A Data-Driven Approach for Travel Time Prediction on Motorway Sections

被引：0
|
作者：
Heilmann, B. [1 ]
Koller, H. [1 ]
Asamer, J. [1 ]
Reinthaler, M. [1 ]
Aleksa, M. [1 ]
Breuss, S. [1 ]
Richter, G. [1 ]
机构：
[1] AIT Austrian Inst Technol, Vienna, Austria
来源：
2014 INTERNATIONAL CONFERENCE ON CONNECTED VEHICLES AND EXPO (ICCVE) | 2014年
关键词：
travel time prediction; traffic patterns;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In the presented case study, travel times for passenger cars (PC) and heavy goods vehicles (HGV) were predicted with a data-driven, hybrid approach, using historical traffic data of the entire high-ranking Austrian road network. In case flow data were available, travel time was predicted with a Kernel predictor searching for similar speed-density patterns. In case of missing flow data, travel time was predicted with deviations from typical historical speed time series. The performed steps in pre-processing traffic data, the hybrid prediction method as well as the results for selected road sections are described and analysed.
引用
收藏
页码：505 / 506
页数：2
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