Abstract:
High quality road infrastructure is essential to support economic growth for any landlocked
region, confirmed by the fact that 79% of South African goods use road transport.
Protection of the road infrastructure is implemented by means of overload control
monitoring at Traffic Control Centres (TCCs) on freight corridors linking ports with
economic hubs. As these systems lack the available information to support intelligent
decision-making, 75% to 85% of statically weighed vehicles are legally loaded, resulting in
unnecessary wastage of time and fuel. This paper proposes an intelligent weigh-in-motion
(IWIM) algorithm aiming to decrease unnecessary static weighing of vehicles through data
sharing between TCCs combined with intelligent data interpretation. Several Artificial
Intelligence (AI) models were evaluated for their ability to decrease static weighing of
vehicles while not increasing the number of overloaded vehicles allowed to proceed on the
corridor. We found that a Random Forest Tree model produced the best performance to
differentiate between overloaded and legal vehicles, achieving an average improvement of
65.83% in terms of vehicles to be statically weighed when compared to the current rulebased system. Implementation of the IWIM concept can therefore have a significant
positive impact for all stakeholders involved in the freight movement process.
