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Coupling models of road tunnel traffic, ventilation and evacuation

    Blaž Luin Affiliation
    ; Stojan Petelin Affiliation

Abstract

As road tunnel accidents can result in numerous fatalities and injuries, attention must be paid to accident prevention 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 during 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) interfaces 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, application 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.


First published online 20 February 2020

Keyword : road tunnel, simulation, safety, ventilation, traffic, tunnel fire, emergency, visualization, operator training, incident management

How to Cite
Luin, B., & Petelin, S. (2020). Coupling models of road tunnel traffic, ventilation and evacuation. Transport, 35(3), 336-346. https://doi.org/10.3846/transport.2020.12079
Published in Issue
Jul 9, 2020
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This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Alvear, D.; Abreu, O.; Cuesta, A.; Alonso, V. 2013. Decision support system for emergency management: road tunnels, Tunnelling and Underground Space Technology 34: 13–21. https://doi.org/10.1016/j.tust.2012.10.005

Caliendo, C.; Ciambelli, P.; De Guglielmo, M. L.; Meo, M. G.; Russo, P. 2012a. Numerical simulation of different HGV fire scenarios in curved bi-directional road tunnels and safety evaluation, Tunnelling and Underground Space Technology 31: 33–50. https://doi.org/10.1016/j.tust.2012.04.004

Caliendo, C.; Ciambelli, P.; De Guglielmo, M. L.; Meo, M. G.; Russo, P. 2012b. Simulation of people evacuation in the event of a road tunnel fire, Procedia – Social and Behavioral Sciences 53: 178–188. https://doi.org/10.1016/j.sbspro.2012.09.871

Capote, J. A.; Alvear, D.; Abreu, O.; Cuesta, A.; Alonso, V. 2013. A real-time stochastic evacuation model for road tunnels, Safety Science 52: 73–80. https://doi.org/10.1016/j.ssci.2012.02.006

Carvel, R.; Rein, G.; Torero, J. L. 2009. Ventilation and suppression systems in road tunnels: some issues regarding their appropriate use in a fire emergency, in 2nd International Tunnel Safety Forum for Road and Rail, 20–22 April 2009, Lyon, France, 375–382.

Cassini, P.; Hall, R.; Pons, P. 2003. Transport of Dangerous Goods through Road Tunnels Quantitative Risk Assessment Model. Version 3.60. Reference Manual. OECD/PIARC/EU (CD-ROM).

Cassini, P. 1998. Road transportation of dangerous goods: quantitative risk assessment and route comparison, Journal of Hazardous Materials 61(1–3): 133–138. https://doi.org/10.1016/S0304-3894(98)00117-4

Cha, M.; Han, S.; Lee, J.; Choi, B. 2012. A virtual reality based fire training simulator integrated with fire dynamics data, Fire Safety Journal 50: 12–24. https://doi.org/10.1016/j.firesaf.2012.01.004

EC. 2004. Directive 2004/54/EC of the European Parliament and of the Council of 29 April 2004 on Minimum Safety Requirements for Tunnels in the Trans-European Road Network. Available from Internet: http://data.europa.eu/eli/dir/2004/54/oj

Fritzsche, H.-T. 1994. A model for traffic simulation, Traffic Engineering & Control 35(5): 317–321.

Gandit, M.; Kouabenan, D. R.; Caroly, S. 2009. Road-tunnel fires: risk perception and management strategies among users, Safety Science 47(1): 105–114. https://doi.org/10.1016/j.ssci.2008.01.001

Gipps, P. G. 1981. A behavioural car-following model for computer simulation, Transportation Research Part B: Methodological 15(2): 105–111. https://doi.org/10.1016/0191-2615(81)90037-0

Haghighat, F.; Li, Y.; Megri, A. C. 2001. Development and validation of a zonal model – POMA, Building and Environment 36(9): 1039–1047. https://doi.org/10.1016/S0360-1323(00)00073-1

Hashemkhani Zolfani, S. H.; Esfahani, M. H.; Bitarafan, M.; Zavadskas, E. K.; Arefi, S. L. 2013. Developing a new hybrid MCDM method for selection of the optimal alternative of mechanical longitudinal ventilation of tunnel pollutants during automobile accidents, Transport 28(1): 89–96. https://doi.org/10.3846/16484142.2013.782567

IEC 62381:2012. Automation Systems in the Process Industry – Factory Acceptance Test (FAT), Site Acceptance Test (SAT), and Site Integration Test (SIT).

IEC TS 62603-1:2014. Industrial Process Control Systems – Guideline for Evaluating Process Control Systems – Part 1: Specifications.

Kesting, A.; Treiber, M.; Helbing, D. 2010. Enhanced intelligent driver model to access the impact of driving strategies on traffic capacity, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 368: 4585–4605. https://doi.org/10.1098/rsta.2010.0084

Kesting, A.; Treiber, M.; Helbing, D. 2007. General lane-changing model MOBIL for car-following models, Transportation Research Record: Journal of the Transportation Research Board 1999: 86–94. https://doi.org/10.3141/1999-10

Krajzewicz, D.; Erdmann, J.; Behrisch, M.; Bieker, L. 2012. Recent development and applications of SUMO – simulation of urban mobility, International Journal on Advances in Systems and Measurements 5(3–4): 128–138.

Luin, B.; Petelin, S.; Vidmar, P. 2011. Trojane road tunnel operation during emergency conditions, in ISEP 2011: 19th International Symposium on Electronics in Transport, 28–29 March 2011, Ljubljana, Slovenia, 1–4.

Peacock, R. D.; Forney, G. P.; Reneke, P. A. 2013. CFAST – Consolidated Model of Fire Growth and Smoke Transport (Version 6): Technical Reference Guide. US Department of Commerce, Washington, DC, US. 129 p. https://doi.org/10.6028/NIST.SP.1026r1

Petelin, S.; Luin, B.; Vidmar, P. 2010. Risk analysis methodology for road tunnels and alternative routes, Strojniški vestnik – Journal of Mechanical Engineering 56(1): 41–51.

Sahlin, P. 1996. Modelling and Simulation Methods for Modular Continuous Systems in Buildings. Royal Institute of Technology, Stockholm, Sweden. 187 p.

Sahlin, P.; Grozman, P. 2003. IDA simulation environment a tool for Modelica based end-user application deployment, in Proceedings of the 3rd International Modelica Conference 2003, 3–4 November 2003, Linköping, Sweden, 105–114.

Shi, J.; Ren, A.; Chen, C. 2009. Agent-based evacuation model of large public buildings under fire conditions, Automation in Construction 18(3): 338–347. https://doi.org/10.1016/j.autcon.2008.09.009

Suzuki, K.; Harada, K.; Tanaka, T. 2003. A multi-layer zone model for predicting fire behavior in a single room, Fire Safety Science 7: 851–862. https://doi.org/10.3801/IAFSS.FSS.7-851

Treiber, M.; Kesting, A. 2013. Traffic Flow Dynamics: Data, Models and Simulation. Springer. 503 p. https://doi.org/10.1007/978-3-642-32460-4

Voeltzel, A.; Dix, A. 2004. A comparative analysis of the Mont Blanc, Tauern and Gotthard tunnel fires, Routes/Roads 324: 18–34.

Wan, H.; Du, Z.; Yan, Q.; Chen, X. 2018. Evaluating the effectiveness of speed reduction markings in highway tunnels, Transport 33(3): 647–656. https://doi.org/10.3846/transport.2018.1574

Wiedemann, R. 1974. Simulation des Straßenverkehrsflusses. Instituts für Verkehrswesen der Universität Karlsruhe, Deutschland. 85 S. (in German).

Xu, Z.; Lu, X. Z.; Guan, H.; Chen, C.; Ren, A. Z. 2014. A virtual reality based fire training simulator with smoke hazard assessment capacity, Advances in Engineering Software 68: 1–8. https://doi.org/10.1016/j.advengsoft.2013.10.004