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Annual average daily traffic (AADT) data are important for various transportation research areas, including travel model calibration and validation, pavement design, roadway design, and air quality compliance. Specifically for model calibration and validation in long-range transportation planning, a base-year model requires numerous count locations across the study region. Sometimes count data for the lower classified roadways are not readily available. Detailed models require traffic counts for not only higher classifications of roadways such as freeways and arterials but also collector and, in some instances, local roadways. To predict AADT better for desired count locations on nonfreeway facilities, spatial dependency is considered. The theory behind the use of spatial dependency is that the traffic volume at one monitoring station is correlated with the volumes at neighboring stations. The spatial regression model takes into account both spatial trend (mean) and spatial correlation, which is modeled by a geostatistical approach called kriging. The spatial regression model is applied to AADT in Wake County, North Carolina. Results indicate that the overall predictive capability of the spatial regression model is much better than that of the ordinary regression model. In addition, the urban area has more reliable prediction than the rural area. Finally, the spatial regression model is expected to provide better predictions for desired count locations where no observed data currently exists due to budget limitations.

Diverse artificial synapse structures and materials are widely proposed for neuromorphic hardware systems beyond von Neumann architecture owing to their capability to mimic complex information processing tasks such as image recognition, natural language processing, and learning. Nevertheless, temporal and spatial randomness in the movement of ion and electron particles that exist in materials usually prevents the solid‐state‐based synaptic devices from enabling the reliable modulation of synaptic plasticity. An aluminum nanoparticle (Al NP)‐embedded indium gallium zinc oxide (IGZO) synaptic transistor whose spike peak level and conductance change can be precisely modulated by the density of Al NPs within the IGZO channel is demonstrated. Essential synaptic functions including excitatory or inhibitory postsynaptic current, paired pulse facilitation, and short‐term potentiation or depression are also thoroughly emulated in the synaptic transistor device with the most optimized Al NP density: IGZO:Al NPs (6 nm). Moreover, controllable switching from short‐term to long‐term memory regimes essential for a learning task is demonstrated. Simulation results prove that this transistor can provide a decent recognition accuracy for neuromorphic computing. Indeed, the integrated IGZO:Al NP synaptic circuit with the effective synaptic plasticity will facilitate the implementation of a reconfigurable neuromorphic computing system.

Many studies have attempted to predict chlorophyll-a concentrations using multiple regression models and validating them with a hold-out technique. In this study commonly used machine learning models, such as Support Vector Regression, Bagging, Random Forest, Extreme Gradient Boosting (XGBoost), Recurrent Neural Network (RNN), and Long–Short-Term Memory (LSTM), are used to build a new model to predict chlorophyll-a concentrations in the Nakdong River, Korea. We employed 1–step ahead recursive prediction to reflect the characteristics of the time series data. In order to increase the prediction accuracy, the model construction was based on forward variable selection. The fitted models were validated by means of cumulative learning and rolling window learning, as opposed to the hold–out technique. The best results were obtained when the chlorophyll-a concentration was predicted by combining the RNN model with the rolling window learning method. The results suggest that the selection of explanatory variables and 1–step ahead recursive prediction in the machine learning model are important processes for improving its prediction performance.

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