中文摘要：

在大城市里可靠地在道路网络上估计即时旅行时间是一项紧迫的任务，尽管漂浮的汽车数据广泛地被用来反映真实交通。当前漂浮的汽车数据主要被用来在道路片断上估计即时交通条件，并且为拐弯延期评价做了很少。然而，道路交叉上的拐弯延期在现代城市里在道路网络上显著地作出贡献到全面旅行时间。在这篇论文，我们在场计算拐弯的一个技术框架与漂流汽车数据在道路网络上推迟。首先，与 GPS 收集的独创漂浮汽车数据装备了 taxies 基于 Hadoop 和 MongoDB 与一个分布式的系统被清洗并且匹配到一张街地图。第二，精制轨道数据集合在 96 次间隔之中被散布(从 0：00 ～ 23：59 ) 。所有与轨道片断轨道过去了的交叉被连接，并且组成了一件实验样品，当动脉的街上的交叉特殊被选择形成另一件实验样品时。第三，一个主要基于曲线的算法被介绍在给定的交叉估计拐弯延期。说服的算法统计上是不仅适合真实交通调节，而且对数据稀疏和失踪的数据问题感觉迟钝，它当前与收集技术的广泛地使用的漂浮的汽车数据是几乎不可避免的。我们采用了在 2011 在北京城市里从三月收集到 6 月的漂浮的汽车数据，它包含在数据体积为大约 600 GB 从大约 20000 辆装备 GPS 的出租车和说明产生的超过 260 万条轨道。结果显示出主要曲线我们介绍了的基于的算法在传统的方法上占主导地位，例如吝啬、中部的基于的途径，并且保持更高的评价精确性(大约 10%a [欧元] “ 15% 在 RMSE 更高) ，以及反映交通拥挤的变化趋势。与在交叉的旅行延期的评价结果，我们在三种次情形分析了拐弯延期的时间空间的分发(0：00a [欧元] “ 0：15， 8：15a [欧元] “ 8：30 和 12：00a [欧元] “ 12：15 ) 。它显示在一个在北京的单个旅行期间，道路网络上的一般水准 60% 旅行时间在

英文摘要：

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 intersec