Intelligent expressway management requires reliable prediction of travellers' route choices and quantitative assessment of their responses to operational interventions. This study investigates a data-driven route choice framework that links large-scale toll transaction data with ensemble learning to support differentiated tolling and route management. Actual vehicle routes were reconstructed from toll transaction records, electronic toll collection (ETC) gantry records, and expressway network topology, from which revealed-preference candidate route sets were derived. A total of 28 features were constructed to characterize route attributes, relative differences among candidate routes, choice-set composition, and trip attributes. A Light Gradient Boosting Machine (LightGBM) model was then developed using each “trip $\times$ candidate route” pair as the unit of observation, and its predictive performance was compared with random forest, Extreme Gradient Boosting (XGBoost), multinomial logit (MNL), nested logit, and binary logistic regression. The empirical analysis used 329,699 trips from the Guangdong expressway network, including 32,473 trips in the test set. The Light Gradient Boosting Machine (LightGBM) model achieved a route-level hit rate of 85.71%, exceeding the random-guessing benchmark by 43.81 percentage points and outperforming the three conventional discrete-choice and logistic models, while showing performance comparable to random forest and XGBoost. The sensitivity analysis further showed that toll responses varied substantially with a route’s competitive position, choice-set size, and vehicle type. Routes with baseline choice probabilities of 50%–80% were more responsive to toll changes than strongly dominant routes, while trucks showed greater toll sensitivity than cars. The results demonstrate that large-scale operational data and ensemble learning can provide a reproducible framework for route choice prediction and scenario-based evaluation of differentiated toll policies. The proposed framework supports data-driven decision-making for intelligent expressway operations and provides a practical basis for evaluating route-level traffic management strategies.

The digital transformation of logistics is increasingly driven by interconnected technologies that support workforce coordination, data-driven decision-making, operational integration, and human-centered industrial management. However, logistics companies face difficulties in selecting digital technologies because their adoption involves simultaneous technological, organizational, economic, and employee-related considerations. This study develops an intelligent multi-criteria decision-support framework for evaluating and prioritizing digital technologies for workforce management in logistics companies. Five technology alternatives—artificial intelligence (AI)-based human resources (HR) systems, Internet of Things (IoT) and wearable technologies, enterprise resource planning (ERP)/HR information system (HRIS), digital learning and training, and digital workforce management—are assessed against eight criteria covering implementation complexity, employee productivity, employee satisfaction and retention, implementation and operating costs, scalability and flexibility, data security and privacy risks, legal and ethical risks, and integration capability. The criteria importance through intercriteria correlation (CRITIC) method is applied to derive objective criterion weights from the decision matrix, while interactive and multicriteria decision making (TODIM) is used to rank the alternatives by incorporating the asymmetric effects of gains and losses in the decision process. The results show that Digital Workforce Management achieves the highest overall dominance value (1.000), followed by Digital Learning and Training (0.967) and ERP/HRIS (0.921) systems, whereas AI-based HR systems and IoT and Wearable Technologies rank fourth and fifth, respectively. Robustness analysis using alternative weighting schemes and the technique for order preference by similarity to ideal solution (TOPSIS) and weighted aggregated sum product assessment (WASPAS) methods confirms a relatively stable separation between the three higher-ranked and two lower-ranked alternatives, although the relative positions of the leading technologies vary across methodological settings. The findings demonstrate that digital technology selection in logistics should be treated as an industrial intelligence problem in which operational integration, workforce performance, organizational adaptability, and human-centered risks are evaluated jointly. The proposed framework provides structured decision support for logistics organizations seeking to align digital workforce technologies with broader Industry 4.0 and Industry 5.0 transformation objectives.

Automated unmanned aerial vehicle (UAV) docking systems play an important role in unattended industrial inspection and monitoring. However, coordinating mechanical movements, maintaining landing-platform stability, and securing aircraft during deployment and recovery remain challenging. This study investigates the mechanical design and coordinated motion control of a four-position UAV docking system. An integrated architecture comprising a sliding roof, a three-degree-of-freedom (3-DOF) landing platform, and a mechanical centering and locking mechanism was developed. The roof and centering drives were analysed through mechanical load calculations and parameter sensitivity analysis, while a supervisory control framework was formulated to coordinate actuator movements through position feedback and operating interlocks. A representative three-actuator model was also established to evaluate platform levelling under static inclination and periodic disturbances using proportional (P) and proportional-derivative (PD) control. The calculated roof resistance and driving torque were 250 N and 6.75 $\mathrm{N\cdot m}$, respectively, while the centering resistance was 325 N and the estimated motor torque was 4.51 $\mathrm{N\cdot m}$ under an assumed unit transmission ratio. In the simplified numerical model, P and PD control achieved static settling times of 1.02 s and 1.21 s, respectively. Under periodic disturbances, the combined root-mean-square (RMS) attitude error decreased from 2.599$^\circ$ without compensation to 1.299$^\circ$ with P control and 1.185$^\circ$ with PD control. The results indicate that coordinated mechanical operation and feedback-based platform levelling can be integrated within a common docking framework. The study provides a basis for the development of unattended industrial UAV docking systems, although the predicted control performance requires validation using the actual platform geometry and actuator characteristics.