Data fusion for ITS: A systematic literature review

doi:10.1016/j.inffus.2022.08.016

Information Fusion

Volume 89, January 2023, Pages 267-291

https://doi.org/10.1016/j.inffus.2022.08.016 Get rights and content

Highlights

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Reviewing of data fusion technologies in ITS applications.
•
Emphasizing the extraction of methodologies, data properties, and evaluation process.
•
Pertaining research data fusion issues to data characteristics.
•
Prospecting directions towards secure and privacy-preserving data fusion in ITS.

Abstract

In recent years, the development of intelligent transportation systems (ITS) has involved the input of various kinds of heterogeneous data in real time and from multiple sources, which presents several additional challenges. Studies on Data Fusion (DF) have delivered significant enhancements in ITS and demonstrated a substantial impact on its evolution. This paper introduces a systematic literature review on recent data fusion methods and extracts the main issues and challenges of using these techniques in intelligent transportation systems (ITS). It endeavors to identify and discuss the multi-sensor data sources and properties used for various traffic domains, including autonomous vehicles, detection models, driving assistance, traffic prediction, Vehicular communication, Localization, and management systems. Moreover, it attempts to associate abstractions of observation-level fusion, feature-level fusion, and decision-level fusion with different methods to better understand how DF is used in ITS applications. Consequently, the main objective of this paper is to review DF methods used for ITS studies to extract its trendy challenges. The review outcomes are (i) a description of the current Data fusion methods that adopt multi-sensor sources of heterogeneous data under different evaluation strategies, (ii) identifying several research gaps, current challenges, and new research trends.

Introduction

Accelerated evolution in intelligent transportation systems is obtained in response to the increased demand for reliable transportation networks. Thanks to the deployment of ubiquitous communication technologies that can continuously measure traffic attributes, e.g., IP, Bluetooth, surveillance video camera, GPS, smartphones, loop detectors, magnetometers, R- ADARs, social media, and Vehicle to X (V2X), massive databases of various traffic data have been so far collected [1]. Such sensors measure traffic conditions with different methods and technologies, resulting in varying degrees of accuracy in their output [2]. E.g., While loop detectors collect frequent traffic information at a limited set of fixed points along a given road section [3], [4], [5], probe vehicles can provide continuous traffic measurements using GPS sensors along the same section of the road [6], [7], [8].

These heterogeneous sources of data provide different traffic conditions and statistics (quantitative and qualitative [2]) to different ITS applications (e.g. vehicle navigation [9], [10], incident detection [11], [12], [13], traffic prediction [14], [15], [16]) to the aim of ease traffic problems by maximizing their safety and efficiency. However, they still suffer from many issues worth mentioning (i) real-time heterogeneous data and (ii) and sensor reliability. First, data are continuously generated with inconsistent formats and managed in different storage settings, which render the data unusable directly [17]. Second, sensors are not continuously reliable because of technical and operation-related issues (geometry locations or damages [7]), which cause gaps and missing information that affects stakeholders’ decision-making. Therefore, a major challenge is to reduce the data missing, redundancy, delay, and anomalies phenomenon to improve the robustness and accuracy of the intelligent transportation systems applications [1].

Multi-source data fusion (MDF) models have grasped an extensive interest in an attempt to deal with these issues. Data fusion is an advanced technique to combine information coming from several sources to get more accurate results in an execution of an application in a way that is hardly performed by the use of individual sources separately [18]. Some existing papers have tried to summarize the efforts in data fusion. Table 1 summaries the characteristics of each data fusion previously conducted survey. We can see that the latest specific systematic review that covers the data fusion techniques applied in intelligent transportation systems was proposed in 2011 by Faouzi et al. [19]. The remaining surveys cover the general application of the data fusion techniques in different domains such as the internet of things (IoT) and smart cities. Both [20], [21] are very recent and updated surveys that focus only on the machine and deep learning data fusion techniques used for different IoT applications that may also concern the ITS.

This paper performs a thorough systematic literature review on recent data fusion techniques, applications to extract issues, and challenges of using these techniques in intelligent transportation systems (ITS).

By and large, the main contributions of this systematic literature review are as follows:

•
We review a wide range of existing data fusion technologies in ITS literature, including their primary methods, data properties, evaluations, and applications.
•
We discuss the important insights gleaned from data fusion techniques gathered from the raised research questions using a multi-perspectives classification methodology.
•
We list several significant open issues and future research directions, which are useful for researchers and practitioners based on the completed review and in-depth analysis.

We organize the remainder of this paper as follows. First, we searched articles in multiple databases using a search strategy described in Section 2. Then, once we collected the articles, they were reviewed and organized in Section 3, which discusses the significant insights gathered from the raised research questions. Finally, we provide the conclusion and suggestions for future research on data fusion techniques within the context of intelligent transportation applications in Section 4.

Section snippets

Methodology and research protocol

This systematic literature review aims to summarize the recent state-of-the-art data fusion techniques applied to intelligent transportation systems (ITS) by performing an exhaustive search of papers since 2011 and reporting our main results and findings following the protocol recommended in the Kitchenham report [27].

Harvest scrutiny

Following the search strategy, we identified 175 articles published between January 2011 and November 2020. We critically reviewed all of the 175 articles to shed light on the issues raised in Section 2. At a glance, Table 16, Table 17, Table 18, Table 19, Table 20, Table 21, Table 22, Table 23, Table 24 sketch the studied articles based on the raised researches questions using the following criteria:

•
Fusion approach: refers to the used data fusion methods/techniques (to answer the first

Conclusion

Intelligent transportation refers to a set of applications that requires knowledge to ensure reliable and safe movement of passengers and freight in various environments. However, because of the ubiquitous deployment of communication technologies, tremendous amounts of traffic-related data have been collected to ease traffic issues. As a result, multi-source data fusion models have grasped an extensive interest in an attempt to deal with these issues. The present paper systematically reviews a

CRediT authorship contribution statement

Chahinez Ounoughi: Conceptualization, Methodology, Validation, Investigation, Visualization, Writing – original draft, Writing – review & editing. Sadok Ben Yahia: Supervision.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This work was supported by grants to TalTech – TalTech Industrial (H2020, grant No 952410) and Estonian Research Council (PRG1573).

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Data fusion for ITS: A systematic literature review

Highlights

Abstract

Introduction

Section snippets

Methodology and research protocol

Harvest scrutiny

Conclusion

CRediT authorship contribution statement

Declaration of Competing Interest

Acknowledgments

Transp. Res. C

Transp. Res. Procedia

Transp. Res. Procedia

Inf. Fusion

Appl. Soft Comput.

Inf. Fusion

Veh. Commun.

Eng. Appl. Artif. Intell.

Inf. Process. Manage.

Inf. Fusion

Inf. Fusion

Inf. Fusion

Inf. Fusion

Inf. Fusion

Inf. Fusion

Inf. Fusion

Veh. Commun.

Inf. Fusion

Robot. Auton. Syst.

Comput. Commun.

Transp. Res. C

Robot. Auton. Syst.

Transp. Res. C

Proc. - Soc. Behav. Sci.

Expert Syst. Appl.

Procedia Comput. Sci.

Procedia Comput. Sci.

Comput. Netw.

Neurocomputing

Inf. Fusion

Procedia Comput. Sci.

Data fusion method based on mutual dimensionless

IEEE/ASME Trans. Mechatronics

Traffic condition monitoring with SCAAT Kalman filter-based data fusion in Toronto, Canada

KSCE J. Civ. Eng.

Online traffic condition evaluation method for connected vehicles based on multisource data fusion

J. Sensors

Ecolight: Eco-friendly traffic signal control driven by urban noise prediction

Lagrangian-based hydrodynamic model for traffic data fusion on freeways

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J. Intell. Transp. Syst.

Short-term traffic flow prediction based on sparse regression and spatio-temporal data fusion

IEEE Access

An efficient algorithm for multisensory data fusion under uncertainty condition

J. Electr. Syst. Inf. Technol.

Data fusion and IoT for smart ubiquitous environments: A survey

IEEE Access

Procedures for performing systematic reviews

An introduction to multisensor data fusion

Proc. IEEE

Data fusion and remote sensing – an ever-growing relationship

IEEE Geosci. Remote Sens. Mag.

From data acquisition to data fusion: A comprehensive review and a roadmap for the identification of activities of daily living using mobile devices

Sensors

A robust low cost approach for real time car positioning in a smart city using extended Kalman filter and evolutionary machine learning

Improving vehicle localization in a smart city with low cost sensor networks and support vector machines

Mob. Netw. Appl.

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Vehicle localization in VANETs using data fusion and V2V communication