FCG-ASpredictor: An Approach for the Prediction of Average Speed of Road Segments with Floating Car GPS Data

The average speed (AS) of a road segment is an important factor for predicting traffic congestion, because the accuracy of AS can directly affect the implementation of traffic management. The traffic environment, spatiotemporal information, and the dynamic interaction between these two factors impact the predictive accuracy of AS in the existing literature, and floating car data comprehensively reflect the operation of urban road vehicles. In this paper, we proposed a novel road segment AS predictive model, which is based on floating car data. First, the impact of historical AS, weather, and date attributes on AS prediction has been analyzed. Then, through spatiotemporal correlations calculation based on the data from Global Positioning System (GPS), the predictive method utilizes the recursive least squares method to fuse the historical AS with other factors (such as weather, date attributes, etc.) and adopts an extended Kalman filter algorithm to accurately predict the AS of the target segment. Finally, we applied our approach on the traffic congestion prediction on four road segments in Chengdu, China. The results showed that the proposed predictive model is highly feasible and accurate. Document type: Articl

The fallacy of profitable green supply chains: The role of green information systems in attenuating the sustainability trade-offs

While green supply chain management (GSCM) has been studied extensively, a lack of a clear view on performance improvements arising from the adoption of GSCM practices obstructs a full understanding of resultant consequences. Moreover, there are still limited efforts to understand the contingent nature of how performance is improved in this context. This study aims to ascertain whether the GSCM implementation yields sustainability–profitability trade-offs and examine the moderating effects of green information systems (GIS) on performance improvements. Survey data were collected from 189 firms operating in the UK automotive industry and analyzed using moderated hierarchical regression. The results suggest that pursuing GSCM can bring trade-offs into play, demonstrating a paradoxical view of enhanced sustainability versus less profitability. The authors call this phenomenon the fallacy of profitable GSCM. Interestingly, high levels of GIS were found to positively moderate the relationships between GSCM practices and economic performance. This study contributes to the knowledge bank of GSCM by elucidating the mixed views about the GSCM adoption and its economic effects and refutes the fallacy that “low-hanging fruits” of GSCM are readily available. Second, this study offers new directions to balance the trade-offs between sustainability and profitability, contributing to the development of a more robust GSCM theory. Two important managerial contributions can be drawn from this study: (1) managers need to prioritize GSCM practices on the basis of having the most significant performance improvement; (2) they are encouraged to develop more robust GIS and exploit the capabilities of information sharing, supply chain traceability, and monitoring as a new pathway to attenuate sustainability trade-offs. Future studies are recommended to explore wider sectors and employ longitudinal or quasi-experimental designs to capture the effects of GSCM practices on performance over time

Chemical compositions of fog and precipitation at Sejila Mountain in the southeast Tibetan Plateau, China

Chemical compositions of fog and rain water were measured between July 2017 and September 2018 at Sejila Mountain, southeast Tibet, where fog events frequently occurred in original fir forests at altitude 3950 m. Fog water samples were collected using a Caltech Active Strand Cloud Collector (CASCC), and rain samples were collected using a precipitation gauge. Differences were observed between fog water and rain composition for most analyzed ions. Ion abundance in fog water was Ca2+ > Cl− > Na+ > SO42− > Mg2+ > NH4+ >K+ > NO3− whereas an order of Ca2+ > Na+ > Cl− > Mg2+ > SO42− > NO3− > K+ > NH4+ was observed for rain water. All ion concentrations were higher in fog water than in rain water. Additionally, Ca2+ was the dominant cation in both fog and rain samples, accounting for more than half of all measured cations. NH4+ and SO42− concentrations were notable for being higher in fog than rain water when compared with other ions. For trace elements, Al, As, Mn and Se were the most abundant elements in fog water; only Al and As were detected in rain water. Seventy-two hour back-trajectory analysis showed that air masses during fog and/or rain events mainly came from the south of Sejila Mountain. Spearman correlation analysis and source contribution calculations indicated that both marine and terrestrial sources contributed to the observed ion concentrations. Considering the higher concentrations of NH4+ and higher ratio of NH4+/NO3− measured in fog than in rain, we suggest that quantification of fog nitrogen deposition and its ecological effect in this area should be given more attention

Boundary-Layer Characteristics of Persistent Regional Haze Events and Heavy Haze Days in Eastern China

This paper analyzed the surface conditions and boundary-layer climate of regional haze events and heavy haze in southern Jiangsu Province in China. There are 5 types with the surface conditions which are equalized pressure (EQP), the advancing edge of a cold front (ACF), the base of high pressure (BOH), the backside of high pressure (BAH), the inverted trough of low pressure (INT), and saddle pressure (SAP) with the haze days. At that time, 4 types are divided with the regional haze events and each of which has a different boundary-layer structure. During heavy haze, the surface mainly experiences EQP, ACF, BOH, BAH, and INT which also have different boundary-layer structures

Quantitative Assessment of Desertification Using Landsat Data on a Regional Scale – A Case Study in the Ordos Plateau, China

Desertification is a serious threat to the ecological environment and social economy in our world and there is a pressing need to develop a reasonable and reproducible method to assess it at different scales. In this paper, the Ordos Plateau in China was selected as the research region and a quantitative method for desertification assessment was developed by using Landsat MSS and TM/ETM+ data on a regional scale. In this method, NDVI, MSDI and land surface albedo were selected as assessment indicators of desertification to represent land surface conditions from vegetation biomass, landscape pattern and micrometeorology. Based on considering the effects of vegetation type and time of images acquired on assessment indictors, assessing rule sets were built and a decision tree approach was used to assess desertification of Ordos Plateau in 1980, 1990 and 2000. The average overall accuracy of three periods was higher than 90%. The results showed that although some local places of Ordos Plateau experienced an expanding trend of desertification, the trend of desertification of Ordos Plateau was an overall decrease in from 1980 to 2000. By analyzing the causes of desertification processes, it was found that climate change could benefit for the reversion of desertification from 1980 to 1990 at a regional scale and human activities might explain the expansion of desertification in this period; however human conservation activities were the main driving factor that induced the reversion of desertification from 1990 to 2000