Detection of Invalid Loop Detector Data in Urban Areas

Weijermars, Wendy; Berkum, Eric C. van

doi:10.1177/0361198106194500111

Cited by 8 publications

(3 citation statements)

References 11 publications

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“…For these links n = 48. The volume measurements were inspected and invalid data were rejected by Weijermars and Van Berkum (2006). Invalid data are the result of errors in the measurements (e.g.…”

Section: Discussionmentioning

confidence: 99%

Variations in urban traffic volumes

Thomas

Weijermars

Berkum

2008

European Journal of Transport and Infrastructure Research

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This paper describes the variation in traffic volumes in the Dutch city of Almelo. For this study, we used traffic counts which were collected at about 20 intersections from September 2004 until September 2005. The objective of this study is to provide insight in both periodic and random variation in urban traffic volumes. This insight is used by policy makers and traffic managers to determine a suitable set of traffic measures. It is also a basis for short term predictions of volumes. We conclude that we can distinguish the following types of variation with respect to the average daily volume profile. (1) Seasonal variations with time-scales of weeks and weekly variations that describe the variation from day to day. These variations can be as large as 10% for the city of Almelo. (2) Periodic variations at time-scales of typically 30 minutes. These variations are recurrent and are often larger than 10%. (3) Noise. This variation has a random character. We show that the noise can be approximated by a Poisson distribution.

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Section: Discussionmentioning

confidence: 99%

Variations in urban traffic volumes

Thomas

Weijermars

Berkum

2008

European Journal of Transport and Infrastructure Research

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“…The results show that after the implementation of the algorithm, the determined data does not have suitable accuracy. Weijermars et al [57] proposed a method for detecting inaccurate data of loop detectors in the city of Almelo in the Netherlands. Data quality assessment at both microscopic and macroscopic levels based on the minimum and maximum flow thresholds has led to the identification of incorrect data.…”

Section: State-of-the-artmentioning

confidence: 99%

An Effective Imputation Method Using Data Enrichment for Missing Data of Loop Detectors in Intelligent Traffic Control Systems

Gouran,

Nadimi-Shahraki,

Rahmani

et al. 2023

Remote Sensing

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In intelligent traffic control systems, the features extracted by loop detectors are insufficient to accurately impute missing data. Most of the existing imputation methods use only these extracted features, which leads to the construction of data models that cannot fulfill the required accuracy. This deficiency is the main motivation to propose an enrichment imputation method for loop detectors namely EIM-LD, in which the imputation accuracy is increased for different missing patterns and ratios by introducing a data enrichment technique using statistical multi-class labeling. It first enriches the clean data by adding a statistical multi-class label, including C1…Cn classes. Then, the class of samples in the missed-volume data is labeled using the best data model constructed from the labeled clean data by five different classifiers. Experts of the traffic control department in Isfahan city determined classes of the statistical multi-class label for n = 5 (class labels), and we also developed subclass labels (n = 20) since the number of samples in the subclass labels was sufficient. Next, the enriched data are divided into n datasets, each of them is imputed independently using various imputation methods, and their results are finally merged. To evaluate the impact of using the proposed method, the original data, including missing volumes, are first imputed without our enrichment method. Then, the proposed method’s accuracy is evaluated by considering two class labels and subclass labels. The experimental and statistical results prove that the proposed EIM-LD method can enrich the real data collected by loop detectors, by which the comparative imputation methods construct a more accurate data model. In addition, using subclass labels further enhances the imputation method’s accuracy.

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“…The Oregon Department of Transportation (DOT) conducted comprehensive research based on collected Bluetooth data and Kalman filtering methods for outlier detection, smoothing and accurate travel time estimation [23]. Weijermars and Berkum suggested a method for detecting outliers in loop detector data collected at urban intersections by using maximum and minimum traffic volumes along with the traffic volume conservation law [24]. Peeta et al presented outlier detection that distinguishes incidents from actual data errors by applying a heuristic algorithm based on the Fourier series [25].…”

Section: Literature Review and Research Opportunitiesmentioning

confidence: 99%

Application of locally weighted regression‐based approach in correcting erroneous individual vehicle speed data

Rim

Park

et al. 2015

J of Advced Transportation

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SUMMARYBecause of the quality of raw data being an essential feature in determining the reliability of traffic information, an effective detection and correction of outliers in raw field-collected traffic data has been an interest for many researchers. Global positioning systems (GPS)-based traffic surveillance systems are capable of producing individual vehicle speeds that are vital for transportation researchers and practitioners in traffic management and information strategies. This study proposes a locally weighted regression (LWR)-based filtering method for individual vehicle speed data. To fully and systematically evaluate this proposed method, a technique to generate synthetic outliers and two approaches to inject synthetic outliers are presented. Parameters that affect the smoothing performance associated with LWR are devised and applied to obtain a more robust and reliable data correction method. For a comprehensive performance evaluation of the developed LWR method, comparisons to exponential smoothing (ES) and autoregressive integrated moving average (ARIMA) methods were conducted. Because the LWR-based filtering method outperformed both the ES and ARIMA methods, this study showed its useful benefits in filtering individual vehicle speed data.

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Detection of Invalid Loop Detector Data in Urban Areas

Cited by 8 publications

References 11 publications

Variations in urban traffic volumes

Variations in urban traffic volumes

An Effective Imputation Method Using Data Enrichment for Missing Data of Loop Detectors in Intelligent Traffic Control Systems

Application of locally weighted regression‐based approach in correcting erroneous individual vehicle speed data

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