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Cheng, Wei
Publications (2 of 2) Show all publications
Sun, B., Cheng, W., Goswami, P. & Bai, G. (2018). Short-Term Traffic Forecasting Using Self-Adjusting k-Nearest Neighbours. IET Intelligent Transport Systems, 12(1), 41-48
Open this publication in new window or tab >>Short-Term Traffic Forecasting Using Self-Adjusting k-Nearest Neighbours
2018 (English)In: IET Intelligent Transport Systems, ISSN 1751-956X, E-ISSN 1751-9578, Vol. 12, no 1, p. 41-48Article in journal (Refereed) Published
Abstract [en]

Short-term traffic forecasting is becoming more important in intelligent transportation systems. The k-nearest neighbours (kNN) method is widely used for short-term traffic forecasting.However, kNN parameters self-adjustment has been a problem due to dynamic traffic characteristics. This paper proposes a fully automatic dynamic procedure kNN (DP-kNN) that makes the kNN parameters self-adjustable and robust without predefined models or training. We used realworld data with more than one-year traffic records to conduct experiments. The results show that DP-kNN can perform better than manually adjusted kNN and other benchmarking methods with regards to accuracy on average. This study also discusses the difference between holiday and workday traffic prediction as well as the usage of neighbour distance measurement.

Place, publisher, year, edition, pages
Institution of Engineering and Technology, 2018
intelligent transportation systems; short-term traffic forecasting; road traffic; DP-kNN; dynamic procedure kNN; self-adjusting k-nearest neighbours
National Category
Computer Sciences Transport Systems and Logistics
urn:nbn:se:bth-15727 (URN)10.1049/iet-its.2016.0263 (DOI)000426045200006 ()
Available from: 2018-01-09 Created: 2018-01-09 Last updated: 2018-11-01Bibliographically approved
Sun, B., Cheng, W., Bai, G. & Goswami, P. (2017). Correcting and complementing freeway traffic accident data using mahalanobis distance based outlier detection. Technical Gazette, 24(5), 1597-1607
Open this publication in new window or tab >>Correcting and complementing freeway traffic accident data using mahalanobis distance based outlier detection
2017 (English)In: Technical Gazette, ISSN 1330-3651, E-ISSN 1848-6339, Vol. 24, no 5, p. 1597-1607Article in journal (Refereed) Published
Abstract [en]

A huge amount of traffic data is archived which can be used in data mining especially supervised learning. However, it is not being fully used due to lack of accurate accident information (labels). In this study, we improve a Mahalanobis distance based algorithm to be able to handle differential data to estimate flow fluctuations and detect accidents and use it to support correcting and complementing accident information. The outlier detection algorithm provides accurate suggestions for accident occurring time, duration and direction. We also develop a system with interactive user interface to realize this procedure. There are three contributions for data handling. Firstly, we propose to use multi-metric traffic data instead of single metric for traffic outlier detection. Secondly, we present a practical method to organise traffic data and to evaluate the organisation for Mahalanobis distance. Thirdly, we describe a general method to modify Mahalanobis distance algorithms to be updatable. © 2017, Strojarski Facultet. All rights reserved.

Place, publisher, year, edition, pages
Strojarski Facultet, 2017
Accident data, Data labelling, Differential distance, Mahalanobis distance, Outlier detection, Traffic data, Updatable algorithm, Accidents, Data mining, Statistics, User interfaces, Mahalanobis distances, Data handling
National Category
Communication Systems Computer and Information Sciences
urn:nbn:se:bth-15472 (URN)10.17559/TV-20150616163905 (DOI)000417100300037 ()2-s2.0-85032512786 (Scopus ID)

Funded by National Natural Science Foundation of China

Funding nr. 61364019

Available from: 2017-11-10 Created: 2017-11-10 Last updated: 2018-11-01Bibliographically approved

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