Space weather events, including solar flares, coronal mass ejections, and geomagnetic storms, have significant effects on various transportation systems. This review provides a comprehensive examination of the current understanding and future outlook of space weather effects on air, maritime, railway, and ground transportation. It explores the mechanisms through which space weather causes communication blackouts, satellite navigation failure, elevated cosmic radiation, and geomagnetically induced currents, leading to disruptions in transportation operations. Historical events are analyzed to underscore the diversity and severity of these impacts. Additionally, this review discusses the anticipated challenges posed by the upcoming solar maximum of Solar Cycle 25 and highlights the need for improved forecasting, mitigation strategies, and resilient infrastructure to safeguard transportation systems against space weather threats. By integrating findings from recent studies and historical data, this review aims to enhance the preparedness and response strategies of the transportation sector in the face of evolving space weather risks.

The performance of Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) integrated navigation can be severely degraded in urban canyons due to the non-line-of-sight (NLOS) signals and multipath effects. Therefore, to achieve a high-precision and robust integrated system, real-time fault detection and localization algorithms are needed to ensure integrity. Currently, the residual chi-square test is used for fault detection in the positioning domain, but it has poor sensitivity when faults disappear. Three-dimensional (3D) light detection and ranging (LiDAR) has good positioning performance in complex environments. First, a LiDAR aided real-time fault detection algorithm is proposed. A test statistic is constructed by the mean deviation of the matched targets, and a dynamic threshold is constructed by a sliding window. Second, to solve the problem that measurement noise is estimated by prior modeling with a certain error, a LiDAR aided real-time measurement noise estimation based on adaptive filter localization algorithm is proposed according to the position deviations of matched targets. Finally, the integrity of the integrated system is assessed. The error bound of integrated positioning is innovatively verified with real test data. We conduct two experiments with a vehicle going through a viaduct and a floor hole, which, represent mid and deep urban canyons, respectively. The experimental results show that in terms of fault detection, the fault could be detected in mid urban canyons and the response time of fault disappearance is reduced by 70.24% in deep urban canyons. Thus, the poor sensitivity of the residual chi-square test for fault disappearance is improved. In terms of localization, the proposed algorithm is compared with the optimal fading factor adaptive filter (OFFAF) and the extended Kalman filter (EKF). The proposed algorithm is the most effective, and the Root Mean Square Error (RMSE) in the east and north is reduced by 12.98% and 35.1% in deep urban canyons. Regarding integrity assessment, the error bound can overbound the positioning errors in deep urban canyons relative to the EKF and the mean value of the error bounds is reduced.

Video analytics (VA), capable of autonomously understanding events in video content, has demonstrated significant potential across various applications, from surveillance to self-driving cars and industrial automation. However, traditional VA, relying on either end-device or cloud-based solutions, faces limitations such as restricted on-device computing power and network congestion at cloud centers. Edge computing offers a promising solution, enabling low-latency, high-accuracy, and bandwidth-efficient performance, thus supporting the rapid growth of VA deployment. This article provides a comprehensive review of VA at the edge, examining aspects of model training, deployment, end-edge-cloud orchestration, and VA platforms. Specifically, we explore model training approaches conducted in the cloud, at the edge, and in hybrid cloud-edge configurations. We also discuss various model deployment techniques, including quantization and network pruning. Furthermore, the article surveys end-edge-cloud orchestration strategies, categorized into VA query offloading and query scheduling. We evaluate practical deployments and review the literature on VA platforms. Finally, we outline several promising future research directions for advancing this field.

Ionospheric delay is one of the largest and most variable sources of error for Ground-Based Augmentation System (GBAS) users because inospheric activity is unpredictable. Under normal conditions, GBAS eliminates ionospheric delays, but during extreme ionospheric storms, GBAS users and GBAS ground facilities may experience different ionospheric delays, leading to considerable differential errors and threatening the safety of users. Therefore, ionospheric monitoring and assessment are important parts of GBAS integrity monitoring. To study the effects of the ionosphere on the GBAS of Guangdong Province, China, GPS data collected from 65 reference stations were processed using the improved “Simple Truth” algorithm. In addition, the ionospheric characteristics of Guangdong Province were calculated and an ionospheric threat model was established. Finally, we evaluated the influence of the standard deviation and maximum ionospheric gradient on GBAS. The results show that, under normal ionospheric conditions, the vertical protection level of GBAS was increased by 0.8 m for the largest over bound ${\sigma }_{vig}$ (sigma of vertical ionospheric gradient), and in the case of the maximum ionospheric gradient conditions, the differential correction error may reach 5 m. From an airworthiness perspective, when the satellite is at a low elevation, this interference does not cause airworthiness risks, but when the satellite is at a high elevation, this interference can cause airworthiness risks.

Considering the inertial measurement unit (IMU) faults risk of an unmanned aerial vehicle (UAV), this paper provides an analysis of the error overboundings of position estimation in a tightly coupled IMU/global navigation satellite system (GNSS) integrated architecture under the IMU fault conditions using an error-state EKF-based approach and provides a comparison to a recently published EKF-based full state method. Simulation results show that both the error overboundings of the error-state and full-state EKFs can fit the state error against the IMU faults, but the error-state EKF is more suitable for UAV navigation system integrity assurance due to its higher calculation eciency. This study will be extended to the integrity monitoring of multisensor systems.