Fusing Heterogeneous and Unreliable Data from Traffic Sensors

Ou, Qing; Lint, Hans van; Hoogendoorn, Serge P.

doi:10.1007/978-3-642-11688-9_19

Cited by 4 publications

(5 citation statements)

References 18 publications

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“…Our forecasting methodology is illustrated using loop detector data which may contain a large percentage of missing values and erroneous measurements . Recently, there has been increased interest on data fusion methods which combine effectively data from multiple sources (including GPS data from probe vehicles and data from video cameras ).…”

Section: Discussionmentioning

confidence: 99%

“…Recently, there has been increased interest on data fusion methods which combine effectively data from multiple sources (including GPS data from probe vehicles and data from video cameras ). Data fusion aims at improved quality and increased spatial coverage , which are important factors that influence the capabilities of traffic prediction tools. The forecasting models we present can use such data as long as they are updated at fixed time intervals.…”

Section: Discussionmentioning

confidence: 99%

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Real-time road traffic forecasting using regime-switching space-time models and adaptive LASSO

Kamarianakis

Shen²,

Wynter

2012

Appl. Stochastic Models Bus. Ind.

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Smart transportation technologies require real-time traffic prediction to be both fast and scalable to full urban networks. We discuss a method that is able to meet this challenge while accounting for nonlinear traffic dynamics and space-time dependencies of traffic variables. Nonlinearity is taken into account by a union of non-overlapping linear regimes characterized by a sequence of temporal thresholds. In each regime, for each measurement location, a penalized estimation scheme, namely the adaptive absolute shrinkage and selection operator (LASSO), is implemented to perform model selection and coefficient estimation simultaneously. Both the robust to outliers least absolute deviation estimates and conventional LASSO estimates are considered. The methodology is illustrated on 5-minute average speed data from three highway networks.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Real-time road traffic forecasting using regime-switching space-time models and adaptive LASSO

Kamarianakis

Shen²,

Wynter

2012

Appl. Stochastic Models Bus. Ind.

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“…These data cannot be straightforwardly aligned and fused with recursive techniques such as the EKF or any of its relatives. In a number of contributions, Ou et al [37,38,39,40] demonstrate that different data assimilation techniques are required to effectively fuse such semantically different data sources. Nonetheless, these socalled data-data consistency techniques can in turn be used to remove structural bias (e.g., due to local averaging) from the available sensor data before these are used in recursive state estimation methods and control applications.…”

Section: 2mentioning

confidence: 99%

Applications of Kalman Filtering in Traffic Management and Control

Lint¹,

Djukic²

2012

2012 TutORials in Operations Research

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Please scroll down for article-it is on subsequent pagesINFORMS is the largest professional society in the world for professionals in the fields of operations research, management science, and analytics. For more information on INFORMS, its publications, membership, or meetings visit http://www.informs.org INFORMS 2012 c 2012 INFORMS | isbn 978-0-9843378-3-5 http://dx.Abstract In many areas of traffic management and control the variables that are of most interest are often the ones that are most difficult to measure and estimate. Take for example vehicular density (vehicles per kilometer) and space-mean speed (kilometers per hour). A reliable real-time estimate of these quantities is critically important for real-time control of traffic networks. However, neither can be straightforwardly deduced from available sensor data. Similarly elusive quantities are origin-destination (OD) flows. These depict the amount of vehicles per hour planning to go from one place to another at a certain moment. Unless we literally know the origin and destination of all vehicles on a traffic network, advanced estimation techniques are required to extract OD patterns from whatever data and prior knowledge we have available. One intuitive and highly effective method to solve these types of problems (i.e., estimating a quantity x when all we have are observations y and prior knowledge about the process) is the Kalman filter, first proposed by Rudolf Kalman in 1960. In this tutorial we explain with many examples how this technique can be applied in the domain of traffic management and control to solve real-world problems. We will see that Kalman filtering is a powerful technique that works surprisingly well in many cases, but there are also clear limitations that relate to the many assumptions underlying its application.

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“…neural networks [9][10][11][12] or Kalman modelling [13], fusing additional data sources e.g. automatic license plate recognition [14].…”

Section: Introductionmentioning

confidence: 99%

Real-time traffic monitoring by fusing floating car data with stationary detector data

Houbraken

Audenaert

Colle

et al. 2015

2015 International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)

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Abstract-Applying the current technological possibilities has led to a wide range of traffic monitoring systems. These heterogeneous data sources individually provide a view on the current traffic state, each source having its own properties and (dis)advantages. However, these different sources can be aggregated to create a single traffic state estimation. This paper presents a data fusion algorithm that combines data on the data sample level. The proposed system fuses floating car data with stationary detector data and was implemented on live traffic. Results show the fusion algorithm allows to eliminate individual source bias and alleviates source-specific limitations.

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Fusing Heterogeneous and Unreliable Data from Traffic Sensors

Cited by 4 publications

References 18 publications

Real-time road traffic forecasting using regime-switching space-time models and adaptive LASSO

Real-time road traffic forecasting using regime-switching space-time models and adaptive LASSO

Applications of Kalman Filtering in Traffic Management and Control

Real-time traffic monitoring by fusing floating car data with stationary detector data

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