A logistics optimisation engine that enables the transition from fixed routes to a dynamic “checkpoint” model, leveraging Google Maps APIs to account for real-time traffic conditions and operational time windows. The solution uses clustering algorithms to distribute workloads evenly across the fleet, while respecting the volumetric capacity of each vehicle. Instead of relying on costly physical sensors, the system applies data inference from historical collection data and simplified driver inputs to predict container fill levels. By cross-referencing historical routes (GPX), the solution validates route feasibility, delivering a significant reduction in total distance travelled, optimisation of the number of active vehicles, and greater accuracy in budgeting for new operations.