Traffic congestion brings not only delay and inconvenience, but other associated national concerns, such as greenhouse gases, air pollutants, road safety issues and risks. Identification, measurement, tracking, and control of urban recurrent congestion are vital for building a livable and smart community. A considerable amount of works has made contributions to tackle the problem. Several methods, such as time-based approaches and level of service, can be effective for characterizing congestion on urban streets. However, studies with systemic perspectives have been minor in congestion quantification. Resilience, on the other hand, is an emerging concept that focuses on comprehensive systemic performance and characterizes the ability of a system to cope with disturbance and to recover its functionality. In this paper, we symbolized recurrent congestion as internal disturbance and proposed a modified metric inspired by the well-applied “R4” resilience-triangle framework. We constructed the metric with generic dimensions from both resilience engineering and transport science to quantify recurrent congestion based on spatial-temporal traffic patterns and made the comparison with other two approaches in freeway and signal-controlled arterial cases. Results showed that the metric can effectively capture congestion patterns in the study area and provides a quantitative benchmark for comparison. Also, it suggested not only a good comparative performance in measuring strength of proposed metric, but also its capability of considering the discharging process in congestion. The sensitivity tests showed that proposed metric possesses robustness against parameter perturbation in Robustness Range (RR), but the number of identified congestion patterns can be influenced by the existence of ϵ. In addition, the Elasticity Threshold (ET) and the spatial dimension of cell-based platform differ the congestion results significantly on both the detected number and intensity. By tackling this conventional problem with emerging concept, our metric provides a systemic alternative approach and enriches the toolbox for congestion assessment. Future work will be conducted on a larger scale with multiplex scenarios in various traffic conditions.

The climate change issue has been striking and bringing pressure on all countries and industries. The responsibility of the Architecture, Engineering, Construction and Facility Management (AEC/FM) industry is heavy because it accounts for over one-third of global energy use and greenhouse gas emissions. At the same time, the development of digital technology brings the opportunity to mitigate environmental issues. Therefore, this study intended to examine the state-of-the-art of digital development and transformation in the AEC/FM industry by collecting and reviewing the developed digital carbon footprint analysis tools in infrastructure, building, and city scopes. Specifically, this study (1) generated a review methodology for carbon footprint analysis results; (2) demonstrated the review results from the infrastructure, building, and city scopes, analysed and compared the results crossing the scopes from four aspects: carbon footprint analysis strategy, standards and protocols, rating systems, and general development level of digital tools; and (3) discussed the potential directions in the industry to address the environmental issues. This study indicated that the digitalisation level regarding carbon-related areas is still at an early stage, and efforts should be taken both academically and practically to drive the digital development confronting the harsh climate change issue.

The Electric Vehicle (EV) market is experiencing unprecedented growth. Accurate prediction of EV charging demand is essential for transportation system operations, such as real-time traffic management, route optimization, and station utilization planning. However, Cyber threats can compromise the accuracy of charging demand predictions, leading to significant disruptions in transportation services, e.g., suboptimal station management, unexpected congestion at charging facilities, and degraded service quality for EV users. This study introduces Generative Multi-task Self-supervised Learning for Prediction (GenS2-P), a cyberattack-resilient framework designed to ensure reliable charging demand predictions under adversarial conditions. GenS2-P incorporates a Denoising/Reconstruction AutoEncoder (DRAE) and a spatio-temporal prediction model to tackle the dual challenges of data poisoning and DoS attacks. By leveraging generative self-supervised learning and multi-task learning, GenS2-P effectively extracts spatio-temporal patterns to denoise and reconstruct data corrupted by cyberattacks. Experimental evaluations using real-world EV charging data demonstrate that GenS2-P significantly reduces prediction errors and mitigates cyberattack-induced disruptions. This improved prediction reliability enables more effective charging infrastructure management and supports robust transportation system operations even under adverse conditions.

Sustainability and its component resilience have become an important issue that cannot be neglected in airspace planning and development. Resilience, as an emerging system concept, is critical to sustainability in many fields. With the rapidly growing demand in China’s air transportation sector, airspace congestion and flight delays have become a major issue in the fast development of this sector, and threatens the sustainability and resilience of air traffic control (ATC) systems such as waste of resources, air pollution, etc. Sectors, the basic units of an ATC system, play a significant role in ensuring the safe and smooth operations of day-to-day flights. In this paper, we apply the complex network theory to establish a model of China’s air sector network (CASN) and examine a series of characteristic parameters with an empirical analysis on its vulnerability and resilience. Through a simulation-based approach, the CASN’s resilience was quantitatively assessed with a resilience indicator (RI) in different scenarios to identify the optimal recovery strategy for building higher system resilience. The results show that the CASN has a lengthy average shortest path and a small clustering coefficient, which demonstrates a hybrid topological feature. We have also found that betweenness has the greatest impact on the resilience and has managerial implications to understand the relationship between vulnerability and resilience in CASN, so as to achieve the resilience and sustainability of CASN.