Data-driven Predictive Modeling of Traffic and Air Flow for the Improved Efficiency of Tunnel Ventilation Systems

Tunnel ventilation systems are strictly controlled by safety regulations. Such regulations define not only their operating conditions during fire situations, but also the way in which they should be activated when the accumulation of pollutant gases reaches certain thresholds that are considered unsafe. In addition to these exceptional circumstances, evacuation of tunnel gases is produced naturally on a regular basis, due to causes like air currents originated in pressure differences among the tunnel portals, or the well known piston effect, as a result of vehicles pushing the air when they pass. This work elaborates on the prediction of air-flow inside the tunnels boosted by traffic flow prediction, in order to assist the system activation, be it automated or manual. After experiments made over real tunnel data with a benchmark of machine learning predictive algorithms, results suggest that traffic flow inside the studied tunnels can be effectively predicted and used to enhance air flow predictions, specially in those cases where an air flow predictor alone is not enough to obtain an actionable forecast. The relevance of these results comes from their direct applicability wherein improving the ventilation activation cycles, by adjusting their automation or by informing operators of future air flow levels.