Predictive Model for Traffic Control in Underground Mines

Abstract: Due to the nature of tunnels, a driver in an underground mine may find themselves driving without much vision of the road up ahead. The tunnels usually allow for traffic in both directions but are often only wide enough for a single vehicle. To let vehicles pass each other meeting slots have been carved into the tunnel walls, where one can park while the other passes. Because of the limited vision, however, it is unlikely that a meeting with another vehicle will occur directly next to such a meeting slot. Instead, one of the vehicles must reverse to the closest meeting slot in order to let the other pass. This makes mine tunnels a very inhospitable driving environment, causing disruptions to traffic flow throughout the mine. Unfortunately, typical traffic management or scheduling solutions are not useful, as real-time positioning for the vehicles is often poor while network connectivity cannot be guaranteed in the mine environment. This thesis presents a solution which will avoid situations where a driver needs to back up, and instead present meeting slots in which to park ahead of time. This is done by calculating velocity probability distributions for road segments from historical data and using these to estimate arrival times to meeting slots. In addition, a more comprehensive solution is presented, taking into account the accuracy of positioning, outdated information due to poor connections and more complicated scenarios. The results show that estimating arrival times using only historical data is a very feasible technology, which can realistically be implemented today. Such an implementation could, in the author's opinion, improve driver safety and efficiency significantly, compared to a driver having no information or simply knowing rough positions of nearby vehicles. This being said, there are still steps that can be taken to improve the solution and to develop a more comprehensive system overall.