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Title:

Design and Analysis of Traffic Incident Detection Based on ADTree

Accession Number:

01627624

Record Type:

Component

Abstract:

For aim applied to develop Intelligent Transportation System (ITS), a traffic incident detection method based on Alternating decision tree (ADTree) algorithm is presented. Different from general decision tree, ADTree model is a decision tree algorithm based on boosting algorithm. ADTree model provide a mechanism for combining the weak hypotheses generated during boosting into a single interpretable representation. The detection performance of the ADTree was compared to multi-layer feed forward neural networks (MLFNN) and Radical Basis Function neural networks (RBFNN) which yield superior incident detection performance in the previous studies. The experimental results indicate that ADTree model is competitive with MLFNN and RBFNN.

Supplemental Notes:

This paper was sponsored by TRB committee ABJ70 Standing Committee on Artificial Intelligence and Advanced Computing Applications.

Monograph Accession #:

01618707

Report/Paper Numbers:

17-03100

Language:

English

Corporate Authors:

Transportation Research Board

500 Fifth Street, NW
Washington, DC 20001 United States

Authors:

Liu, Qingchao
Wong, Qianwen
Lu, Jian
Chen, Long
Jiang, Haobin
Chen, Shuyan

Pagination:

20p

Publication Date:

2017

Conference:

Transportation Research Board 96th Annual Meeting

Location: Washington DC, United States
Date: 2017-1-8 to 2017-1-12
Sponsors: Transportation Research Board

Media Type:

Digital/other

Features:

Figures; References; Tables

Subject Areas:

Data and Information Technology; Highways; Operations and Traffic Management

Source Data:

Transportation Research Board Annual Meeting 2017 Paper #17-03100

Files:

TRIS, TRB, ATRI

Created Date:

Dec 8 2016 11:09AM