Unmanned Aerial Vehicles (UAVs) have immense potential to enhance Intelligent Transport Systems (ITS) by aiding in real-time traffic monitoring, emergency response, and infrastructure inspection, leading to rich data collection, lower response times, and efficient urban mobility management. Machine learning (ML) is a crucial component in UAV-assisted ITS as it processes UAV-captured data in both the perception layer and decision layers of intelligent components for vehicle/pedestrian detection, trajectory optimization, and resource allocation. Importantly, the integration of UAVs and cutting-edge deep learning (DL) techniques is fostering an exciting synergy, equipping UAVs with unparalleled intelligence and autonomy, particularly, for the perception layer of UAVs. Despite these enhancements, their usefulness for detection and traffic extraction tasks remains largely unexplored. The contributions of this paper are divided into two main aspects: (1) UAVs in different ITS application scenarios that are empowered by ML technologies are reviewed. (2) A thorough survey aiming to explore a quantitative understanding of widely used DL models via a series of experiments and comparisons is presented. Four DL models, namely Convolution Neural Network (CNN), regions with CNN (R-CNN), Faster R-CNN, and You Only Look Once (YOLO)), in combination with different backbones, are designed and employed on five aerial datasets. Finally, we present a discussion of the remaining challenges and future works.

Operational disturbances within 30 min, namely, short-term operational disturbances (STODs), occur frequently during the daily operation of an urban rail transit (URT) system. Therefore, there is an urgent need to assess a network’s ability to respond to STODs to improve its operational level. The resilience of a URT network jointly considering turn-back operations, the occurrence time of STODs, and passenger travel experience is addressed. By considering passenger travel alternatives under STODs, we assessed the resilience of a network by utilizing the ratio of the average loss of a time-dependent performance indicator, which is referred to as the operational service level indicator in this paper. A simulation-based resilience assessment flowchart is also proposed. Numerical experiments conducted on the Chengdu subway network indicate that this network is more resilient to successive station failure than to simultaneous failure. Guaranteeing the normal operation of transfer stations is vital for enhancing the resilience of a network. The metric proposed in this paper is more practical for assessing resilience than the two commonly used metrics (performance loss and disturbance duration). Additionally, the experimental results highlight that turn-back operations at the stations have a significant impact on the resilience of a URT network. The sensitivity analysis of the parameters indicates that ensuring the normal operation of links connected by critical stations, preventing large-scale station failure, improving passenger tolerance to disturbance durations, and restoring the connectivity of disrupted links rapidly have practical significance for enhancing the resilience of a network.