The Use of Microscopic Traffic Flow Simulator to Assess the Safety of Road Tunnels

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By Aleksander Król, Małgorzata Król, Silesian University of Technology, Poland

This is the short version of the article:

The factors determining the number of the endangered people in a case of fire in a road tunnel, Fire Safety Journal 111 (2020) 102942, doi.org/10.1016/j.firesaf.2019.102942

Introduction

Road tunnels are fundamental elements of a road network. The incidents in road tunnels are rarer than at other sections of the transportation network because a tunnel itself calms down the traffic [1,2]. However, on the other hand if an accident happens, its consequences are more severe than in other places [3]. The threatened are not only the persons directly involved in the accident, but all the people in the tunnel as well. This is because of the possibility of a fire outbreak and even a not fully developed fire causes a significant part of the tunnel filled with toxic and hot fire gases. This dangerous for a human health and life zone is expanding fast, covering the successive parts of a tunnel in a few minutes. Therefore, it is extremely important that people in the tunnel undertake the self-evacuation in the initial phase of a fire development. For this to happen all the tunnel safety systems have to operate properly and support the self-rescue of the people [4–8].

To best design the process of self-rescue of people from a road tunnel during a fire, appropriate fire scenarios should be prepared. In the case of fire scenarios for road tunnels, it seems crucial to determine the number of people staying in the tunnel when a fire breaks out. The number of people whose health and life could be threatened in a case of fire in a road tunnel depends on the number of vehicles, which are trapped inside and the filling of vehicles. In turn the number of trapped vehicles depends on traffic conditions, the traffic mode, the number of lanes and the location of a road accident.

The determination of the number of trapped vehicles is a complex task. To solve it some theoretical models or numerical simulation of road traffic can be applied, additionally statistical data based on traffic measurements must be provided. The article proposes to use the commercial VISSIM program to determine the number of vehicles  remaining in the tunnel during the fire. This allows to determine the number of threatened people.

Modeling of traffic and congestion formation

Nowadays, there are many commercial (VISSIM) or non-commercial (TRANSIMS, SUMO) software packages, which in satisfactory degree of reliability model road traffic in different spatial scales [9-11]. In the simulation approach the idea of cellular automata is commonly applied.

Eventually, the VISSIM software was selected to simulation the process of a congestion formation as a result of a road accident. VISSIM is a microscopic traffic flow simulator developed by Planung Transport Verkehr (Germany). Term ‘microscopic’ means that every real entity (a vehicle, a pedestrian, a traffic light, a road lane) involved in the traffic is simulated individually. VISSIM implements the principle ‘car following’ introduced by Widemann [12]. It is a psychophysical model of a driver behavior: he slows down when approaching to the preceding vehicle and accelerates when this vehicle moves away. The key issue is that perceiving of the distance between cars is subjective and the drivers’ reaction is often excessive and a bit delayed, what leads to alternated braking and acceleration. VISSIM allows for building a detailed model of a transportation network with accurately adjusted traffic parameters. The parameters like the traffic intensity or the generic structure of the traffic are set as average values, but the software generates traffic flows randomly. The seed of the pseudorandom numbers generator is adjustable, so particular simulations can be repeated.

Traffic conditions depend on the traffic intensity expressed by the number of vehicles passing per one lane and hour. The fundamental relation binds the basic quantities describing the road traffic:

                     Q=Kv                                           

where  Q         - average traffic intensity [veh/h],

            K         - average traffic density [veh/km]

            v          - average traffic velocity [km/h].

Despite an apparent simplicity this relation is complex due to non-linear dependence of traffic velocity on traffic density (Figure 1).

Fig. 1. Relation between basic quantities describing the road traffic

Traffic conditions can vary from a free flow, where each driver chooses his speed at his own to the congestion state, where the vehicles density takes the maximum value and the average velocity becomes close to zero (traffic breakdown). In the road traffic engineering the traffic conditions can be qualitatively described by six Levels of Service called briefly LOS [13]. They are coded from A (completely free flow) to F (congestion state), what is shown schematically in Figure 1.

For purposes of such research two opposed states was selected: an almost free traffic of LOS B (1000 veh/h/lane) and a state close to a congestion of LOS F (2800 veh/h/lane). Such choice is somewhat arbitral, however it covers two quite different but likely states of road traffic. The values of traffic intensity were so adopted to meet the definitions of relevant degrees of Levels of Service. The congestion state was additionally forced by introducing of a zone of lowered speed of 10 km/h at some distance behind the exit portal. The generic structure of vehicles was adopted according to the averaged data from General Traffic Measurement 2015 shown in Table 1.

Table 1. Generic structure of vehicles [29].

Figure 2 shows the examples of simulated congestion which were formed in the tunnel. For different traffic conditions (LOS B or LOS F), different accident locations (in the middle or near one of the tunnel portals) and different traffic modes (uni or bi-directional) six runs of VISSIM are carried out. It is a state at 15 seconds after the alarm triggering and the tunnel entrance closing. It corresponds to a situation, in which all the tunnel users should be aware what has happened and are making the decision on their actions. As can be seen the stochastic nature of the traffic and the congestion formation resulted in significant differences of the number of trapped vehicles and peoples.

Figure 2. Examples of the formed congestions.

Application of traffic modeling results

The results obtained from the VISSIM program was used to model evacuation from the road tunnel. To this end, an evacuation model should be built for each case that is the result of VISSIM calculations. It is still necessary to propose the vehicles manning. The vehicles manning can be generated randomly in a way to get the accordance with the measurements of the real fillings. The adopted average values are show in Table 2. This table shows the lengths of modeled vehicles as well. All vehicles were of the same width equal to 2 m. The vehicles can be modeled as spaces delimited by walls and equipped with doors. The inner space of a vehicle was divided by bulkheads to prevent the evacuees to shorten their way by crossing a vehicle. The examples of the models of vehicles made in PATHFINDER are shown in Figure 3.

Table 2. Average vehicles manning

Figure 3. Models of a passenger car, a HGV and a bus with exemplary manning made in PATHFINDER software.

Given the number of vehicles trapped in the tunnel, they can be used in any evacuation modeling program. The parameters of evacuees’ behavior are adopted in accordance to literature review, however the available data differ significantly depending on reported researches. The delay time before evacuation start and the movement speed are random variables of normal distribution. Mean values and standard deviations are adopted to cover most of literature data and to account for diversity of physical fitness.

Conclusion

The safety study on using road tunnels requires preparing fire scenarios for each facility. Efforts are being made to ensure that the fire scenarios reproduce the tunnel conditions as accurately as possible during the evacuation. However, many factors that influence the course of this evacuation must be assumed at the beginning of creating such a scenario. One of them is the number of cars involved in a road accident or trapped in a tunnel during a fire. This number directly affects the assumed number of people who will evacuate from the tunnel. Therefore, determining the number of cars is important to make the fire scenarios created to assess the safety of road tunnels real. The use of traffic modeling to obtain this information may be a solution.

References

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[4] Kashef, A.Z. & Benichou, N. (2008) Investigation of the performance of emergency ventilation strategies in the event of fire in a road tunnel-a case study, J. Fire Prot. Eng. 18 (3).

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[8] VDI 6029, (2000) Ventilation Plants for Road Tunnels, Verein Deutscher Ingenieure (in german).

[9] Ehlert, A. Schneck, A. & Chanchareon, N. (2017) Junction parameter calibration for mesoscopic simulation in Vissim, Transp. Res. Procedia. 21, 216–226.

[10] Lee, K.S. Eom, J.K. & Moon, D. (2014) Applications of TRANSIMS in transportation: a Literature review, Procedia. Comput. Sci. 32, 769–773.

[11] Pribyla, O. Pribyla, P. Horaka, T. (2017) System for deterministic risk assessment in road tunnels, Procedia. Eng. 192, 336–341.

[12] Wiedemann, R. (1974) Simulation des Strassenverkehrsflusses, Band 8, Schriftenreihe des Instituts für Verkehrswesen der Universit€at Karlsruhe (in german).

[13] Transportation Research Board, (2000) Highway Capacity Manual, TRB, Washington, D.C. ISBN 0-309-06681-6.

Aleksander Król and Małgorzata Król are with Silesian University of Technology, Poland.