Mixed traffic flow, consisting of human-driven vehicles (HDVs) and connected autonomous vehicles (CAVs), is expected to dominate future roadways. This study develops a heterogeneous traffic flow model in an intelligent connected environment to analyze macroscopic characteristics, including traffic capacity, critical density, and fundamental diagrams, under varying CAV penetration rates. Three car-following modes are considered: HDVs following HDVs, HDVs following CAVs, and CAVs following CAVs, modeled respectively by the Intelligent Driver Model (IDM), Adaptive Cruise Control (ACC), and Cooperative Adaptive Cruise Control (CACC). Model parameters are calibrated using genetic algorithms based on publicly available autonomous driving datasets. Calibration results demonstrate high predictive accuracy, with mean absolute percentage errors of 0.009% (distance) and 2.37% (speed) for IDM, 0.27% and 3.67% for ACC, and near-zero errors for CACC. Analysis of fundamental diagrams shows that increasing CAV penetration significantly enhances traffic flow efficiency, improves stability, and mitigates congestion. The findings provide theoretical guidance for optimizing mixed traffic operations and support the development of intelligent and connected transportation systems.

The increasing frequency and intensity of extreme weather events, coupled with rapid urbanization, are placing unprecedented pressure on urban environments. The Three-Point Sponge Policy Approach (3PSPa) integrates the Three-Point Approach (3PA) with Sponge City principles to enhance urban flood resilience through design-driven, adaptive, and multifunctional solutions. This paper applies the 3PSPa framework to Zhengzhou, China, which experienced devastating flooding in 2021, to assess its current flood resilience measures and explore pathways for improvement. Two contrasting urban districts in Zhengzhou were analysed: B1, a newly developed, low-density district offering high potential for implementing large-scale blue-green-grey infrastructure (BGGI); and B2, a dense, older district where  interventions are limited, typically emerging incrementally through targeted retrofitting associated with urban renewal activities. By applying the 3PSPa’s five-step design process, this study identifies resilience gaps and proposes tailored interventions, balancing short-term, localized strategies with long-term, catchment-wide transformations. It emphasizes the importance of shifting from a conveyance-based water management approach to a diversified strategy that integrates infrastructure with natural hydrological processes.

Despite the continued success of machine learning (ML), its indiscriminate use may not always yield the expected benefits, can waste resources, introduce unforeseen complexities, and even lead to failure. From this lens, this paper systematically addresses the following elemental questions: When should we integrate ML, and which problem-solving workflows benefit the most? Thus, this paper critically examines existing decision making frameworks and guidelines to identify their strengths and limitations in defining ML applicability. Then, through a comprehensive literature review and analysis, we outline explicit suitability criteria to assess when ML should be favored over traditional engineering methods. Based on this analysis, we propose a conceptual structured decision making framework that incorporates practical steps and checklists to guide engineers in method selection, model development, validation, and deployment. Additionally, this paper highlights critical challenges and potential future research questions.

Vehicular Ad Hoc Networks (VANETs) are critical for transportation safety and efficiency but face serious security challenges due to limitations of traditional cryptography and emerging cyber threats. This paper proposes a novel reconciliation-free Physical Layer Key Generation (PKG) framework based on dynamic Received Signal Strength (RSS) measurements and Triplet Networks (TN). Unlike conventional PKG schemes, the proposed approach eliminates reconciliation overhead while maintaining high entropy and strong resistance to eavesdropping. Realistic RSS is generated by integrating SUMO-based vehicular mobility with NS-3 network simulations, capturing spatiotemporal channel dynamics. The TN architecture extracts reciprocal channel features through hierarchical convolutional layers and optimized embedding spaces for secure key extraction. Experimental results show near-zero bit error rates between legitimate vehicles, high key entropy (0.69 bits/bit), and complete failure of eavesdroppers to reconstruct keys. Among 3,200 generated keys, 74.1% achieved perfect agreement without reconciliation, while eavesdroppers achieved 0% success. The proposed framework demonstrates a scalable, and robust security solution for VANET communications.

Transverse cracking is a major distress mechanism in Continuously Reinforced Concrete Pavement (CRCP), affecting ride smoothness, service life, and maintenance strategies. This research introduces a hybrid predictive framework that couples Particle Swarm Optimization (PSO) with Gradient Boosting Machine (GBM) to enhance the accuracy of transverse crack prediction in CRCP. The analysis utilized 395 records from 33 pavement sections obtained from the Long-Term Pavement Performance (LTPP) program, encompassing structural, environmental, traffic, and performance-related parameters. PSO was applied to fine-tune critical GBM hyperparameters, namely the number of iterations, learning rate, and tree depth. The optimized PSO–GBM model demonstrated excellent performance, yielding an average RMSE of 1.62 and an R² of 0.99 under 5-fold cross-validation, surpassing benchmark models such as conventional GBM, Random Forest, Support Vector Regression (SVR), Artificial Neural Networks (ANN), and Linear Regression. Sensitivity analysis revealed that L3 thickness, L4 thickness, and Annual Average Daily Traffic (AADT) were the most significant contributors, consistent with engineering knowledge of crack development. Validation through residual distribution and equality line plots confirmed the robustness and stability of the proposed approach across varying severity levels.

Centrifugal compressors are commonly employed for pressure compensation in long-distance natural gas pipelines. Accurate prediction of compressor performance is essential for safety and economic considerations. However, due to the invariance of process requirements over time, operational data is usually insufficient to establish a comprehensive model that covers varying operating conditions. This paper presents a prediction method based on the long short-term memory (LSTM) network and transfer learning technique, integrating similarity laws to expand the feature domain and address the issue of limited data. In the case study, the constructed TransLSTM model, a fusion of transfer learning and LSTM, is compared with pure LSTM, Gaussian Process (GP), Gradient Boosting Decision Tree (GBDT), Random Forest (RF), and Support Vector Machine (SVM) models. Results indicate that TransLSTM achieves the highest prediction accuracy, with deviations from actual values of only 2.9% for power and 0.971% for compressor ratio. Verification through comparisons between the training and test sets demonstrates its excellent generalization ability, which is particularly crucial when operational data spans a limited range of working conditions. Additionally, its stability is significantly superior to that of benchmark models.

The rapid advancement of new technologies and the widespread availability of data have enabled new business paradigms in urban logistics and transportation. Although optimization theories and algorithms are well-established, a persistent gap remains between these theoretical foundations and the practical skills required to formulate models and implement effective, data-driven solutions. This positioning paper aims to bridge this gap by introducing optimization modeling frameworks and solution methodologies tailored to urban logistics and transportation applications. The paper targets researchers, students, and practitioners in non-business disciplines such as civil engineering, industrial engineering, economics, and computer science. We present several widely used data-driven optimization paradigms, including deterministic optimization with sensitivity analysis, two-stage stochastic programming, integrated simulation-optimization, and Markov decision process, and discuss how mathematical programming, statistical methods, simulation, and machine learning can support decision making. We then provide a systematic classification of urban transportation applications based on key decision characteristics, such as whether decisions are static or dynamic, deterministic or stochastic, and whether they involve interactions among multiple decision makers. Building on this classification, we propose a practical roadmap to guide the selection and implementation of appropriate optimization approaches. Illustrative applications, demonstrate the relevance and versatility of the proposed frameworks.

Cross-camera vehicle trajectory reconstruction is essential for roadside perception systems supporting traffic analysis, safety evaluation, and infrastructure monitoring. In practical deployments, roadside cameras often exhibit partial or minimal overlaps, providing limited spatiotemporal continuity and making cross-camera association challenging. This paper proposes a global trajectory reconstruction framework designed for such roadside surveillance networks. Single-camera trajectories are first extracted and projected into a unified road-plane coordinate system through calibration, enabling metric-level comparison across views. Appearance features and motion cues are then jointly exploited in a multi-stage association strategy that integrates spatiotemporal feasibility and visual similarity. Matched trajectories are stitched and refined using quality-aware alignment and short-gap compensation to generate continuous and physically plausible cross-camera trajectories. Real-world experiments with roadside cameras, RTK-equipped vehicles, and UAV-based trajectory data demonstrate that the proposed method achieves high association accuracy and reconstruction precision in both single- and multi-vehicle scenarios while maintaining low computational overhead.

This study proposes a novel adaptive traffic signal control method leveraging a Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) to optimize signal timing by integrating variable cell length and multi-channel state representation. A road partition formula consisting of the sum of logarithmic and linear functions was proposed. The state variables are a vector composed of three channels: the number of vehicles, the average speed, and space occupancy. The set of available signal phases constitutes the action space, and the selected phase is executed with a fixed green time. The reward function is formulated using the absolute values of key traffic state metrics—waiting time, speed, and fuel consumption. Each metric is normalized by a typical maximum value and assigned a weight that reflects its priority and optimization direction. The simulation results, using Sumo-TensorFlow-Python, demonstrate a cross-range transferability evaluation and show that the proposed variable cell length and multi-channel state representation method excels compared to fixed cell length in optimization performance.

Increasing public transport adoption is important for reducing dependence on private vehicles, thereby helping to lower transport-related emissions and support urban climate goals. Despite substantial public investment, public transport ridership in Malaysia remains low, suggesting that infrastructure improvements alone may be insufficient to shift travel behaviour. This underscores the need to understand the behavioral and contextual factors underlying public transport adoption. This study applies the Technology Acceptance Model (TAM) to examine how perceived usefulness and perceived ease of use shape behavioral intention to use public transport, while considering trust in government as a contextual factor in public service delivery. A cross-sectional survey of 122 Malaysian university students measured perceived usefulness, perceived ease of use, trust in government, and behavioral intention. An exploratory path analysis indicated that trust in government significantly predicted both perceived ease of use and perceived usefulness, while perceived usefulness emerged as the most immediate predictor of behavioral intention. The findings suggest that increasing public transport adoption requires more than infrastructure investment alone; it also depends on building institutional trust and improving usability in ways that enhance the perceived usefulness of public transport.

This study investigates the suspension of sediments accumulated in water distribution tanks during seismic sloshing. Shaking‑table tests were conducted using a small rectangular tank to examine how internal water flow and sloshing amplitude influence sediment uplift. Two experiments were performed under identical vibration conditions: one using tracers to visualize flow patterns, and another using actual sediments to evaluate suspension and resulting turbidity. When sinusoidal input at the tank’s primary sloshing frequency was applied, tracers near the bottom wall rose to the surface once the water level reached the ceiling. Sediment tests similarly showed increased suspension and turbidity. For a water depth of 600 mm, turbidity remained below the water quality standard of 5 mg/L as long as the water level rise did not exceed 126 mm. Turbidity peaked not during shaking but after oscillation ceased. The study also examined the effect of internal columns, finding that sediments beneath column bases were rapidly disturbed, producing turbidity more than twice that observed without columns. Because the seismic motions used correspond to observed ground motions, the results suggest that uncleaned distribution tanks may pose a risk of water supply interruption during earthquakes.

Protective energy-absorbing components must reconcile lightweight design, high dissipation capacity, and reliable performance under diverse loading conditions. This study presents a novel origami unit, whose manufacturability was verified via kinematic analysis and whose baseline quasi-static axial compression performance exceeds that of two benchmark origami geometries by up to 52% in total energy absorption (E_abs) and 62% in specific energy absorption (SEA). A hybrid optimization framework integrating XGBoost machine learning surrogate modeling and NSGA-II multi-objective algorithms efficiently identified Pareto-optimal geometric parameters, achieving more than 97% prediction accuracy for key performance metrics. Embedding aluminum-foam-filled into the optimized origami cores produced a sandwich panel whose energy absorption (E_abs), specific energy absorption (SEA), and crush force efficiency (CFE) surged by approximately 619%, 101%, and 349%, respectively, compared to the unfilled structure. Finite-element simulations accurately capture deformation stages and confirm that foam filling yields more uniform hinge formation and markedly enhanced stability. The findings provide a novel design concept and methodology for advanced protective sandwich structures in both civilian and military applications.