Integrating GIS into Traffic Incident Management: A Web-Based System
Authors
Faculty Technology Maklumat dan Komunikasi, University Technical Malaysia Melaka (UTeM), Durian Tunggal, Melaka, 76100 (Malaysia)
Faculty Technology Maklumat dan Komunikasi, University Technical Malaysia Melaka (UTeM), Durian Tunggal, Melaka, 76100 (Malaysia)
Faculty Technology Maklumat dan Komunikasi, University Technical Malaysia Melaka (UTeM), Durian Tunggal, Melaka, 76100 (Malaysia)
Faculty of Arts and Social Science University Malaya, Kuala Lumpur (Malaysia)
Article Information
DOI: 10.47772/IJRISS.2025.910000136
Subject Category: Technology
Volume/Issue: 9/10 | Page No: 1605-1616
Publication Timeline
Submitted: 2025-09-30
Accepted: 2025-10-06
Published: 2025-11-06
Abstract
The increasing frequency and severity of road accidents in Malaysia, driven by a significant disparity between vehicle growth and infrastructure capacity, present a pressing need for advanced traffic management solutions. This study details the design, development, and evaluation of a Web-GIS Traffic Incident Management System (WGTIMS), an integrated platform designed to enhance incident reporting, spatial visualization, and multi-stakeholder coordination. The system was constructed using a structured methodology of planning, design, development, and implementation, with deliberate integrations for performance and security. Built on an open-source stack (PHP, MySQL, Leaflet.js), WGTIMS employs a role-based architecture to serve administrators, police officers, and public users. A rigorous evaluation strategy was employed, combining black-box testing with preliminary user feedback. The technical testing demonstrated that the system successfully met all specified functional requirements, with test cases for critical workflows—including user authentication, incident reporting, and spatial data visualization, yielding the expected outcomes and robust error handling. User sessions indicated that the interface was intuitive and the GIS visualization was particularly effective for situational awareness. These findings confirm that WGTIMS is a viable and robust platform for improving response times and analytical decision-making in traffic incident management. Future work will focus on large-scale field deployment, cloud integration, and incorporating AI models for predictive analytics to further elevate its operational impact.
Keywords
Web-GIS, Traffic Incident Management, Road Accidents, Geographic Information System
Downloads
References
1. Shamsuddin, S., Minhans, A., Che Puan, O., Hasan, S. A., & Ismail, T. (2023). Spatial and temporal pattern of road accidents and casualties in Peninsular Malaysia. Jurnal Teknologi (Sciences & Engineering), 76. https://doi.org/10.11113/jt.v76.5843 [Google Scholar] [Crossref]
2. Mohd Nusa, F. N., Ishak, S. Z., Rusli, R., Mat Isa, C. M., Abdul Manan, M. M., & Sulistyono, S. (2023). Road crash data visualisation and analytics using Tableau for mountainous roadway areas in Cameron Highlands, Malaysia. Planning Malaysia, 21(28). https://doi.org/10.21837/pm.v21i28.1314 [Google Scholar] [Crossref]
3. Suhaimi, N. A., & Naharudin, N. (2025). Ambulance emergency responses vulnerability analysis towards traffic conditions using GIS. Built Environment Journal, 20(1), 1–12. [Google Scholar] [Crossref]
4. Alhajri, B., Abdul Rasam, A. R., Tarudin, N. F., Khalid, N., & Alshukaili, D. (2024). Spatial analysis of road traffic accident hotspots and patterns in Muscat, Oman: An exploratory risk management assessment. Planning Malaysia, 22(34). https://doi.org/10.21837/pm.v22i34.1614 [Google Scholar] [Crossref]
5. ElSahly, O., & Abdelfatah, A. (2024). Developing a machine-learning-based automatic incident detection system for traffic safety: Promises and limitations. Infrastructures, 9(10), 170. https://doi.org/10.3390/infrastructures9100170 [Google Scholar] [Crossref]
6. Alsahfi, T. (2024). Spatial and temporal analysis of road traffic accidents in major Californian cities using a Geographic Information System. ISPRS International Journal of Geo-Information, 13(5), 157. https://doi.org/10.3390/ijgi13050157 [Google Scholar] [Crossref]
7. Man, T.-C. (2024). GIS-based spatial analysis model for assessing impact and cumulative risk in road traffic accidents via Analytic Hierarchy Process (AHP)—Case study: Romania. Applied Sciences, 14(6), 2643. https://doi.org/10.3390/app14062643 [Google Scholar] [Crossref]
8. TM One. (n.d.). Malaysia Smart City – Smart Traffic Light Management (STARS). TM One. Retrieved September 26, 2025, from https://www.tmone.com.my/think-tank/malaysia-smart-city-components-smart-traffic-light-management/ [Google Scholar] [Crossref]
9. CelcomDigi, MyDigital Corporation (MyDigital), Digital Nasional Berhad (DNB), & Majlis Bandaraya Petaling Jaya (MBPJ). (2025, September 19). CelcomDigi launches Malaysia’s first AI traffic platform. Malaysian Wireless. Retrieved September 26, 2025, from https://www.malaysianwireless.com/2025/09/celcomdigi-malaysia-ai-traffic-platform/ [Google Scholar] [Crossref]
10. The Malaysian Highway Authority (LLM). (2025, September 22). Malaysia eyes full rollout of AI-based road safety system. Borneo Bulletin Online. Retrieved September 26, 2025, from https://borneobulletin.com.bn/malaysia-eyes-full-rollout-of-ai-based-road-safety-system/ [Google Scholar] [Crossref]
11. Ulu, M., Kilic, E., & Türkan, Y. S. (2024). Prediction of traffic incident locations with a geohash-based model using machine learning algorithms. Applied Sciences, 14(2), 725. https://doi.org/10.3390/app14020725 [Google Scholar] [Crossref]
12. Chen, P. (2024). Integrating AI and GIS for real-time traffic accident prediction and emergency response: A case study on high-risk urban areas. Advances in Engineering Innovation, 13, 44–48. https://www.ewadirect.com/journal/aEI/article/view/16960 [Google Scholar] [Crossref]
13. Ibe, C. C. et al. (2025). Geospatial probability mapping of road incidents for prioritizing road safety awareness. Journal Name, Volume(Issue). https://www.sciencedirect.com/science/article/pii/S235214652500465X [Google Scholar] [Crossref]
14. Idakwo, P. O., et al. (2025). Geo-parsing and analysis of road traffic crash incidents for geographic information extraction. Journal Name.https://www.sciencedirect.com/science/article/pii/S2405844024170983 [Google Scholar] [Crossref]
15. Abuhasel, K. A. (2023). A GIS Approach for Analysis of Traffic Accident Hotspots in Abha and Bisha. Sustainability, 15(19), 14112. https://www.mdpi.com/2071-1050/15/19/14112 [Google Scholar] [Crossref]
16. Development of Web Based Road Accident Data Management System in GIS Environment (Case Study). (n.d.). Retrieved from https://www.researchgate.net/publication/305449558_Development_of_Web_Based_Road_Accident_Data_Management_System_in_GIS_Environment_a_Case_Study [Google Scholar] [Crossref]
17. Adebayo, P., Williams, K., & Olonade, E. (2015). Online Road Traffic Accident Monitoring System for Nigeria (RTAMS). Transactions on Networks and Communications, Volume 3, Issue 1. Retrieved from https://www.researchgate.net/publication/276512934_Online_Road_Traffic_Accident_Monitoring_System_for_Nigeria [Google Scholar] [Crossref]
Metrics
Views & Downloads
Similar Articles
- LeafQuest: A Mobile-Based Augmented Reality for Plant Placement, Discovery, and Growth
- Participatory Ergonomic Intervention Approach on Musculoskeletal Disorder (MSD) in Construction Sectors: A Systematic Review
- RideSmart: A Personalized Motorcycle Product Recommendation System Using TF-IDF and Descriptive Analytics for Javidson Motorshop
- Educational Technology Course Design in Pre-Service Teachers Education: A Bibliometric Review of the Research Landscape
- Digital Tools for Constructive Alignment in Science Education: A Systematic Synthesis of Technology Enhanced Motivation and Assessment