modeling with large-scale traffic flow simulation. Cameras mounted on traffic light poles within the CARLA environment use a YOLO-based computer vision system to detect and count vehicles, providing real-time traffic data as input for adaptive signal control in SUMO. MARL agents, trained with four different reward structures, leverage this visual feedback to optimize signal timings and improve network-wide traffic flow. Experiments in the test-bed demonstrate the effectiveness of the proposed MARL approach in enhancing traffic conditions using real-time camera-based detection. The framework also evaluates the robustness of MARL under faulty or sparse sensing and compares the performance of YOLOv5 and YOLOv8 for vehicle detection. Results show that while better accuracy improves performance, MARL agents can still achieve significant improvements with imperfect detection, demonstrating adaptability for real-world scenarios.

Deep Learning based Computer Vision Methods for Complex Traffic Environments Perception: A Review

Computer vision applications in intelligent transportation systems (ITS) and autonomous driving (AD) have gravitated towards deep neural network architectures in recent years. While performance seems to be improving on benchmark datasets, many real-world challenges are yet to be adequately considered in research. This paper conducted an extensive literature review on the applications of computer vision in ITS and AD, and discusses challenges related to data, models, and complex urban environments. The data challenges are associated with the collection and labeling of training data and its relevance to real world conditions, bias inherent in datasets, the high volume of data needed to be processed, and privacy concerns. Deep learning (DL) models are commonly too complex for real-time processing on embedded hardware, lack explainability and generalizability, and are hard to test in real-world settings. Complex urban traffic environments have irregular lighting and occlusions, and surveillance cameras can be mounted at a variety of angles, gather dirt, shake in the wind, while the traffic conditions are highly heterogeneous, with violation of rules and complex interactions in crowded scenarios. Some representative applications that suffer from these problems are traffic flow estimation, congestion detection, autonomous driving perception, vehicle interaction, and edge computing for practical deployment. The possible ways of dealing with the challenges are also explored while prioritizing practical deployment.