Optimising heavy cargo traffic management: enhancing decision-making through effective data management at the Port of Richards Bay.

Abstract

This study's primary objective was to explore the utilisation of a data management system to optimise and reduce heavy cargo traffic at the Port of Richards Bay. The study was based on the following objectives: first, to analyse current traffic data to understand patterns, congestion points and high impact points, particularly on the N2 and the John Ross Highway, which leads to the Port of Richards Bay; secondly, to explore the feasibility of the use of a designed data management system to optimise heavy cargo traffic flow and alleviate the strain on local infrastructure and services; thirdly, to recommend a system to integrate real-time data collection, visualisation, and communication tools for stakeholders, including the Richards Bay Coal Terminal (RBCT), Transnet, and the City of uMhlathuze traffic authorities. The study employed a qualitative, desktop-based research methodology using secondary data and a PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) (Haddaway, Page, Pritchard, & McGuinness, 2022) systematic literature review to analyse existing studies on traffic management systems and their effectiveness. The PRISMA framework was chosen to ensure a comprehensive and structured review of the data sources. The research questions guiding this study sought to answer the research objectives. Employing these data analysis methods corresponded with the PRISMA framework The findings of the study reveal current traffic congestion trends pose a constant challenge with negative effects, especially on infrastructure, environment and the overall functioning of the city of uMhlathuze, specifically from the N2 and John Ross Highway, leading to the port. The study points to the possible successful implementation of a developed data management system which will result in Richards Bay’s truck congestion significantly reduced, freeing up traffic law enforcement services for other critical tasks while addressing environmental and infrastructural concerns. This study serves not only as a scholarly contribution but also as a practical solution to a pressing issue which has effects on the daily lives the Richards Bay residents. The study recommended the development of a system to address traffic delays, reduce emissions from idling trucks, and optimise road use to prevent further damage to local infrastructure. In the application, the system tracks heavy trucks using sensors, automatic number plate recognition (ANPR) cameras, and an app that allows truck drivers to check in and monitor their queue positions. By providing real-time updates on truck movements and daily handling capacity, the system aims to reduce traffic congestion, improve decision-making for traffic authorities, and ease the burden on local traffic law enforcement officials, who are often preoccupied with managing this truck traffic rather than strengthening law enforcement. This study also provides recommendations to address other broader logistics challenges.