Data-oriented intelligent transportation systems

Author

Ibrahim, Hamdy ; Far, Behrouz H.

Author_Institution

Dept. of Electr. & Comput. Eng., Univ. of Calgary, Calgary, AB, Canada

fYear

2014

Firstpage

322

Lastpage

329

Abstract

Real-time analysis of traffic data is a key challenge in intelligent transportation system. It aims at discovering useful traffic patterns that can help decision makers better manage the transportation system and test and introduce new policies. Discovered patterns can also be used to support road users to reach their destination safely and with reasonable commuting time. In this paper, a number of key challenges associated with transportation systems and possible solutions are discussed. A method that analyzes real-time traffic data to predict future status of traffic flow and incidents is introduced. The proposed method includes three phases: offline, real-time, and decision support phases. In this paper, a decision tree classification model is constructed and validated for an accident dataset. Possible benefits of using the constructed model are demonstrated using results of the classification analysis.

Keywords

data analysis; data mining; decision trees; intelligent transportation systems; pattern classification; traffic engineering computing; accident dataset; classification analysis; data-oriented intelligent transportation system; decision support phase; decision tree classification model; offline phase; realtime phase; realtime traffic data analysis; traffic pattern discovery; Accidents; Data mining; Decision trees; Real-time systems; Roads; Vehicles; Intelligent transportation systems; clustering; traffic data analysis; traffic patterns;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Reuse and Integration (IRI), 2014 IEEE 15th International Conference on

Type

conf

DOI

10.1109/IRI.2014.7051907

Filename

7051907