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Missing Value Imputation for Traffic-Related Time Series Data Based on a Multi-View Learning Method
The results indicate that the proposed model outperforms other baselines, especially for block missing pattern with a high missing ratio, and can conclude that combining different views can improve the performance of the imputation. Expand
The relation between working conditions, aberrant driving behaviour and crash propensity among taxi drivers in China.
In insights into potential strategies for preventive education and taxi industry management to improve the working conditions and hence reduce fatigue and road risk for the taxi drivers, the estimates of the hybrid bivariate model reveal that increasing levels of fatigue, reckless behaviour and aggressive behaviour are positively related to a higher propensity of crash involvement. Expand
Estimation of missing values in heterogeneous traffic data: Application of multimodal deep learning model
This study clearly demonstrates the effectiveness of deep learning for heterogeneous traffic data synthesis and missing data imputation with a multimodal deep learning model that can accurately impute the continuously missing data. Expand
Short-to-medium Term Passenger Flow Forecasting for Metro Stations using a Hybrid Model
Metro passenger flow forecasting is an essential component of intelligent transportation system. To enhance the forecasting accuracy and explainable of traditional models, a hybrid model combiningExpand
Traffic speed prediction for intelligent transportation system based on a deep feature fusion model
A deep feature fusion model to predict space–mean–speed using heterogeneous data using artificial neural network, support vector regression, regression tree and k-nearest neighbor is built and compared and results indicate that proposed deep feature fused model can achieve a better performance. Expand
Material conversion, microbial community composition and metabolic functional succession during green soybean hull composting.
The results showed that bacterial and fungal communities had different temporal successions during composting, and saprotrophs represented the dominant fungal trophic mode in the composting process. Expand
Spatiotemporal variations of snow characteristics in Xinjiang, China over 1961–2013
Daily snow data during 1961–2013 at the 105 meteorological stations in Xinjiang, China were used to investigate the spatiotemporal variations of several parameters, including starting and endingExpand
Modeling impacts of mulching and climate change on crop production and N2O emission in the Loess Plateau of China
Abstract Covering soils using mulch can increase crop productivity in dryland agriculture. However, there remains large uncertainty regarding impacts of mulching on nitrous oxide (N2O) emissions,Expand
Day-ahead traffic flow forecasting based on a deep belief network optimized by the multi-objective particle swarm algorithm
A deep feature leaning approach is proposed to predict short-term traffic flow in the following multiple steps using supervised learning techniques and indicates the proposed model can extract complex features of traffic flow and therefore the forecasting accuracy and stability can be effectively improved. Expand
Short‐term highway traffic flow prediction based on a hybrid strategy considering temporal–spatial information
A hybrid strategy that is general and can make use of a large number of underlying machine learning or time-series prediction models to capture the complex patterns beneath the traffic flow is proposed. Expand