TRANSPORT ISSN 1648-4142 / eISSN 1648-3480 2020 Volume 35 Issue 3: 336–346 https://doi.org/10.3846/transport.2020.12079 COUPLING MODELS OF ROAD TUNNEL TRAFFIC, VENTILATION AND EVACUATION Blaž LUIN*, Stojan PETELIN Faculty of Maritime Studies and Transport, University of Ljubljana, Slovenia Received 2 October 2018; revised 31 July 2019; accepted 6 November 2019; first published online 20 February 2020 Abstract. As road tunnel accidents can result in numerous fatalities and injuries, attention must be paid to accident pre- vention and management. To address this issue, use of integrated tunnel model for system evaluation and training of road tunnel operators on computer simulator is presented. A unified tunnel model, including traffic, meteorological conditions, ventilation and evacuation that is presented. An overview of simulation models, simulator architecture and challenges dur- ing the development are discussed. The integrated tunnel model is used as a core of a simulation system that is capable of reproducing tunnel accidents in real time and it interfaces with Supervisory Control And Data Acquisition (SCADA) in- terfaces used in real tunnel control centres. It enables operators to acquire experience they could otherwise get only during major accidents or costly exercises. It also provides the possibility for evaluation of tunnel control algorithms and Human Machine Interfaces (HMIs) for efficient operation of all safety systems during upgrades and maintenance. Finally, applica- tion of the model for accident analysis and optimization of emergency ventilation control is presented where it was used to identify cause of emergency ventilation malfunction and design fault. Keywords: road tunnel, simulation, safety, ventilation, traffic, tunnel fire, emergency, visualization, operator training, incident management. Introduction Although there are plenty of efforts to increase the level As prescribed by the directive, several risk assess- of safety, the tunnels remain areas of increased risk as ment methodologies evolved. The most widely used is they are semi-enclosed spaces where explosions, fires and the World Road Association (PIARC) and Organisation smoke can have more devastating effects than in the open for Economic Co-operation and Development (OECD) spaces. There is a great deal of focus on tunnel safety after Quantitative Risk Assessment Model (QRAM) (Cassini catastrophic road tunnel accidents that have occurred in et al. 2003; Cassini 1998). It assumes many different sce- the past, such as the Mont Blanc, Tauern and Gotthard narios of dangerous and non-dangerous goods accidents. tunnel fires whose causes were widely analysed and de- When evaluation of risk minimization measures that were scribed (Voeltzel, Dix 2004). The Mont Blanc fire in 1999 not included in the methodology were needed, extensions resulted in 39 fatalities, the Tauern tunnel fire in the same to the methodology were developed (Petelin et al. 2010). year resulted in 12 fatalities and 42 injured and 60 vehicles Results from tunnel risk analyses are key data for imple- collided during the accident. As a result, the EU issued a mentation of risk mitigation measures, which may be also Directive 2004/54/EC on tunnel safety to prescribe mini- be chosen by consulting expert opinions and multi-crite- mum safety requirements for road tunnels that requires ria analyses (Hashemkhani Zolfani et al. 2013). The results mandatory risk assessment for all road tunnels on the obtained with the above-mentioned methodologies often trans-European road network that are longer than 500 m pointed out importance of prompt accident detection and (EC 2004). A reminder that tunnel safety remains an in- response in order to minimize consequences. The tunnel teresting topic is the Yanhou tunnel fire (China) that oc- operator response in case of emergency is crucial as it is curred in 2014 and resulted in 40 fatalities. the first who should take actions including informing and *Corresponding author. E-mail: [email protected] Copyright © 2020 The Author(s). Published by VGTU Press This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unre- stricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Transport, 2020, 35(3): 336–346 337 coordinating other emergency services. For this reason, except in some rare cases where 2D simulation is assumed. risk for tunnel users can vary greatly due to the great vari- Among the most popular are the Fritzsche (1994), Wiede- ability of tunnel operator responses and response times mann (1974) and IDM (Intelligent Driver Model) – Kest- (Gandit et al. 2009). ing et al. (2007, 2010), Treiber (2013), Gipps (1981) mod- Experience from past accidents showed that the road els. It is common among them that speed, acceleration and tunnel operators were often not sufficiently prepared be- longitudinal position are continuous variables and lateral cause they never experienced a major emergency event. position (lane) is a discrete variable. The Fritzsche (1994) While minor incidents occur daily, major tunnel accidents and Wiedemann (1974) approaches to modelling repre- such as fires and explosions occur rarely. Some operators sent real driver actions more realistically as they take into might experience such an event once in a career, some consideration physiological limitation of human drivers, never; therefore, lacking sufficient experience they could especially in terms of ability to sense and react to changes not handle situation in an optimum way. in speed. To sum up, interactions between traffic and ventila- Most of the traffic microsimulation software uses a tion control and communication between tunnel users particular vehicle following model that cannot be changed and emergency services can be complex and demanding or mixed with different models; for example, VISSIM to understand. Improper interaction can increase dam- (http://vision-traffic.ptvgroup.com) uses the Wiede- age and the number of fatalities (Carvel et al. 2009). For mann model, paramics uses Fritzsche and AIMSUN example, if air velocity is too high, more oxygen might (http://www.aimsun.com) uses a modification of the Gipps help a fire spread faster or in the wrong direction - air model (Wiedemann 1974; Fritzsche 1994; Gipps 1981). might push smoke into tunnel users and emergency ser- For research purposes Simulation of Urban MObility vices. One way to approach complexity of tunnel control (SUMO) tool is becoming increasingly popular as it is is through proposed automated decision-making system open source and allows modifications to be made to any (Alvear et al. 2013). part of the code (Krajzewicz et al. 2012). Even without The problem of efficient tunnel control could also be modifications of the code, it provides many configuration approached in a similar way as in the aerospace, chemical options not usually found in other packages such as choice and nuclear industries – by training on computer simu- of a vehicle following, lane changing and emissions mod- lators. These industries have a long history of standard- els and mixing of them in the same simulation scenario. ized equipment testing prior to the commissioning and It is common among all these models that lane is a personnel training according to the standards IEC TS discrete variable and longitudinal position is a continuous 62603-1:2014 and IEC 62381:2012. In the standard IEC variable. This causes some difficulties during visualization TS 62603-1:2014, Factory Acceptance Tests (FAT) and where smooth movements are required for realistic repre- Site Acceptance Tests (SAT) it is specified what evalua- sentation. Similar issues are found when using such mod- tions must be carried out on Human-Machine Interfaces els in driving simulators, which often use 3D virtual real- (HMIs), controllers, terminations, fields and process defi- ity interfacing in order to provide realistic visualization for nitions; provisions for life cycle support, including person- evaluation of driver responses (Wan et al. 2018). Real-time nel training are specified, also, a demo unit of a system at the site is suggested. and faster computation is easily achieved using the traffic The aim of this work is to develop a unified model microsimulation models, where the road network is a sin- of a road tunnel traffic, ventilation and evacuation. It can gle road tunnel with surrounding road sections. serve as a core of a computer-based simulator, which in- cludes traffic, ventilation and evacuation sub-models that 1.2. Tunnel ventilation models are computed in real time, representing a demo unit for There are three basic approaches to simulating tunnel a road tunnel. It could not only be applied for operator ventilation: 1D, zone and Computational Fluid Dynamics training, but also for evaluation of different accident sce- (CFD) models. 1D models are comprised of continuity, narios during the tunnel design and risk analyses. There- momentum and energy conservation equations. Simpli- fore, an overview of state-of-the-art models that were con- fied versions assume stationary conditions and use vari- sidered to be incorporated into the simulator will follow ations of the Bernoulli equation for flow estimation. A in the next sections. notable model of this kind is the IDA Tunnel Ventilation Model (Sahlin 1996; Sahlin, Grozman 2003). Zonal mod- 1. Approaches to tunnel traffic, ventilation els assume