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Book part
Publication date: 2 November 2009

Caroline Bayart, Patrick Bonnel and Catherine Morency

Data fusion and the combination of multiple data sources have been part of travel survey processes for some time. In the current context, where technologies and…

Abstract

Data fusion and the combination of multiple data sources have been part of travel survey processes for some time. In the current context, where technologies and information systems spread and become more and more diverse, the transportation community is getting more and more interested in the potential of data fusion processes to help gather more complete datasets and help give additional utility to available data sources. Research is looking for ways to enhance the available information by using both various data collection methods and data from various sources, surveys or observation systems. Survey response rates are decreasing over the world, and combining survey modes appears to be an interesting way to address this problem. Letting interviewees choose their survey mode allows increasing response rates, but survey mode could impact the data collected. This paper first discusses issues rising when combining survey modes within the same survey and presents a method to merge the data coming from different survey modes, in order to consolidate the database. Then, it defines and describes the data fusion process and discusses how it can be relevant for transportation analysis and modelling purposes. Benefiting from the availability of various datasets from the Greater Montréal Area and the Greater Lyon Area, some applications of data fusion are constructed and/or reproduced to illustrate and test some of the methods described in the literature.

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Transport Survey Methods
Type: Book
ISBN: 978-1-84-855844-1

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Article
Publication date: 6 March 2017

Pei-Ju Lee, Peng-Sheng You, Yu-Chih Huang and Yi-Chih Hsieh

The historical data usually consist of overlapping reports, and these reports may contain inconsistent data, which may return incorrect results of a query search…

Abstract

Purpose

The historical data usually consist of overlapping reports, and these reports may contain inconsistent data, which may return incorrect results of a query search. Moreover, users who issue the query may not learn of this inconsistency even after a data cleaning process (e.g. schema matching or data screening). The inconsistency can exist in different types of data, such as temporal or spatial data. Therefore, this paper aims to introduce an information fusion method that can detect data inconsistency in the early stages of data fusion.

Design/methodology/approach

This paper introduces an information fusion method for multi-robot operations, for which fusion is conducted continuously. When the environment is explored by multiple robots, the robot logs can provide more information about the number and coordination of targets or victims. The information fusion method proposed in this paper generates an underdetermined linear system of overlapping spatial reports and estimates the case values. Then, the least squares method is used for the underdetermined linear system. By using these two methods, the conflicts between reports can be detected and the values of the intervals at specific times or locations can be estimated.

Findings

The proposed information fusion method was tested for inconsistency detection and target projection of spatial fusion in sensor networks. The proposed approach examined the values of sensor data from simulation that robots perform search tasks. This system can be expanded to data warehouses with heterogeneous data sources to achieve completeness, robustness and conciseness.

Originality/value

Little research has been devoted to the linear systems for information fusion of tasks of mobile robots. The proposed information fusion method minimizes the cost of time and comparison for data fusion and also minimizes the probability of errors from incorrect results.

Details

Engineering Computations, vol. 34 no. 1
Type: Research Article
ISSN: 0264-4401

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Article
Publication date: 12 January 2015

Sabeur Elkosantini and Ahmed Frikha

Traffic congestion is becoming a serious problem that has adverse consequences on the socio-economy, environment, and public health of various cities worldwide. The…

Abstract

Purpose

Traffic congestion is becoming a serious problem that has adverse consequences on the socio-economy, environment, and public health of various cities worldwide. The purpose of this paper is to contribute to the continuous search for new alternative solutions to prevent or alleviate these concerns. It particularly deals with the development of decision support system based on a data fusion for the management and control of traffic at signalized intersections. The role of such systems is to manage the existing infrastructure to ease congestion and respond to crises. The proposed system is based on multi-detector data fusion, a data processing function that combines imperfect information collected from systems involving several detectors. The developed system is then tested on a virtual junction, and the results obtained are reported and discussed.

Design/methodology/approach

This paper presents a new traffic light control based on multi-detectors data fusion theory. The system uses a new multi-detectors data fusion method for traffic data analysis. Moreover, the system integrates a method for the estimation of the reliability degree of different detectors taking into account their imperfection and the conflict between them. These estimated reliability degrees are combined using Dempster’s rule of combination.

Findings

The paper provides a decision support system for traffic regulation at intersection based on multi-sensors. It suggests the fusion of captured data by many sensors for measuring information. The system use the Belief Functions Theory for information fusion and decision making using combination and decision rules.

Originality/value

The paper proposes a new Adaptive Traffic Control System based on a new data fusion approach that include a method for the estimation of the reliability degree of different detectors taking into account their imperfection and the conflict between them. These estimated reliability degrees are combined using Dempster’s rule of combination.

Details

Kybernetes, vol. 44 no. 1
Type: Research Article
ISSN: 0368-492X

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Article
Publication date: 1 September 2001

G. Simone and F.C. Morabito

A data fusion approach to the classification of eddy current and ultrasonic measurements is proposed in a context of defect detection/recognition methods for…

Abstract

A data fusion approach to the classification of eddy current and ultrasonic measurements is proposed in a context of defect detection/recognition methods for non‐destructive testing/evaluation systems: the purpose is to demonstrate that a multi‐sensor approach that combines the advantages carried by each sensor is able to locate potential cracks on the inspected specimen. Different approaches have been compared: a pixel level data fusion approach, that distinguishes between the defect area and the no‐defect areas, by means of the information carried by the intensity of each pixel of the eddy current and ultrasonic data; a feature level data fusion approach that uses the features computed on the measured data; a symbol level data fusion approach that extracts symbols from the two sensors as complementary information and classifies the data by using these symbols. The experimental results, carried out on an aluminium plate, pointed out the ability of the symbol level proposed approach to classify the input images within a minimum overall error, by taking into account the probability of detection and the probability of false alarm for the defect.

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COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, vol. 20 no. 3
Type: Research Article
ISSN: 0332-1649

Keywords

Content available
Article
Publication date: 20 July 2020

Abdelghani Bakhtouchi

With the progress of new technologies of information and communication, more and more producers of data exist. On the other hand, the web forms a huge support of all these…

Abstract

With the progress of new technologies of information and communication, more and more producers of data exist. On the other hand, the web forms a huge support of all these kinds of data. Unfortunately, existing data is not proper due to the existence of the same information in different sources, as well as erroneous and incomplete data. The aim of data integration systems is to offer to a user a unique interface to query a number of sources. A key challenge of such systems is to deal with conflicting information from the same source or from different sources. We present, in this paper, the resolution of conflict at the instance level into two stages: references reconciliation and data fusion. The reference reconciliation methods seek to decide if two data descriptions are references to the same entity in reality. We define the principles of reconciliation method then we distinguish the methods of reference reconciliation, first on how to use the descriptions of references, then the way to acquire knowledge. We finish this section by discussing some current data reconciliation issues that are the subject of current research. Data fusion in turn, has the objective to merge duplicates into a single representation while resolving conflicts between the data. We define first the conflicts classification, the strategies for dealing with conflicts and the implementing conflict management strategies. We present then, the relational operators and data fusion techniques. Likewise, we finish this section by discussing some current data fusion issues that are the subject of current research.

Details

Applied Computing and Informatics, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2210-8327

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Abstract

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Transport Survey Methods
Type: Book
ISBN: 978-1-84-855844-1

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Article
Publication date: 3 May 2016

Andy Chow

This paper aims to present collection and analysis of heterogeneous urban traffic data, and integration of them through a kernel-based approach for assessing performance…

Abstract

Purpose

This paper aims to present collection and analysis of heterogeneous urban traffic data, and integration of them through a kernel-based approach for assessing performance of urban transport network facilities. The recent development in sensing and information technology opens up opportunities for researching the use of this vast amount of new urban traffic data. This paper contributes to analysis and management of urban transport facilities.

Design/methodology/approach

In this paper, the data fusion algorithm are developed by using a kernel-based interpolation approach. Our objective is to reconstruct the underlying urban traffic pattern with fine spatial and temporal granularity through processing and integrating data from different sources. The fusion algorithm can work with data collected in different space-time resolution, with different level of accuracy and from different kinds of sensors. The properties and performance of the fusion algorithm is evaluated by using a virtual test bed produced by VISSIM microscopic simulation. The methodology is demonstrated through a real-world application in Central London.

Findings

The results show that the proposed algorithm is able to reconstruct accurately the underlying traffic flow pattern on transport network facilities with ordinary data sources on both virtual and real-world test beds. The data sources considered herein include loop detectors, cameras and GPS devices. The proposed data fusion algorithm does not require assumption and calibration of any underlying model. It is easy to implement and compute through advanced technique such as parallel computing.

Originality/value

The presented study is among the first utilizing and integrating heterogeneous urban traffic data from a major city like London. Unlike many other existing studies, the proposed method is data driven and does not require any assumption of underlying model. The formulation of the data fusion algorithm also allows it to be parallelized for large-scale applications. The study contributes to the application of Big Data analytics to infrastructure management.

Details

Journal of Facilities Management, vol. 14 no. 2
Type: Research Article
ISSN: 1472-5967

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Article
Publication date: 14 June 2013

Wen‐Tsai Sung and Chia‐Cheng Hsu

This study aims to analyze the inertial weight factor value in the (PSO) algorithm and propose non‐linear weights with decreasing strategy to implement the improved PSO…

Abstract

Purpose

This study aims to analyze the inertial weight factor value in the (PSO) algorithm and propose non‐linear weights with decreasing strategy to implement the improved PSO (IPSO) algorithm. Using various types of sensors, combined with ZigBee wireless sensor networks and the TCP/IP network. The GPRS/SMS long‐range wireless network will sense the measured data analysis and evaluation to create more effective monitoring and observation in a regional environment to achieve an Internet of Things with automated information exchange between persons and things.

Design/methodology/approach

This study proposes a wireless sensor network system using ZigBee (PSoC‐1605A) chip, sensor and circuit boards to constitute the IOT system. The IOT system consists of a main coordinator (PSoC‐1605A), smart grid monitoring system, robotic arm detection warning system and temperature and humidity sensor network. The hardware components communicate with each other through wireless transmission. Each node collects data and sends messages to other objects in the network.

Findings

This study employed IPSO to perform information fusion in a multi‐sensor network. The paper shows that IPSO improved the measurement preciseness via weight factors estimated via experimental simulations. The experimental results show that the IPSO algorithm optimally integrates the weight factors, information source fusion reliability, information redundancy and hierarchical structure integration in uncertain fusion cases. The sensor data approximates the optimal way to extract useful information from each fusion data and successfully eliminates noise interference, producing excellent fusion results.

Practical implications

Robotic arm to tilt detection warning system: Several geographic areas are susceptible to severe tectonic plate movement, often generating earthquakes. Earthquakes cause great harm to public infrastructure, and a great threat to high‐tech, high‐precision machinery and production lines. To minimize the extent of earthquake disasters and allow managers to deal with power failures, vibration monitoring system construction can enhance manufacturing process quality and stability. Smart grid monitoring system: The greenhouse effect, global energy shortage and rising cost of traditional energy are related energy efficiency topics that have attracted much attention. The aim of this paper is that real‐time data rendering and analysis can be more effective in understanding electrical energy usage, resulting in a reduction in unnecessary consumption and waste. Temperature and humidity sensor network system: Environmental temperature and humidity monitoring and application of a wide range of precision industrial production lines, laboratories, antique works of art that have a higher standard of environmental temperature and humidity requirements. The environment has a considerable influence on biological lifeforms. The relative importance of environmental management and monitoring is acute.

Originality/value

This paper improves the fixed inertial weight of the original particle swarm optimization (PSO) algorithm. An illustration in the paper indicates that IPSO applies the Internet of Things (IOT) system in monitoring a system via adjusted weight factors better than other existing PSO methods in computing a precise convergence rate for excellent fusion results.

Details

Sensor Review, vol. 33 no. 3
Type: Research Article
ISSN: 0260-2288

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Article
Publication date: 12 May 2021

Noor Cholis Basjaruddin, Faris Rifqi Fakhrudin, Yana Sudarsa and Fatimah Noor

In the context of overcoming malnutrition in elementary school children and increasing public awareness of this issue, the Indonesian Government has created a “Card for…

Abstract

Purpose

In the context of overcoming malnutrition in elementary school children and increasing public awareness of this issue, the Indonesian Government has created a “Card for Healthy School Children” (KMS-AS) program in the form of a paper health card. However, currently, the KMS-AS record data are still written on paper, which is less effective in terms of the health process. An integrated measuring device and an online data-recording system are needed to promote children’s health and facilitate access and transfer of data from one place to another. This study aims to develop NFC and IoT-based KMS-AS using sensor fusion method.

Design/methodology/approach

The results of this study show that an integrated measuring device for weight, height, body temperature and Spo2 level can be connected with mobile and Web applications using IoT technology, facilitating data recording and monitoring of children’s nutritional status. The sensor fusion method was used for the classification of nutritional status and health status, based on the results of measurement tools. Near field communication (NFC) technology was used to facilitate user identification when making measurements.

Findings

The results show that KMS-AS can facilitate classification of nutritional status and children's health status. Measurement and classification data can be monitored via Web and mobile applications. The accuracy of height, weight, body temperature and Spo2 measurements was 98.21%, 98.59%, 98.93% and 98.93%, respectively.

Originality/value

In this research, the authors successfully produced a system using sensor fusion method for measuring body weight, height, temperature and Spo2 level, which is integrated and can be connected to mobile applications and the Web using the IoT and NFC.

Details

International Journal of Pervasive Computing and Communications, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1742-7371

Keywords

Content available
Article
Publication date: 1 October 2018

Xunjia Zheng, Bin Huang, Daiheng Ni and Qing Xu

The purpose of this paper is to accurately capture the risks which are caused by each road user in time.

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Abstract

Purpose

The purpose of this paper is to accurately capture the risks which are caused by each road user in time.

Design/methodology/approach

The authors proposed a novel risk assessment approach based on the multi-sensor fusion algorithm in the real traffic environment. Firstly, they proposed a novel detection-level fusion approach for multi-object perception in dense traffic environment based on evidence theory. This approach integrated four states of track life into a generic fusion framework to improve the performance of multi-object perception. The information of object type, position and velocity was accurately obtained. Then, they conducted several experiments in real dense traffic environment on highways and urban roads, which enabled them to propose a novel road traffic risk modeling approach based on the dynamic analysis of vehicles in a variety of driving scenarios. By analyzing the generation process of traffic risks between vehicles and the road environment, the equivalent forces of vehicle–vehicle and vehicle–road were presented and theoretically calculated. The prediction steering angle and trajectory were considered in the determination of traffic risk influence area.

Findings

The results of multi-object perception in the experiments showed that the proposed fusion approach achieved low false and missing tracking, and the road traffic risk was described as a field of equivalent force. The results extend the understanding of the traffic risk, which supported that the traffic risk from the front and back of the vehicle can be perceived in advance.

Originality/value

This approach integrated four states of track life into a generic fusion framework to improve the performance of multi-object perception. The information of object type, position and velocity was used to reduce erroneous data association between tracks and detections. Then, the authors conducted several experiments in real dense traffic environment on highways and urban roads, which enabled them to propose a novel road traffic risk modeling approach based on the dynamic analysis of vehicles in a variety of driving scenarios. By analyzing the generation process of traffic risks between vehicles and the road environment, the equivalent forces of vehicle–vehicle and vehicle–road were presented and theoretically calculated.

Details

Journal of Intelligent and Connected Vehicles, vol. 1 no. 2
Type: Research Article
ISSN: 2399-9802

Keywords

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