Data fusion in intelligent transportation systems: Progress and challenges – A survey

Faouzi, Nour‐Eddin El; Leung, Henry; Kurian, Ajeesh P.

doi:10.1016/j.inffus.2010.06.001

Cited by 356 publications

(169 citation statements)

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“…Data fusion approaches have become popular for heterogeneous data as they handle the process of integration of multiple data and knowledge from the same real-world object into a consistent, accurate, and useful representation. In practice, data fusion has been evolving for a long time in multi-sensor research (Hall and Llinas, 1997;Khaleghi et al, 2013) and other areas such as robotics and machine learning (Abidi and Gonzalez, 1992;Faouzi et al, 2011). However, there has been little interaction with data mining research until recently (Dasarathy, 2003).…”

Section: Related Workmentioning

confidence: 99%

An adaptive version of k-medoids to deal with the uncertainty in clustering heterogeneous data using an intermediary fusion approach

Mojahed

Iglesia

2016

Knowl Inf Syst

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Abstract. This paper introduces Hk -medoids, a modified version of the standard kmedoids algorithm. The modification extends the algorithm for the problem of clustering complex heterogeneous objects that are described by a diversity of data types, e.g. text, images, structured data and time series. We first proposed an intermediary fusion approach to calculate fused similarities between objects, SMF, taking into account the similarities between the component elements of the objects using appropriate similarity measures. The fused approach entails uncertainty for incomplete objects or for objects which have diverging distances according to the different component. Our implementation of Hk -medoids proposed here works with the fused distances and deals with the uncertainty in the fusion process. We experimentally evaluate the potential of our proposed algorithm using five datasets with different combinations of data types that define the objects. Our results show the feasibility of the our algorithm and also they show a performance enhancement when comparing to the application of the original SMF approach in combination with a standard k -medoids that does not take uncertainty into account. In addition, from a theoretical point of view, our proposed algorithm has lower computation complexity than the popular PAM implementation.

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Section: Related Workmentioning

confidence: 99%

An adaptive version of k-medoids to deal with the uncertainty in clustering heterogeneous data using an intermediary fusion approach

Mojahed

Iglesia

2016

Knowl Inf Syst

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“…In a survey on data fusion methods in Intelligent Transportation Systems (ITS) El Fazoui et al [3] concluded that there are several methods available that can be used to fuse heterogeneous data. The methods range from more naïve statistical approaches as different weighting methods to more complex methods as neural networks and Kalman filters.…”

Section: Previous Workmentioning

confidence: 99%

Travel Time and Point Speed Fusion Based on a Macroscopic Traffic Model and Non-linear Filtering

Gundlegård

Allström

Bergfeldt

et al. 2015

2015 IEEE 18th International Conference on Intelligent Transportation Systems

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“…If rough data is processed in an integrated way, the information about the network is more precise that results in the fact that all subsystems work in a more efficient way. This means that data fusion can help all intelligent transportation subsystems to improve (Anand et al, 2013), (El Faouzi et al, 2011). The functions of these subsystems are usually measurement, estimation, forecasting, control and information collecting or providing (Quing, 2011).…”

Section: The Purpose Of Data Fusion In Transportationmentioning

confidence: 99%

Road Traffic Measurement and Related Data Fusion Methodology for Traffic Estimation

Tettamanti

Horváth

Varga

2014

Transport and Telecommunication Journal

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The knowledge of road traffic parameters is of crucial importance to ensure state-of-the-art traffic services either in public or private transport. In our days, a plethora of road traffic data are continuously collected producing historical and real-time traffic information as well. The available information, however, arrive from inhomogeneous sensor systems. Therefore, a data fusion methodology is proposed based on Switching Kalman Filter. The concept enables efficient travel time estimation for urban road traffic network. On the other hand, the method may contribute to a better macroscopic traffic modelling.

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Data fusion in intelligent transportation systems: Progress and challenges – A survey

Cited by 356 publications

References 50 publications

An adaptive version of k-medoids to deal with the uncertainty in clustering heterogeneous data using an intermediary fusion approach

An adaptive version of k-medoids to deal with the uncertainty in clustering heterogeneous data using an intermediary fusion approach

Travel Time and Point Speed Fusion Based on a Macroscopic Traffic Model and Non-linear Filtering

Road Traffic Measurement and Related Data Fusion Methodology for Traffic Estimation

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