It is a pressing task to estimate the real-time travel time on road networks reliably in big cities, even though floating car data has been widely used to reflect the real traffic. Currently floating car data are main...It is a pressing task to estimate the real-time travel time on road networks reliably in big cities, even though floating car data has been widely used to reflect the real traffic. Currently floating car data are mainly used to estimate the real-time traffic conditions on road segments, and has done little for turn delay estimation. However, turn delays on road intersections contribute significantly to the overall travel time on road networks in modem cities. In this paper, we present a technical framework to calculate the turn delays on road networks with float car data. First, the original floating car data collected with GPS equipped taxies was cleaned and matched to a street map with a distributed system based on Hadoop and MongoDB. Secondly, the refined trajectory data set was distributed among 96 time intervals （from 0：00 to 23： 59）. All of the intersections where the trajectories passed were connected with the trajectory segments, and constituted an experiment sample, while the intersections on arterial streets were specially selected to form another experiment sample. Thirdly, a principal curve-based algorithm was presented to estimate the turn delays at the given intersections. The algorithm argued is not only statistically fitted the real traffic conditions, but also is insensitive to data sparseness and missing data problems, which currently are almost inevitable with the widely used floating car data collecting technology. We adopted the floating car data collected from March to June in Beijing city in 2011, which contains more than 2.6 million trajectories generated from about 20000 GPS-equipped taxicabs and accounts for about 600 GB in data volume. The result shows the principal curve based algorithm we presented takes precedence over traditional methods, such as mean and median based approaches, and holds a higher estimation accuracy （about 10%-15% higher in RMSE）, as well as reflecting the changing trend of traffic congestion. With the estimation result for the travel delay at intersections, we analyzed the spatio-temporal distribution of turn delays in three time scenarios （0： 00-0： 15, 8： 15-8：30 and 12： 00-12： 15）. It indicates that during one＇s single trip in Beijing, average 60% of the travel time on the road networks is wasted on the intersections, and this situation is even worse in daytime. Although the 400 main intersections take only 2.7% of all the intersections, they occupy about 18% travel time.展开更多
Vehicles have been increasingly equipped with GPS receivers to record their trajectories,which we call floating car data.Compared with other data sources,these data are characterized by low cost,wide coverage,and rapi...Vehicles have been increasingly equipped with GPS receivers to record their trajectories,which we call floating car data.Compared with other data sources,these data are characterized by low cost,wide coverage,and rapid updating.The data have become an important source for road network extraction.In this paper,we propose a novel approach for mining road networks from floating car data.First,a Gaussian model is used to transform the data into bitmap,and the Otsu algorithm is utilized to detect road intersections.Then,a clothoid-based method is used to resample the GPS points to improve the clustering accuracy,and the data are clustered based on a distance-direction algorithm.Last,road centerlines are extracted with a weighted least squares algorithm.We report on experiments that were conducted on floating car data from Wuhan,China.To conclude,existing methods are compared with our method to prove that the proposed method is practical and effective.展开更多
为了消除跟驰模型用于交通排放测算产生的系统误差,提高微观交通仿真模型与交通排放模型融合应用于交通排放测算的准确性,引入衡量加速度变化率的参数急动度(jerk),提出一种考虑jerk分布特征的Wiedemann跟驰模型优化方法。通过对实际轨...为了消除跟驰模型用于交通排放测算产生的系统误差,提高微观交通仿真模型与交通排放模型融合应用于交通排放测算的准确性,引入衡量加速度变化率的参数急动度(jerk),提出一种考虑jerk分布特征的Wiedemann跟驰模型优化方法。通过对实际轨迹数据与车辆跟驰数据的拟合分析,建立各加速度区间下的jerk分布,并增设jerk约束对交通仿真模型生成轨迹进行优化。以北京市出租车轨迹数据和跟驰数据为例,测算结果表明:Wiedemann模型优化后,不同速度区间的车辆比功率(Vehicle Specific Power,VSP)分布误差平均降低1.2%,CO_(2),CO,THC,NOX等4种排放物的排放因子平均误差分别降低了16.9%,118.3%,27.0%,20.5%,表明该优化方法能够有效改善原模型中不真实的加速度,降低排放测算误差。展开更多
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 prob...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.展开更多
基金This research was supported by the National Natural Science Foundation of China （Grant No. 41271408）, the National Hi-tech Research and Development Program of China （No. 2012AA12A211） and State Key Laboratory of Resources and Environmental Information System Open Foundation （No. 088RA500KA）. And we also thank the anonymous referees for their helpful comments and suggestions.
文摘It is a pressing task to estimate the real-time travel time on road networks reliably in big cities, even though floating car data has been widely used to reflect the real traffic. Currently floating car data are mainly used to estimate the real-time traffic conditions on road segments, and has done little for turn delay estimation. However, turn delays on road intersections contribute significantly to the overall travel time on road networks in modem cities. In this paper, we present a technical framework to calculate the turn delays on road networks with float car data. First, the original floating car data collected with GPS equipped taxies was cleaned and matched to a street map with a distributed system based on Hadoop and MongoDB. Secondly, the refined trajectory data set was distributed among 96 time intervals （from 0：00 to 23： 59）. All of the intersections where the trajectories passed were connected with the trajectory segments, and constituted an experiment sample, while the intersections on arterial streets were specially selected to form another experiment sample. Thirdly, a principal curve-based algorithm was presented to estimate the turn delays at the given intersections. The algorithm argued is not only statistically fitted the real traffic conditions, but also is insensitive to data sparseness and missing data problems, which currently are almost inevitable with the widely used floating car data collecting technology. We adopted the floating car data collected from March to June in Beijing city in 2011, which contains more than 2.6 million trajectories generated from about 20000 GPS-equipped taxicabs and accounts for about 600 GB in data volume. The result shows the principal curve based algorithm we presented takes precedence over traditional methods, such as mean and median based approaches, and holds a higher estimation accuracy （about 10%-15% higher in RMSE）, as well as reflecting the changing trend of traffic congestion. With the estimation result for the travel delay at intersections, we analyzed the spatio-temporal distribution of turn delays in three time scenarios （0： 00-0： 15, 8： 15-8：30 and 12： 00-12： 15）. It indicates that during one＇s single trip in Beijing, average 60% of the travel time on the road networks is wasted on the intersections, and this situation is even worse in daytime. Although the 400 main intersections take only 2.7% of all the intersections, they occupy about 18% travel time.
基金supported by the Joint Fund for Innovation and Development of Automobile Industry of National Natural Science Foundation of China[Grant Number U1764262]the National Natural Science Foundation of China[Grant Number 42101448].
文摘Vehicles have been increasingly equipped with GPS receivers to record their trajectories,which we call floating car data.Compared with other data sources,these data are characterized by low cost,wide coverage,and rapid updating.The data have become an important source for road network extraction.In this paper,we propose a novel approach for mining road networks from floating car data.First,a Gaussian model is used to transform the data into bitmap,and the Otsu algorithm is utilized to detect road intersections.Then,a clothoid-based method is used to resample the GPS points to improve the clustering accuracy,and the data are clustered based on a distance-direction algorithm.Last,road centerlines are extracted with a weighted least squares algorithm.We report on experiments that were conducted on floating car data from Wuhan,China.To conclude,existing methods are compared with our method to prove that the proposed method is practical and effective.
文摘为了消除跟驰模型用于交通排放测算产生的系统误差,提高微观交通仿真模型与交通排放模型融合应用于交通排放测算的准确性,引入衡量加速度变化率的参数急动度(jerk),提出一种考虑jerk分布特征的Wiedemann跟驰模型优化方法。通过对实际轨迹数据与车辆跟驰数据的拟合分析,建立各加速度区间下的jerk分布,并增设jerk约束对交通仿真模型生成轨迹进行优化。以北京市出租车轨迹数据和跟驰数据为例,测算结果表明:Wiedemann模型优化后,不同速度区间的车辆比功率(Vehicle Specific Power,VSP)分布误差平均降低1.2%,CO_(2),CO,THC,NOX等4种排放物的排放因子平均误差分别降低了16.9%,118.3%,27.0%,20.5%,表明该优化方法能够有效改善原模型中不真实的加速度,降低排放测算误差。
文摘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.
