Skip to content
Research Article Open access CC BY 4.0

Bibliometric Analysis of Traffic Management in Industrial Area

Jason Angelo Ong, Andri Irfan Rifai, Susanty Handayani, Joewono Prasetijo, Muhammad Isradi

Journal of Engineering Research and Reports · pp. 236–246 · Published 11 Aug 2025

10.9734/jerr/2025/v27i81607

Abstract

Aims: This study aims to analyze the global research trends on traffic management in industrial areas through a bibliometric approach. The research identifies publication patterns, key themes, and influential contributors within this topic. Study Design: Descriptive bibliometric research. Place and Duration of Study: Batam International University, Indonesia, from November to December 2023. Methodology: A total of 1000 relevant documents were retrieved using the Publish or Perish software with Crossref as the data source, covering the period from 2000 to 2023. The data were filtered using keywords such as "Traffic Management," "Road Infrastructure," "Traffic Congestion," and "Vehicle." Visualization and analysis were conducted using VOSviewer to map keyword connections and density. The research types, annual publication trends, and most influential authors were also identified and evaluated. Results: The findings indicate that the highest number of related studies occurred in 2022, with 100 publications. Journal articles were the dominant publication type, with 657 entries, while books ranked second with 197. The most influential authors in the field were Falcocchio, John C., and Levinson, Herbert S., both contributing to 27 publications. Keyword analysis revealed strong associations between traffic data, road safety, and traffic management, while terms such as urban areas and smart cities showed lower relevance. Conclusion: This study reveals a growing global interest in traffic management within industrial areas, especially in recent years. This bibliometric study gives us vital information about how traffic management is changing in industrial places around the world.   It also demonstrates that there are gaps in study, especially when it comes to how smart city ideas may be applied with traffic management systems and how emerging technologies like AI and the Internet of Things can make traffic go more smoothly. Researchers could investigate into how these technologies could be applied in real-world industrial settings, especially in countries with emerging economies.   Also, a closer examination into traffic bottlenecks in countries that are increasingly becoming cities, like Southeast Asia, might help us understand the unique issues and opportunities that come with living there. Cross-disciplinary studies that look at urban planning, environmental impact assessments, and traffic management all at once are especially crucial for addressing the growing concerns about sustainability and mobility in industrial locations.   This might give city planners and lawmakers vital information that could help them make better choices about how to manage traffic without harming the environment as much.

Traffic management road infrastructure traffic congestion vehicle

Cited by 4

Mapping Research on AI-Driven Traffic Advisory and Decision Support Systems: A Bibliometric Analysis

Maika V. Garbes, Eric Blancaflor · IEEE International Conference on System Engineering and Technology (ICSET) · 2025

Global Research Trends in Urban Parking Systems and Traffic Management: A Bibliometric Analysis (2000-2024)

Yusra Aulia Sari, Muhammad Hauzan Suhenal, Andri Irfan Rifai · Civil and Sustainable Urban Engineering · 2026

Showing 2 of 4 known citations — external sources report more than can currently be individually listed.

Article metrics

Real usage data collected on this platform.

18

Page views

0

PDF downloads

0

Outbound clicks

4

Citations

Views over time

Views by country

Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".

Traffic sources

Referring site, by host.

Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.