Spatial-temporal prediction of the environmental conditions inside an urban road tunnel during an incident scenario

Nowadays, environmental conditions inside tunnel have attracting more and more public attentions. The occurrence of traffic accident will greatly change the spatiotemporal distribution of traffic flow, and further influence the environmental condition inside tunnel. However, very few research can be found in literature. To fill this gap, a multi-segments model is developed by considering the spatial-temporal characteristics of traffic flow. Detailed field measurements have been conducted in an urban tunnel with car accident during the evening rush hours in a winter day. The air temperature, air velocity, CO concentration, CO2 concentration and PM concentration are measured simultaneously. The proposed theoretical multi-segments model agrees well with the measurement results. The occurrence of the accident directly influences the air velocity and CO concentration near the accident location, and has no noticeable effect on air temperature. The air velocity in the region prior to accident location decreases significantly from 3.7 to 4 m/s to 1.3-1.4 m/s. The massive traffic jam quickly propagates upstream. The CO concentration gradient (delta C/delta C/delta x) after the accident is about 3.4-9 times of the pre accident value. This research can provide real-time support to the tunnel management and improve air quality inside tunnel.