Analyzing Spatiotemporal Road Congestion And Traffic Line Source Emissions Based On Floating Car Data

Analyzing spatiotemporal distribution patterns of urban road congestion and traffic line source emissions in depth is the key to realize sustainable development of urban transportation.Average speed and line source emissions of Shanghai urban road segments were calculated based on floating car data(FCD),and their distribution patterns as well as influence brought by built environment and land use were further analyzed.This study could lay the foundations for urban transportation planning and sustainable transportation development.First,the 24-hour average speed and hourly taxi flow rate of road segments were extracted based on GPS trajectory data provided by Qiangsheng Company so as to recognize the congestion pattern.The ratio of Qiangsheng taxis to the rest of passenger cars,buses and trucks were investigated according to the relative location to the expressway rings.Afterwards,the hourly NO_x emission factors of road segments were calculated based on traffic survey,emission standard distribution,and COPERT model.Fuzzy C-means clustering was applied to classify the road segments based on 24-hour speed vectors and emission vectors respectively,and the probabilistic results were used to identify the spatial distribution pattern and variation trend.Geographical Detector and MORAN’s I were used to verify the preliminary impact of built environment and land use on clustering results,and SARMA regression was further used to analyze the clustering results quantitatively to explain the cause of abnormal congestion and emission.According to this research,serious congestion always happens in city center.Road type,bus station density,ramps nearby,commercial land and so on would cause great impact on formation of congestion.Segments with high emission rates are concentrated between inner ring and middle ring,while relative location to city,residential land,commercial land,and road length play important roles in abnormal emission.This research discloses the influencing factors of road congestion and on-road emission,whose spatiotemporal distribution patterns were recognized through massive GPS data.The main innovation is to combine fuzzy clustering algorithm with spatial analysis so as to overcome the defect that most previous studies are confined to qualitative analysis.The results could assist to guide urban planning and transportation system management,avoiding the formation of congestion and high emission beforehand,and thus to realize future sustainable transportation development.