Transportation of Dangerous goods by road may have serious consequences in case of an accident occur. The consequences of a road accident of a heavy goods vehicle carrying dangerous goods may affect not only the truck driver but also the nearby population present. Routing selection is a complicated issue influenced from a number of parameters that may vary during the day, a week or a month. The purpose of the research is to develop a preliminary tool concerning the Transportation of Dangerous Goods which will prove whether a risk analysis using real time data (traffic flows, meteorological conditions, etc.) can offer higher level of safety to the society and the personnel involved in the transportation. The final goal is to enhance safety by making the Dangerous Goods (DG) Risk transportation totally digitalized as a risk management process with real time data acquisition and real time risk assessment through an online platform linked with Global Positioning System (GPS). During the research the risk analysis conducted taking into account all critical parameters for two selected routes. All the necessary data derived from annual statistical data and “simulated” real time data. Data collected concerned all the critical parameters and constraints in order to compare the results to be comparable. Risk quantification was implemented using the DG Quantitative Risk Assessment Model (QRAM) and was illustrated in terms of F/N curves. The results of this research were compared with the ones existed till today which are calculated based on annual statistical data for the above-mentioned factors. The results obtained were compared by means of Societal Risk expressed by Expected Value (EV) and showed that specific factors affect the final routing selection because of the calculated risk.
Cite this paper
Vagiokas, N. and Zacharias, C. (2021). Tool for Analyzing the Risks in Dangerous Goods Transportation. Open Access Library Journal, 8, e7373. doi: http://dx.doi.org/10.4236/oalib.1107373.
Toumazis, I. and Kwon, C. (2013) Routing Hazardous Materials on Time-Depen- dent Networks Using Conditional Value-at-Risk. Transportation Research Part C: Emerging Technologies, 37, 73-92. https://doi.org/10.1016/j.trc.2013.09.006
Chakrabarti, U.K. and Parikh, J.K., (2013) A Societal Risk Study for Transportation of Class-3 Hazmats—A Case of Indian State Highways. Process Safety and Environmental Protection, 91, 275-284. https://doi.org/10.1016/j.psep.2012.06.009
Cappanera, P. and Nonato, M., (2014) The Gateway Location Problem: A Cost Oriented Analysis of a New Risk Mitigation Strategy in Hazmat Transportation. Procedia-Social and Behavioral Sciences, 111, 918-926.
https://doi.org/10.1016/j.sbspro.2014.01.126
Mahmoudabadi, A. and Seyedhosseini, S.M. (2013) Developing a Chaotic Pattern of Dynamic Hazmat Routing Problem. IATSS Research, 37, 110-118.
https://doi.org/10.1016/j.iatssr.2013.06.003
OECD (2001) Transport of Dangerous Goods through Road Tunnels. Research Report, January 2001.
https://www.itf-oecd.org/safety-tunnels-transport-dangerous-goods-through-road-tunnels
Hall, R., Knoflacher, H. and Pons, P. (2006) Quantitative Risk Assessment Model for Dangerous Goods Transport through Road Tunnels. Piarc Internet, RoutesRoads 2006, No. 329. https://www.piarc.org/ressources/publications/3/5089,RR329-UK.pdf
Vagiokas, N. (2018) Developing a Methodology for Determining Design and Operating Requirements for Contracting Tunnel Works. Open Access Library Journal, 5, Article ID: e4741. https://doi.org/10.4236/oalib.1104741
14th Census of Population in Cyprus (2011) Statistical Service of Cyprus.
https://www.mof.gov.cy/mof/cystat/statistics.nsf/census-2011_cystat_en/census-2011_cystat_en