• Proposes a Digital Twin framework for EVs routing and charging optimization • Develops a Dual-Population Evolutionary Algorithm regarding to the dynamic environments • Demonstrates robustness under traffic disruptions, road closures, and charging station failures • Enhances adaptability and efficiency of EV operations in urban traffic networks The growing adoption of electric vehicles (EVs) presents new challenges for intelligent transportation systems (ITS), particularly in dynamic traffic environments where routing and charging decisions must adapt to fluctuating conditions. This paper proposes a Digital Twin-based Electric Vehicle Routing and Charging approach (DT-EVRC) that integrates real-time traffic data, predictive analytics, and a Dual-Population Evolutionary Algorithm (DPEA) to optimize EV travel and charging schedules. Unlike traditional static or simplified models, DT-EVRC continuously synchronizes with the physical transportation network, capturing variations in traffic density, charging station availability, and energy constraints. Experimental results on diverse grid-based urban scenarios demonstrate that DT-EVRC achieves robust and adaptive performance under traffic disruptions, road closures, and charging station failures. The proposed approach highlights the potential of digital twin technologies, combined with advanced optimization, to support next-generation ITS by enabling efficient, resilient, and sustainable urban mobility.

Existing flood resilience assessments face a fundamental trade-off: correlational analyses of real-world data suffer from confounding bias, while simulations of hypothetical scenarios lack empirical grounding. To address this identification gap, we propose a physically-grounded causal inference framework that derives unbiased causal parameters directly from physical observation. First, we generate high-fidelity flood maps from synthetic aperture radar (SAR) imagery using a fine-tuned U-Net model adapted for dense urban environments. Second, we integrate these empirical maps into a microscopic traffic simulation model to quantify dynamic network disruptions. Third, we apply double machine learning (DML) to isolate the causal effect of flooding on transport resilience while rigorously controlling for high-dimensional urban form confounders. Applied to a flood event in a dense coastal megacity, our analysis reveals two key discoveries: (1) a nonlinear tipping point around 25% flood intensity, distinct from recent studies assuming linear degradation, beyond which system performance collapses chaotically; and (2) a ‘dual nature of vulnerability’, where low-density suburbs suffer a fragility of isolation (2.9× higher road closure rate) while dense urban cores exhibit a fragility of congestion (1.47× greater waiting time increase). Validated across 130 sensitivity tests, this framework provides a robust, data-driven blueprint for spatially targeted resilience investments, demonstrating that causal identification in disaster contexts requires physically-grounded observation, not synthetic assumptions.

The development of intelligent transportation systems (ITS) in urban areas currently faces a critical misalignment between technological applications and the developmental stages of cities. Most studies on ITS to date have focused on solutions tailored to developed cities, which overlook the practical needs of growing cities. The above problem reduces the applicability of advanced technologies in resource-constrained contexts. To fill the gap, our study constructs a five-tier ITS evaluation framework for cities evolving from city 1.0 to 5.0. Through the synthesis of the literature and practical case analysis, a three-view research paradigm is proposed, “issue–science–engineering” (ISE). It integrates three key elements of ITS in urban areas: the infrastructure network, the transportation network, and the management network, which identify five distinct evolutionary stages of ITS for cities. By pinpointing common critical challenges across these stages, the study distills four priority research themes of ITS for academia: data-driven collaborative optimization, system resilience under complex disruptions, human-centered equity assurance, and incremental transitions toward sustainability. Furthermore, the study establishes a matching mechanism between technological pathways and city developmental stages, which could offer actionable insights for collaboration between industry and academia. Our study provides a decision-making framework for the development of ITS in cities at different stages. We hope that the study could contribute to a more balanced global advancement in this field.

All-in-focus(AIF) images, which contain comprehensive scene information with global sharpness, play a crucial role in high-precision light field (LF) measurement and computational imaging. However, generating AIF images from LF data typically requires accurate depth priors, which are often unavailable or unreliable in practice. To overcome this limitation, directly fusing a series of LF refocused images provides an effective alternative that eliminates the dependency on explicit depth estimation. Nevertheless, existing multi-focus image fusion(MFIF) methods are primarily designed for fusing image pairs with complementary focus, performing poorly when applied to stacks due to the error accumulation that occurs during iterative fusion. To this end, we propose a Frequency-Decoupled Stack Fusion Network (FDSNet) for high-precision depth-free LF AIF image generation. FDSNet incorporates a spatial-frequency joint feature extraction module that captures multi-scale spatial details while decoupling high- and low- frequency components to model textures and contextual information separately, thereby alleviating edge blurring caused by subtle focal variations and weak textures in transition regions. Moreover, a dual-stage cross-attention fusion module, following a coarse-to-fine strategy, suppresses artifacts, enhances edge fidelity, and enables simultaneous fusion of arbitrary numbers of refocused images, thereby avoiding error accumulation and computational redundancy. Extensive experiments on both synthetic and real LF datasets demonstrate that FDSNet achieves superior visual quality and quantitative performance. Additional experiments further demonstrate that FDSNet performs robustly under varying low-light and noisy conditions. These results validate that FDSNet delivers excellent fusion capability in terms of image clarity, detail preservation, noise resistance, and generalization, outperforming existing state-of-the-art methods.

Extreme rainfall and flood events increasingly threaten urban transportation networks, yet conventional resilience evaluations fail to capture the dynamic interplay between flooding, traffic congestion, and emergency service accessibility. To address the above gap, this study provides an integrated framework for transportation resilience evaluation with the proposed spatiotemporal accessibility analysis. Our approach combines real-time traffic simulation, flood modeling, an enhanced two-step floating catchment area (E2SFCA) method with a novel congestion-sensitive impedance function, and grid-based percolation theory to quantify network fragmentation via accessibility thresholds and largest connected component (LCC) metrics. A case study in Shanghai’s Huangpu District demonstrates that peak-hour floods induce greater accessibility losses and faster network fragmentation than off-peak events. Sensitivity analysis highlights accessibility’s higher responsiveness to congestion dynamics over facility siting, highlighting the importance of adaptive traffic management in disaster planning. The workflow provides a robust tool for planners to anticipate vulnerabilities, prioritize interventions, and enhance time-critical disaster preparedness.

Abstract Semantic segmentation is applied extensively in autonomous driving and intelligent transportation with methods that highly demand spatial and semantic information. Here, an STDC‐MA network is proposed to meet these demands. First, the STDC‐Seg structure is employed in STDC‐MA to ensure a lightweight and efficient structure. Subsequently, the feature alignment module is applied to understand the offset between high‐level and low‐level features, solving the problem of pixel offset related to upsampling on the high‐level feature map. The approach implements the effective fusion between high‐level features and low‐level features. A hierarchical multiscale attention mechanism is adopted to reveal the relationship among attention regions from two different input sizes of one image. Through this relationship, regions receiving much attention are integrated into the segmentation results, thereby reducing the unfocused regions of the input image and improving the effective utilisation of multiscale information. STDC‐MA maintains the segmentation speed as the STDC‐Seg network while improving the segmentation accuracy of small objects. STDC‐MA was verified on the validation dataset of Cityscapes. The segmentation result of STDC‐MA attained 78.32% mIOU with the input of 0.5× scale, 4.92% higher than STDC‐Seg.

As well as offering tremendous opportunities for existing transportation systems, autonomous driving presents challenges for traffic management, particularly in mixed traffic flow. The design of driving strategies for autonomous vehicles (AVs) considers both safety and efficiency to promote smooth traffic flow with no collisions. However, the complex traffic conditions at freeway bottlenecks mean that the traffic impacts of AVs in such scenarios remain unclear. Therefore, this study employs several driving strategies utilizing deep reinforcement learning (RL) to form a mixed-autonomy traffic flow on a one-way two-lane freeway with on- and off-ramps and weaving sections in SUMO traffic microsimulations. The study also examines the overall traffic impacts in terms of congested patterns, safety, efficiency, and passenger comfort. Multiple experiments are conducted with different AV driving strategies, penetration rates, and key traffic conditions. The results show that AVs with a deep-RL driving strategy significantly mitigate traffic-congested patterns and prevent the propagation of shockwaves, but at the expense of speed at low penetration rates. Moreover, the proposed driving strategy is conducive to a safer capability in vehicle dynamics. However, AVs under a deep-RL strategy lead to poorer passenger comfort at bottlenecks. Therefore, it is crucial to design a driving strategy with high robustness and scalability so that deep RL-based AVs can adapt to more complex scenarios and traffic events. Overall, the present research improves the understandings of AV driving mechanisms and provides insights into corresponding future traffic-management measures.