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Driving cycles and road slope are two important factors affecting fuel saving performance of plug-in hybrid electric buses (PHEBs) in Chinese cities. Moreover, onboard auxiliary equipment (e.g., Global Position System receiver and General Packet Radio Service (GPRS) wireless module) of PHEB may provide potential means to communicate with the control center of the bus company, allowing for driving cycle prediction through data communication between foregoing buses and the control center. With this general approach in mind, and by utilizing driving data clustering and driving cycle classifier, this paper presents a multimode switched logic control strategy, targeting fuel economy improvement of the PHEB team for a particular city bus route. First, the normal feature parameters are extracted from the sampled driving history cycles, and the composed feature parameters are given by a mapping of normal feature parameters in this approach. A novel improved hierarchical clustering algorithm is applied for driving cycles' data clustering into four groups. Then, on the basis of the clustering results, support vector machine method is used to predict the current driving cycle. Finally, a switched driving controller is presented according to current type of driving cycle and slope information. Simulation results are compared with those of traditional methods in the given real-world driving cycles of city bus, showing significant improvement, which may offer a theoretical solution with engineering application. Experimental results also demonstrate that the proposed control approach is feasible in the tested bus routes. [ABSTRACT FROM PUBLISHER]

Cellular automata (CA) models have been extensively employed to predict and understand the spatiotemporal dynamics of land use. Driving factors play a significant role in shaping and driving land-use changes. Mining land-use transition rules from driving factors and quantifying the contribution of driving factors to land-use dynamics are fundamental aspects of CA simulation. However, existing CA models have limitations in obtaining accurate transition rules and reliable interpretations simultaneously for multiple land-use simulations. In this study, we constructed a CA model based on a tree-based deep learning algorithm, deep cascade forest (DCF), to improve multiple land-use simulations and driving factors analysis. The DCF algorithm was utilized to mine accurate multiple land-use transition rules without overfitting to improve CA simulation accuracy. Additionally, a novel ensemble mean decrease of impurity (MDI) factor importance analysis method (DCF-MDI), which leverages the cascade structure of the DCF model, was proposed to qualify the contribution of each driving factor to land-use dynamics stably and efficiently. To evaluate the effectiveness of the proposed DCF-CA, we applied the model to simulate land-use distributions and explore the driving mechanisms of land-use dynamics in the Pearl River Delta (PRD), China, from 2000 to 2010. Compared to existing models, the proposed DCF-CA model exhibits the highest accuracy (FoM = 23.79%, PA = 39.77%, UA = 36.35%, OA = 91.50%), which demonstrates its superiority in mining accurate transition rules for capturing multiple land-use dynamics. Factor importance analysis reveals that the proposed DCF-MDI method yields more stable ranking orders and lower standard deviation of contribution weights (<0.10%) compared to the traditional method, indicating its robustness to random disturbances and effectiveness in elucidating the driving mechanisms of land-use dynamics. The DCF-CA model proposed in this study, demonstrating high simulation accuracy and reliable interpretability simultaneously, can provide substantial support for sustainable land use management. [ABSTRACT FROM AUTHOR]

Machine learning methods offer opportunities improve pandemic response and risk management by supplementing mechanistic modeling approaches to pandemic planning and response based on diverse sources of data at every level from the local to global scale. However, such solutions rely on the availability of appropriate data as well as communication and dissemination of that data to develop tools and guidance for decision making. A lack of consistency in the reporting and availability of disaggregated, detailed data on COVID-19 in the US has limited the application of artificial intelligence methods and the effectiveness of those methods for projecting the spread and subsequent impacts of this disease in communities. These limitations are missed opportunities for AI methods to make a positive contribution, and they introduce the possibility of inappropriate use of AI methods when not acknowledged. Going forward, governing bodies should develop data collection and sharing standards in collaboration with AI researchers and industry experts to facilitate preparedness for pandemics and other disasters in the future. [ABSTRACT FROM AUTHOR]