Estimation of missing traffic counts using factor, genetic, neural, and regression techniques

Zhong, Ming; Lingras, Pawan; Sharma, Satish C.

doi:10.1016/j.trc.2004.07.006

Cited by 158 publications

(70 citation statements)

References 10 publications

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“…Whether the termination condition is met, if the objective function value is less than a preset threshold, the difference between objective function values of two consecutive iterations is less than a preset threshold, or the number of iterations reaches its preset maximum number, then the termination condition is met and go to the next step; otherwise update the according to (6) and return to Step 2.…”

Section: Fcm-based Imputationmentioning

confidence: 99%

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An Imputation Method for Missing Traffic Data Based on FCM Optimized by PSO-SVR

Shang

Yang

Gao

et al. 2018

Journal of Advanced Transportation

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Missing traffic data are inevitable due to detector failure or communication failure. Currently, most of imputation methods estimated the missing traffic values by using spatial-temporal information as much as possible. However, it ignores an important fact that spatial-temporal information of the traffic missing data is often incomplete and unavailable. Moreover, most of the existing methods are verified by traffic data from freeway, and their applicability to urban road data needs to be further verified. In this paper, a hybrid method for missing traffic data imputation is proposed using FCM optimized by a combination of PSO algorithm and SVR. In this method, FCM is the basic algorithm and the parameters of FCM are optimized. Firstly, the patterns of missing traffic data are analyzed and the representation of missing traffic data is given using matrix-based data structure. Then, traffic data from urban expressway and urban arterial road are used to analyze spatial-temporal correlation of the traffic data for the determination of the proposed method input. Finally, numerical experiment is designed from three perspectives to test the performance of the proposed method. The experimental results demonstrate that the novel method not only has high imputation precision, but also exhibits good robustness.

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Section: Fcm-based Imputationmentioning

confidence: 99%

“…In these methods, a missing data point is regarded as a value to be predicted, and then the value is predicted using the relationship extracted from historical past-to-future data pairs [6]. However, two major differences between missing traffic data imputation and traffic flow prediction had not been fully considered in these methods.…”

Section: Introductionmentioning

confidence: 99%

An Imputation Method for Missing Traffic Data Based on FCM Optimized by PSO-SVR

Shang

Yang

Gao

et al. 2018

Journal of Advanced Transportation

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“…It is possible to find different ways to impute the data in the foreign literature: from the simplest consisting of the manual replacement of missing data with historical data, or using simple computer programs (solutions analogous to the indicator method of estimating AADT based on short term measurements) described by S. Datla, S. Sharma [7], M. Zhong, P. Lingras, S. Sharma [4,8,9], for example:…”

Section: Ways To Impute Datamentioning

confidence: 99%

The Problem Of Imputation Of The Missing Data From The Continuous Counts Of Road Traffic

Spławińska

2015

Archives of Civil Engineering

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1Missing traffic data is an important issue for road administration. Although numerous ways can be found to impute them in foreign literature (inter alia, the most effective method, that is Box-Jenkins models), in Poland, still only proven and simplified methods are applied. The article presents the analyses including an assessment of the completeness of the existing traffic data and works related to the construction of SARIMA model. The study was conducted on the basis of hourly traffic volumes, derived from the continuous traffic counts stations located in the national road network in Poland (Golden River stations) from the years 2005 -2010. As a result, the proposed model was used to impute the missing data in the form of SARIMA (1.1,1)(0,1,1) 168 . The newly developed model can be used effectively to fill in the missing required days of measurement for estimating AADT by AASHTO method. In other cases, due to its accuracy and laboriousness of the process, it is not recommended.

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“…the problem of missing data is prevalent in many transport management systems [4][5][6][7]. The methods employed to tackle this problem either use information from neighboring links [8,9] or consider historical information of the road segment for imputation [5][6][7]10].…”

Section: Fig 1: Road Network In Singapore (Outram To Changi)mentioning

confidence: 99%

“…The spatial and temporal distribution of missing data points is usually highly erratic [11]. Therefore, the methods which rely on complete historical or current information from neighbors for data imputation [5][6][7][8][9][10] may not work in such settings.…”

Section: Fig 1: Road Network In Singapore (Outram To Changi)mentioning

confidence: 99%

Low-dimensional models for missing data imputation in road networks

Asif

Mitrović

Garg

et al. 2013

2013 IEEE International Conference on Acoustics, Speech and Signal Processing

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Intelligent transport systems (ITS) require data with high spatial and temporal resolution for applications such as modeling, traffic management, prediction and route guidance. However, field data is usually quite sparse. This problem of missing data severely limits the effectiveness of ITS. Missing values are usually imputed by either using historical data of the road or current information from neighboring links. In most scenarios, information from some or all of neighboring links might not be available.Furthermore, historical data may also be incomplete. To overcome these issues, we propose methods which can construct low-dimensional representation of large and diverse networks, in presence of missing historical and neighboring data. We use these low-dimensional models to reconstruct data profiles for road segments, and impute missing values. To this end we use Fixed Point Continuation with Approximate SVD (FPCA) and Canonical Polyadic (CP) decomposition for incomplete tensors to solve the problem of missing data. We apply these methods to expressways and a large urban road network to assess their performance for different scenarios.Index Terms-Missing data in large networks, low-dimensional models

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Estimation of missing traffic counts using factor, genetic, neural, and regression techniques

Cited by 158 publications

References 10 publications

An Imputation Method for Missing Traffic Data Based on FCM Optimized by PSO-SVR

An Imputation Method for Missing Traffic Data Based on FCM Optimized by PSO-SVR

The Problem Of Imputation Of The Missing Data From The Continuous Counts Of Road Traffic

Low-dimensional models for missing data imputation in road networks

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