NDLI: Speed pattern recognition technique for short-term traffic forecasting based on traffic dynamics

Content Provider	IET Digital Library
Author	Kehagias, Dionysios Salamanis, Athanasios Tzovaras, Dimitrios
Abstract	This study introduces a new short-term traffic forecasting technique, based on the dynamic features of traffic data derived from vehicles moving in urban networks. The authors goal is to forecast the values of appropriate traffic status indicators such as average travel time or speed, for one or more time steps in the future until the next half hour. The proposed forecasting technique is based on road profiles generated from the application of data clustering techniques on real traffic data. Data clustering is applied after the original feature space is transformed to a new one of a significantly lower dimension. This transformation is based on the dynamic characteristics of current traffic, expressed in the form of the speed derivatives. To evaluate the proposed technique they used two-week historical data from the city of Berlin, Germany. Extensive evaluation results indicate improvement of the forecasting accuracy after comparison with a set of existing traffic forecasting techniques.
Starting Page	646
Ending Page	653
Page Count	8
ISSN	1751956X
Volume Number	9
e-ISSN	17519578
Issue Number	Issue 6, Aug (2015)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-its/9/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-its.2014.0213
Journal	IET Intelligent Transport Systems
Publisher Date	2015-06-25
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Average Speed Average Travel Time Berlin City Data Clustering Technique Data Handling Technique Dynamic Traffic Data Features Forecasting Theory Germany Intelligent Transport System Pattern Clustering Road Profile Road Traffic Robust Advanced Traffic Management Short-term Traffic Forecasting Technique Speed Pattern Recognition Technique Traffic Dynamics Traffic Engineering Computing Traffic Information System Traveller Information System Urban Network
Content Type	Text
Resource Type	Article
Subject	Law Transportation Environmental Science Mechanical Engineering

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