Advances of positioning and wireless communication technologies make it possible to collect a large number of trajectory data of moving vehicles in a fast and convenient fashion. The data can be applied to various fields such as traffic study. In this paper, we attempt to derive average delay of traffic flow around intersections and verify the results with changes of time. The intersection zone is delineated first. Positioning points geographically located within this zone are selected, and then outliers are removed. Turn trips are extracted from selected trajectory data. Each trip, physically consisting of time-series positioning points, is identified with entry road segment and turning direction, i.e. target road segment. Turn trips are grouped into different categories according to their time attributes. Then, delay of each trip during a turn is calculated with its recorded speed. Delays of all trips in the same period of time are plotted to observe the change pattern of traffic conditions. Compared to conventional approaches, the proposed method can be applied to those intersections without fixed data collection devices such as loop detectors since a large number of trajectory data can always provide a more complete spatio-temporal picture of a road network. With respect to data availability, taxi trajectory data and an intersection in Shanghai are employed to test the proposed methodology. Results demonstrate its applicability.
Many studies have been carried out using vehicle trajectory to analyze traffic conditions, for instance, identifying traffic congestion. However, there is a lack of a systematic study on the appropriate number of probe vehicles and their sampling interval in order to identify traffic congestion accurately. Moreover, most of related studies ignore the streaming feature of trajectory data. This paper first represents a novel method of identifying traffic congestion considering the stream feature of vehicle trajectories. Instead of processing the whole data stream, a series of snapshots are extracted. Congested road segments can be identified by analyzing the clusters’ evolution among a series of adjacent snapshots. We then calculated a series of parameters and their corresponding congestion identification accuracy. The results have implications for related probe vehicle deployment and traffic analysis; for example, when 5% of probe vehicles are available, 85% identification accuracy can be reached if the sampling time interval is 10 s.
To understand the diversity of culturable fungi in soil at alpine sites,
A simple and accurate method to estimate evapotranspiration (ET) is essential for dynamic monitoring of the Earth system at a large scale. In this paper, we developed an artificial neural network (ANN) model forced by remote sensing and AmeriFlux data to estimate ET. First, the ANN was trained with ET measurements made at 13 AmeriFlux sites and land surface products derived from satellite remotely sensed data (normalized difference vegetation index, land surface temperature and surface net radiation) for the period 2002–2006. ET estimated with the ANN was then validated by ET observed at five AmeriFlux sites during the same period. The validation sites covered five different vegetation types and were not involved in the ANN training. The coefficient of determination (