An improved k-nearest neighbours method for traffic time series imputation

Sun, Bin; Ma, Liyao; Cheng, Wei; Wei, Wen; Goswami, Prashant; Bai, Guohua

doi:10.1109/cac.2017.8244105

Cited by 38 publications

(13 citation statements)

References 29 publications

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“…The type of day (holidays versus workdays) matters, but how to make use of the differences from the traffic flow itself without manual work is a challenge. A better way to handle the missing data, such as imputation [56], should be considered. These topics will be investigated in our future work.…”

Section: Discussionmentioning

confidence: 99%

Short‐term traffic forecasting using self‐adjusting k‐nearest neighbours

Sun

Cheng

Goswami

et al. 2017

IET Intelligent Transport Systems

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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, the self-adjustment of kNN parameters 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 for the parameters. A real-world dataset with more than one year traffic records is used to conduct experiments. The results show that DP-kNN can perform better than manually adjusted kNN and other benchmarking methods in terms of accuracy on average. This study also discusses the difference between holiday and workday traffic prediction as well as the usage of neighbour distance measurement.

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Section: Discussionmentioning

confidence: 99%

Short‐term traffic forecasting using self‐adjusting k‐nearest neighbours

Sun

Cheng

Goswami

et al. 2017

IET Intelligent Transport Systems

Self Cite

View full text Add to dashboard Cite

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“…The other weakness of KNN imputation is that it searches through all the data set, hence increasing computational time [67]. However, there are approaches in literature that have been developed to improve the KNN imputation algorithm for missing values problems, see [68][69][70][71][72][73].…”

Section: K Nearest Neighbour Classificationmentioning

confidence: 99%

A Survey On Missing Data in Machine Learning

Emmanuel

Maupong

Mpoeleng

et al. 2021

Preprint

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Machine learning has been the corner stone in analysing and extracting information from data and often a problem of missing values is encountered. Missing values occur as a result of various factors like missing completely at random, missing at random or missing not at random. All these may be as a result of system malfunction during data collection or human error during data pre-processing. Nevertheless, it is important to deal with missing values before analysing data since ignoring or omitting missing values may result in biased or misinformed analysis. In literature there have been several proposals for handling missing values. In this paper we aggregate some of the literature on missing data particularly focusing on machine learning techniques. We also give insight on how the machine learning approaches work by highlighting the key features of the proposed techniques, how they perform, their limitations and the kind of data they are most suitable for. Finally, we experiment on the K nearest neighbor and random forest imputation techniques on novel power plant induced fan data and offer some possible future research direction.

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“…However, because of various reasons such as limited sensor coverage area, sensor failure or transmission error, it is common that some traffic flow data are lost. Many data mining methods were proposed using a wide spectrum of techniques to estimate missing traffic data [27] such as k-nearest neighbors method [28], artificial neural networks [29], adaptive rolling smoothing (ARS) approach by dynamically tuning the filter parameters in a rolling horizon scheme for online applications [30], and analyzing spatio-temporal characteristics of traffic flow and the spatial location of road segments [31].…”

Section: Related Workmentioning

confidence: 99%

Mining Urban Traffic Condition from Crowd-Sourced Data

et al. 2020

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Traffic congestion is an inherent and hard issue to be tackled in huge urban areas, particularly in developing countries where transportation infrastructures have not been grown well to fulfill speedy developing request demands. This paper proposes novel solutions to these issues by devising mobile crowd-sourcing based approaches to traffic estimation. A framework for effective collecting, integrating and analyzing traffic-related data shared by mobile crowds has been devised. Besides, essential issues on predicting traffic conditions at streets where real-time data is missed are also resolved by applying data mining techniques to historical data. A prototype system has been developed to validate the proposed solutions. The experimental results show the feasibility and the effectiveness of the proposed methods revealing that they are ready to be applied in the practice.

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An improved k-nearest neighbours method for traffic time series imputation

Cited by 38 publications

References 29 publications

Short‐term traffic forecasting using self‐adjusting k‐nearest neighbours

Short‐term traffic forecasting using self‐adjusting k‐nearest neighbours

A Survey On Missing Data in Machine Learning

Mining Urban Traffic Condition from Crowd-Sourced Data

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