The data with the advancement of information technology are increasing on daily basis. The data mining technique has been applied to various fields. The complexity and execution time are the major factors viewed in existing data mining techniques. With the rapid development of database technology, many data storage increases, and data mining technology has become more and more important and expanded to various fields in recent years. Association rule mining is the most active research technique of data mining. Data mining technology is used for potentially useful information extraction and knowledge from big data sets. The results demonstrate that the precision ratio of the presented technique is high comparable to other existing techniques with the same recall rate, i.e., the R-tree algorithm. The proposed technique by the mining effectively controls the noise data, and the precision rate is also kept very high, which indicates the highest accuracy of the technique. This article makes a systematic and detailed analysis of data mining technology by using the Apriori algorithm.
With the rapid increase of the amount of vehicles in urban areas, the pollution of vehicle emissions is becoming more and more serious. Precise prediction of the spatiotemporal evolution of urban traffic emissions plays a great role in urban planning and policy making. Most existing methods usually focus on estimating vehicle emissions at historical or current moments which cannot well meet the demands of future planning. Recent work has started to pay attention to the evolution of vehicle emissions at future moments using multiple attributes related to emissions, however, they are not effective and efficient enough in the combination and utilization of different inputs. To address this issue, we propose a joint framework to predict the future evolution of vehicle emissions based on the GPS trajectories of taxis with a multi-channel spatiotemporal network and the motor vehicle emission simulator (MOVES) model. Specifically, we first estimate the spatial distribution matrices with GPS trajectories through map-matching algorithms. These matrices can reflect the attributes related to the traffic status of road networks such as volume, speed and acceleration. Then, our multi-channel spatiotemporal network is used to efficiently combine three key attributes (volume, speed and acceleration) through the feature sharing mechanism and generate a precise prediction of them in the future period. Finally, we adopt an MOVES model to estimate vehicle emissions by integrating several traffic factors including the predicted traffic states, road networks and the statistical information of urban vehicles. We evaluate our model on the Xi’an taxi GPS trajectories dataset. Experiments show that our proposed network can effectively predict the temporal evolution of vehicle emissions.
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