Quantitative Risk Assessment on the Transport of Dangerous Goods Vehicles Through Unidirectional Road Tunnels: An Evaluation of the Risk of Transporting Hydrogen.

A quantitative risk analysis (QRA) concerning dangerous goods vehicles (DGVs), including also vehicles for the transport of liquid hydrogen (LH2 TVs), running through unidirectional motorway tunnels was performed. An event tree was built, and a wide parametric analysis based on different geometric and traffic characteristics of tunnels was carried out. The effects of the annual average daily traffic (AADT) per lane, the tunnel length (L), the percentage both of heavy goods vehicles (HGVs) and DGVs (for a given 7% of LH2 TVs) were investigated. The results in terms of social risk, as expressed by F/N curves and the expected value (EV), show an increased risk level with the presence of the hydrogen transported, and with certain F/N curves that might also lie above the acceptability limit. This means that additional safety measures should be implemented in order to reduce the risk level or that, alternatively, appropriate strategies of traffic control systems should be taken. A statistical modeling for developing a predictive method of the EV is also performed. The outcomes show that the regression coefficients have the signs expected. In particular, the EV increases with the tunnel length (L), the AADT, and the percentage both of HGVs and DGVs. However, the magnitude of estimated coefficients indicates that the expected value EV increases more with the traffic (AADT per lane, HVGs, or DGVs) than the tunnel length. The application of the approximate method might help the Tunnel Management Agencies (TMAs) in making quick decisions, at a preliminary stage, about temporarily allowing, forbidding or limiting the circulation of DGVs and/or LH2 TVs through tunnels; and subsequently investigating in greater depth the potential hazards due to the transport of hydrogen in the worst cases individualized.

Quantitative Risk Analysis on the Transport of Dangerous Goods Through a Bi-Directional Road Tunnel.

A quantitative risk analysis (QRA) regarding dangerous goods vehicles (DGVs) running through road tunnels was set up. Peak hourly traffic volumes (VHP), percentage of heavy goods vehicles (HGVs), and failure of the emergency ventilation system were investigated in order to assess their impact on the risk level. The risk associated with an alternative route running completely in the open air and passing through a highly populated urban area was also evaluated. The results in terms of social risk, as F/N curves, show an increased risk level with an increase the VHP, the percentage of HGVs, and a failure of the emergency ventilation system. The risk curves of the tunnel investigated were found to lie both above and below those of the alternative route running in the open air depending on the type of dangerous goods transported. In particular, risk was found to be greater in the tunnel for two fire scenarios (no explosion). In contrast, the risk level for the exposed population was found to be greater for the alternative route in three possible accident scenarios associated with explosions and toxic releases. Therefore, one should be wary before stating that for the transport of dangerous products an itinerary running completely in the open air might be used if the latter passes through a populated area. The QRA may help decisionmakers both to implement additional safety measures and to understand whether to allow, forbid, or limit circulation of DGVs.

A crash-prediction model for road tunnels.

Considerable research has been carried out into open roads to establish relationships between crashes and traffic flow, geometry of infrastructure and environmental factors, whereas crash-prediction models for road tunnels, have rarely been investigated. In addition different results have been sometimes obtained regarding the effects of traffic and geometry on crashes in road tunnels. However, most research has focused on tunnels where traffic and geometric conditions, as well as driving behaviour, differ from those in Italy. Thus, in this paper crash prediction-models that had not yet been proposed for Italian road tunnels have been developed. For the purpose, a 4-year monitoring period extending from 2006 to 2009 was considered. The tunnels investigated are single-tube ones with unidirectional traffic. The Bivariate Negative Binomial regression model, jointly applied to non-severe crashes (accidents involving material-damage only) and severe crashes (fatal and injury accidents only), was used to model the frequency of accident occurrence. The year effect on severe crashes was also analyzed by the Random Effects Binomial regression model and the Negative Multinomial regression model. Regression parameters were estimated by the Maximum Likelihood Method. The Cumulative Residual Method was used to test the adequacy of the regression model through the range of annual average daily traffic per lane. The candidate set of variables was: tunnel length (L), annual average daily traffic per lane (AADTL), percentage of trucks (%Tr), number of lanes (NL), and the presence of a sidewalk. Both for non-severe crashes and severe crashes, prediction-models showed that significant variables are: L, AADTL, %Tr, and NL. A significant year effect consisting in a systematic reduction of severe crashes over time was also detected. The analysis developed in this paper appears to be useful for many applications such as the estimation of accident reductions due to improvement in existing tunnels and/or to modifications of traffic control systems, as well as for the prediction of accidents when different tunnel design options are compared.

A crash-prediction model for multilane roads.

Considerable research has been carried out in recent years to establish relationships between crashes and traffic flow, geometric infrastructure characteristics and environmental factors for two-lane rural roads. Crash-prediction models focused on multilane rural roads, however, have rarely been investigated. In addition, most research has paid but little attention to the safety effects of variables such as stopping sight distance and pavement surface characteristics. Moreover, the statistical approaches have generally included Poisson and Negative Binomial regression models, whilst Negative Multinomial regression model has been used to a lesser extent. Finally, as far as the authors are aware, prediction models involving all the above-mentioned factors have still not been developed in Italy for multilane roads, such as motorways. Thus, in this paper crash-prediction models for a four-lane median-divided Italian motorway were set up on the basis of accident data observed during a 5-year monitoring period extending between 1999 and 2003. The Poisson, Negative Binomial and Negative Multinomial regression models, applied separately to tangents and curves, were used to model the frequency of accident occurrence. Model parameters were estimated by the Maximum Likelihood Method, and the Generalized Likelihood Ratio Test was applied to detect the significant variables to be included in the model equation. Goodness-of-fit was measured by means of both the explained fraction of total variation and the explained fraction of systematic variation. The Cumulative Residuals Method was also used to test the adequacy of a regression model throughout the range of each variable. The candidate set of explanatory variables was: length (L), curvature (1/R), annual average daily traffic (AADT), sight distance (SD), side friction coefficient (SFC), longitudinal slope (LS) and the presence of a junction (J). Separate prediction models for total crashes and for fatal and injury crashes only were considered. For curves it is shown that significant variables are L, 1/R and AADT, whereas for tangents they are L, AADT and junctions. The effect of rain precipitation was analysed on the basis of hourly rainfall data and assumptions about drying time. It is shown that a wet pavement significantly increases the number of crashes. The models developed in this paper for Italian motorways appear to be useful for many applications such as the detection of critical factors, the estimation of accident reduction due to infrastructure and pavement improvement, and the predictions of accidents counts when comparing different design options. Thus this research may represent a point of reference for engineers in adjusting or designing multilane roads.