Design and Application of Reduced Error Pruning Tree for Traffic Incident Detection

Qing Chao Liu; Jian Lu; Shu Yan Chen; Ya Ping Li; Xing Chen Yan

doi:10.4028/www.scientific.net/AMM.744-746.1931

Paper Titles

The Design of Closed-Loop Supply Chain Network
p.1910

The Research on Transportation Modes Combinatorial Optimization Model in Multimodal Transportation System
p.1915

Three Dimensional Offline Packing Optimization Problem Based on Genetic Simulated Annealing Algorithm
p.1919

Transportation Decision and Logistics Cost Analysis Based on Risk Sharing of Enterprises
p.1924

Design and Application of Reduced Error Pruning Tree for Traffic Incident Detection
p.1931

Evaluation and Modeling of Passenger Vehicle Celeration Performance Based on Artificial Neural Networks
p.1938

Low-Tech Urban Solutions in Data-Based Environment – A New Concept of Sharing-Oriented Metropolitan Transportation
p.1943

The Analysis of Cars and Pedestrians Collision Based on the PC-Crash Program and MADYMO Simulation
p.1949

The Prediction of Vehicle Collision Risk in Traffic Conflict Zone Based on Bayesian Network
p.1953

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 744-746Design and Application of Reduced Error Pruning...

Design and Application of Reduced Error Pruning Tree for Traffic Incident Detection

Abstract:

For aim applied to develop Intelligent Transportation System (ITS), a traffic incident detection method based on Reduced Error Pruning Tree (REPTree) algorithm of decision tree is presented. Different from unpruned decision tree, REPTree model is a fast decision tree learner which builds a decision tree using information gain as the splitting criterion, and prunes it using reduced-error pruning. The detection performance of the REPTree 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 REPTree model is competitive with MLFNN and RBFNN.

You might also be interested in these eBooks

Advances in Civil Engineering and Transportation IV

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 744-746)

Pages:

1931-1937

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.744-746.1931

Citation:

Cite this paper

Online since:

March 2015

Authors:

Qing Chao Liu, Jian Lu*, Shu Yan Chen, Ya Ping Li, Xing Chen Yan

Keywords:

Decision Tree, ITS, Reduced Error Pruning Tree, Traffic Incident Detection

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] Y. -S. Jeong, Manoel C. -N.: A wavelet-based freeway incident detection algorithm with adapting threshold parameters. Transportation Research Part C: Emerging Technologies, 2011, 19, 1-19.

DOI: 10.1016/j.trc.2009.10.005

Google Scholar

[2] Payne H.J. andTignor S.C.: Freewayincident-detectionalgorithmsbasedondecisiontreeswithstates. TransportationResearchRecord, No. 682, TRB, NationalResearchCouncil, pp.30-37.

Google Scholar

[3] Cheu,R., Ritchie,S., Investigationofaneuralnetworkmodelf or freeway incidentdetection, Transportation Research Board 92ndAnnual Meeting, WashingtonDC, USA, (1991).

Google Scholar

[4] Srinivasan,D., Cheu,R. Developmentofanintelligenttechniquefortrafficnetworkincidentdetection, EngineeringApplications ofArtificial Intelligence, 2000, 13(3): 311-322.

Google Scholar

[5] AhmadlouM., AdeliH. Enhancedprobabilisticneural networkwithlocaldecisioncircles: Arobust classifier, Integrated Computer-AidedEngineering, 2010, 17(3): 197-210.

Google Scholar

[6] LiuQ., LuJ., TrafficIncidentDetectionUsingRandomForest, TransportationResearchBoard92ndAnnual Meeting, Washington DC, USA, (2013).

Google Scholar

[7] Han,J. W. (2004). Micheline Kamber. Datdy. ROCAnalysisin ArtiﬁcialIntelligence. InFirstinternational workshop(p.61–70), ROCAI-2004, Spain, August22.

Google Scholar

[8] Catlett,J. (1991). Overpruninglarge decisiontrees. Inproceedingsofthetwelfthinternational joint conference on artificialintelligence, pp.764-769, SanMateo, CA. MorganKaufmann.

Google Scholar

[9] John Mingers. (1989), An Empirical Comparison of Pruning Methods for Decision TreeInduction, Machine Learning, 4, 227-243.

Google Scholar

[10] JeffreyP. Bradford, ClaytonKunz, Pruningdecisiontreeswithmisclassificationcosts, pp.131-136.

Google Scholar

[11] Quinlan, J.R. (1987). Simplifyingdecisiontrees. InternationalJournalof Man-Machine Studies, 27(3), 221-248.

Google Scholar

[12] Breiman,L., Freidman,J., Olshen,R., andStone,C. (1984). Classificationandregressiontrees. California: WadsworthInternational.

Google Scholar