Predicting vehicle fuel consumption patterns using floating vehicle data.

The status of energy consumption and air pollution in China is serious. It is important to analyze and predict the different fuel consumption of various types of vehicles under different influence factors. In order to fully describe the relationship between fuel consumption and the impact factors, massive amounts of floating vehicle data were used. The fuel consumption pattern and congestion pattern based on large samples of historical floating vehicle data were explored, drivers' information and vehicles' parameters from different group classification were probed, and the average velocity and average fuel consumption in the temporal dimension and spatial dimension were analyzed respectively. The fuel consumption forecasting model was established by using a Back Propagation Neural Network. Part of the sample set was used to train the forecasting model and the remaining part of the sample set was used as input to the forecasting model.

Urban air quality, meteorology and traffic linkages: Evidence from a sixteen-day particulate matter pollution event in December 2015, Beijing.

A heavy 16-day pollution episode occurred in Beijing from December 19, 2015 to January 3, 2016. The mean daily AQI and PM2.5 were 240.44 and 203.6µg/m3. We analyzed the spatiotemporal characteristics of air pollutants, meteorology and road space speed during this period, then extended to reveal the combined effects of traffic restrictions and meteorology on urban air quality with observational data and a multivariate mutual information model. Results of spatiotemporal analysis showed that five pollution stages were identified with remarkable variation patterns based on evolution of PM2.5 concentration and weather conditions. Southern sites (DX, YDM and DS) experienced heavier pollution than northern ones (DL, CP and WL). Stage P2 exhibited combined functions of meteorology and traffic restrictions which were delayed peak-clipping effects on PM2.5. Mutual information values of Air quality-Traffic-Meteorology (ATM-MI) revealed that additive functions of traffic restrictions, suitable relative humidity and temperature were more effective on the removal of fine particles and CO than NO2.

Identifying regional service function from PM2.5 mass concentration throughout a city with non-negative tensor factorization approach.

This paper examines the holistic viewpoint on pollution pattern from time, day, and region dimensions based on the public data of fine particle concentrations, which cover 35 ambient monitoring stations in Beijing firstly. According to data driven, non-negative tensor factorization (NTF) method is introduced to distinguish pollution patterns which could identify the area service function. Results show that five patterns are obtained and annotated as traffic, industrial, residential, commercial, and steady ones. Each type owns special characteristics on time basis or day basis. Furthermore, calculating the reconstruction correlation of tensors with respect to sites, time, and days approximately approaches to 0.95-0.96, and it can be employed with high evaluation values of the model. Comparing with the original classifications drew by land use, this method corresponds with the reality well for considering the changes of surrounding sources. Some commendations on public travel and controlling measures based on local pollution presented in this study can be provided for further decrease of fine particle and improvement of air quality.