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1. 发明授权

US09286793B2 Traffic prediction using real-world transportation data 有权
标题翻译：使用现实世界交通数据的交通预测
公开(公告)号：US09286793B2
公开(公告)日：2016-03-15
申请号：US14060360
申请日：2013-10-22
申请人： Bei Pan , Ugur Demiryurek , Cyrus Shahabi
发明人： Bei Pan , Ugur Demiryurek , Cyrus Shahabi
IPC分类号： G08G1/00 , G08G1/01
CPC分类号： G06N5/04 , G08G1/00 , G08G1/0112 , G08G1/0129 , G08G1/0141
摘要： Real-time high-fidelity spatiotemporal data on transportation networks can be used to learn about traffic behavior at different times and locations, potentially resulting in major savings in time and fuel. Real-world data collected from transportation networks can be used to incorporate the data's intrinsic behavior into a time-series mining technique to enhance its accuracy for traffic prediction. For example, the spatiotemporal behaviors of rush hours and events can be used to perform a more accurate prediction of both short-term and long-term average speed on road-segments, even in the presence of infrequent events (e.g., accidents). Taking historical rush-hour behavior into account can improve the accuracy of traditional predictors by up to 67% and 78% in short-term and long-term predictions, respectively. Moreover, the impact of an accident can be incorporated to improve the prediction accuracy by up to 91%.
摘要翻译：运输网络上的实时高保真时空数据可用于了解不同时间和地点的交通行为，有可能大大节省时间和燃料。从运输网络收集的现实数据可用于将数据的内在行为纳入时间序列挖掘技术，以提高其对流量预测的准确性。例如，高峰时段和事件的时空行为可以用于对路段上的短期和长期平均速度进行更准确的预测，即使在出现偶然事件（例如事故）的情况下也是如此。考虑到历史高峰时期的行为，可以将传统预测指标的准确度分别提高67％和78％，在短期和长期预测中。此外，可以引入事故的影响，将预测精度提高高达91％。

2. 发明申请

US20140114556A1 TRAFFIC PREDICTION USING REAL-WORLD TRANSPORTATION DATA 有权
标题翻译：使用实际运输数据的交通预测
公开(公告)号：US20140114556A1
公开(公告)日：2014-04-24
申请号：US14060360
申请日：2013-10-22
申请人： Bei Pan , Ugur Demiryurek , Cyrus Shahabi
发明人： Bei Pan , Ugur Demiryurek , Cyrus Shahabi
IPC分类号： G08G1/00
CPC分类号： G06N5/04 , G08G1/00 , G08G1/0112 , G08G1/0129 , G08G1/0141
摘要： Real-time high-fidelity spatiotemporal data on transportation networks can be used to learn about traffic behavior at different times and locations, potentially resulting in major savings in time and fuel. Real-world data collected from transportation networks can be used to incorporate the data's intrinsic behavior into a time-series mining technique to enhance its accuracy for traffic prediction. For example, the spatiotemporal behaviors of rush hours and events can be used to perform a more accurate prediction of both short-term and long-term average speed on road-segments, even in the presence of infrequent events (e.g., accidents). Taking historical rush-hour behavior into account can improve the accuracy of traditional predictors by up to 67% and 78% in short-term and long-term predictions, respectively. Moreover, the impact of an accident can be incorporated to improve the prediction accuracy by up to 91%.
摘要翻译：运输网络上的实时高保真时空数据可用于了解不同时间和地点的交通行为，有可能大大节省时间和燃料。从运输网络收集的现实数据可用于将数据的内在行为纳入时间序列挖掘技术，以提高其对流量预测的准确性。例如，高峰时段和事件的时空行为可以用于对路段上的短期和长期平均速度进行更准确的预测，即使在出现偶然事件（例如事故）的情况下也是如此。考虑到历史高峰时期的行为，可以将传统预测指标的准确度分别提高67％和78％，在短期和长期预测中。此外，可以引入事故的影响，将预测精度提高高达91％。

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