To facilitate the intelligent and automated provision of mobility services by autonomous transportation systems, bike-sharing can be a supplement to public transport for addressing their point-to-point issue, namely, last mile service. However, according to the different nature of land use, the uneven spatio-temporal distribution of travel demand can directly lead to difficult access to bikes with high travel costs for users and operating costs for operators. Based on this, this paper analyzes the user behavior patterns within different areas by using GeoHash encoding and proposes a hierarchical autonomous vehicle scheduling model based on tide bicycle-sharing traffic, namely, HATB. It minimizes operating costs and maximizes user satisfaction to dynamically optimize scheduling routes and required vehicles within each layered zone. As for extracting historical orders of Mobike in Beijing, an example analysis through the genetic algorithm of HATB is conducted to support effective and efficient scheduling. Compared to existing scheduling methods, HATB has faster convergence and lower time complexity, which improves bike turnaround efficiency and practical application ability, thus making it more convenient for the public to travel during peak hours.

To study the classification and evolution of key technologies in the transportation field, the data of 36 authoritative SCI journals in the transportation field were collected from the Web of Science core collection database from 2001 to 2020. Based on the bibliometric method, this study used Python to process and visualize data, combined with bibliometric software VOSviewer to assist data visualization. Firstly, a preprocessing data algorithm was designed to deduplicate the collected data, merge synonyms, and extract key technologies. Then the paper records that contained the key technology lexicon were filtered out. Next, the annual number of publications and the distribution of key technologies over time were counted. The least squares method was used to fit the distribution of the annual proportion of the publications, and the slope k₁ of the fitted linear regression equation was used to determine the research interest trend of key technologies. The key technologies were divided into hot technology, cold technology, and other technologies, according to the research heat trend. In order to further explore the research hotspots, the least squares method was also used to fit the citations of all technologies to obtain the slope k₂. We use the Gaussian mixture model (GMM) algorithm to cluster k₁ and k₂ of each technology. As a result, the 144 technologies were divided into 13 super-key technologies, 60 key technologies, 59 relative key technologies, and 12 lower-key technologies. Then, the evolution of key technologies was analyzed from two perspectives of weighted evolution and cumulative evolution. And the technology evolution trend in the transportation field in the past 20 years was explored. Finally, the cooccurrence clustering method was adopted to divide key transportation technologies into five categories: vehicle technology and control, optimization algorithms and simulation techniques, artificial intelligence and big data, Internet of Things and computing, and communication technology. The research results can provide references for different people in the transportation field, including but not limited to researchers, journal editors, and funding agencies.

The safe deployment of multiple Unmanned Aerial Vehicles (UAVs) in complex urban environments relies heavily on accurate environmental perception and efficient cooperative path planning. However, executing multi-UAV operations in low-altitude airspaces faces severe challenges due to the dual constraints of complex building clusters and steady-state wind field disturbances. These dynamic environmental factors frequently distort sensory expectations, inducing trajectory drift and degrading policy robustness. To address these limitations, this paper proposes an enhanced Dueling Double Deep Q-Network (D3QN) algorithm, termed NPD3QN, tailored for perception-aware multi-UAV cooperative path planning. By formulating the perceived environmental data (e.g., wind speed, obstacle distances, and inter-UAV states) into a Markov Decision Process, an N-step update strategy is integrated to enhance the characterization of long-term returns. Simultaneously, an improved Prioritized Experience Replay (PER) mechanism is developed to actively filter negative experiences and assign dynamic weights to critical state-action samples, thereby significantly elevating training stability. A 3D urban kinematic environment incorporating a steady-state simulated wind field is constructed. Extensive ablation and comparative results demonstrate that NPD3QN effectively maps high-dimensional state perceptions to robust control commands. In wind-disturbed scenarios, it generates highly streamlined cooperative trajectories, reducing the total path length by approximately 11.7% compared to the standard D3QN baseline. While currently evaluated within steady-state simulated constraints, this study establishes a robust, sensor-driven methodological foundation for autonomous multi-UAV cooperative path planning in wind-disturbed airspaces.

Driven by economic development and environmental protection, vehicles are gradually renovating their power to renewable energy. As an essential part of renewable energy, photovoltaic (PV) energy is highly valued and studied worldwide. Future social development is inseparable from it when facing the current situation of exhausting fossil energy and highly polluting. To solve the problem of the low utilization of converting solar power to electrical energy, this paper proposes a wavelet-improved fuzzy logic (W-IFL) maximum power point (MPP) control model. The W-IFL designs a wavelet network for predicting the MPP and fuzzy rules for tracking the MPP, which achieves full online control on the basis of a neural-fuzzy structure. Comparative analysis indicates that W-IFL outperforms other widely used MPP tracking (MPPT) methods, which reduces oscillation at MPP, prediction error, and tracking time, and improves training efficiency and controlling ability, thus making it more rational to promote the development of the vehicle industry.

The coupling coordination of the logistics industry and manufacturing industry conducive to the sustainable development of logistics and manufacturing and the stability of sustainable supply chain. The logistics and manufacturing industries are not only the basic industries that support social development, but also the industries with high carbon emissions. Firstly, this paper classifies the carbon emissions from the logistics industry and manufacturing industry as undesirable outputs, evaluates the ecological efficiency of the logistics industry (LEE) and manufacturing industry (MEE) in the Yangtze River Delta from 2006 to 2019 by using the unexpected slacks-based measure (SBM) model. Secondly, the coupling coordination method is used to analyze the coupling coordination scheduling of industrial ecological efficiency. Thirdly, the paper analyzes the spatial differences of the coupling coordination ecological efficiency between logistics industry and manufacturing industry (MLCC) by using the exploratory spatial data analysis method. Finally, the spatial econometric model is used to analyze the driving factors of the MLCC. The results show: The ecological efficiency of the manufacturing industry has steadily improved. The ecological efficiency of the logistics industry presents the rising trend in fluctuation. The level of the coupling coordination development between the logistics and manufacturing industries is high. The results of the spatial heterogeneity analysis show that the spatial differentiation of high-high agglomeration and low-low agglomeration is obvious. The spatial agglomeration characteristics are relatively stable, and the spatial diffusion effect is strong; In space, the MLCC shows a trend of developing from multiple agglomeration areas to one agglomeration area. The results of driving factor analysis show that foreign direct investment(FDI), government intervention(GI) and human capital(HP) have positive effects on the MLCC, while industrial structure(IS), environmental regulation(ER) and energy intensity(EI) have negative effects on the MLCC.