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 (AADT_L), percentage of trucks (%Tr), number of lanes (N_L), and the presence of a sidewalk. Both for non-severe crashes and severe crashes, prediction-models showed that significant variables are: L, AADT_L, %Tr, and N_L. 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 novel approach for analyzing severe crash patterns on multilane highways

This study presents a novel approach for analysis of patterns in severe crashes that occur on mid-block segments of multilane highways with partially limited access. A within stratum matched crash vs. non-crash classification approach is adopted towards that end. Under this approach crashes serve as units of analysis and it does not require aggregation of crash data over arterial segments of arbitrary lengths. Also, the proposed approach does not use information on non-severe crashes and hence is not affected by under-reporting of the minor crashes. Random samples of time, day of week, and location (i.e., milepost) combinations were collected for multilane arterials in the state of Florida and matched with severe crashes from the corresponding corridor to form matched strata consisting of severe crash and non-crash cases. For these cases, geometric design/roadside and traffic characteristics were derived based on the corresponding milepost locations. Four groups of crashes, severe rear-end, lane-change related, pedestrian, and single-vehicle/off-road crashes, on multilane arterials segments were compared separately to the non-crash cases. Severe lane-change related crashes may primarily be attributed to exposure while single-vehicle crashes and pedestrian crashes have no significant relationship with the ADT (Average Daily Traffic). For severe rear-end crashes speed limit, ADT, K-factor, time of day/day of week, median type, pavement condition, and presence of horizontal curvature were significant factors. The proposed approach uses general roadway characteristics as independent variables rather than event-specific information (i.e., crash characteristics such as driver/vehicle details); it has the potential to fit within a safety evaluation framework for arterial segments.     

Models relating traffic safety with road environment and traffic flows on arterial roads in Addis Ababa

This paper presents the study carried out to develop accident predictive models based on the data collected on arterial roads in Addis Ababa. Poisson and negative binomial regression methods were used to relate the discrete accident data with the road and traffic flow explanatory variables. Significant accident predictive models were found with a number of significant explanatory variables. The results show that the existing inadequate road infrastructure and poor road traffic operations are the potential contributors of this ever-growing challenge of the road transport in Addis Ababa. The results also indicate that improvements in roadway width, pedestrian facilities, and access management are effective in reducing road traffic accidents.     

Development of serviceability prediction model for county paved roads

This paper developed a pavement serviceability prediction model for county paved roads. Most county paved roads were built decades ago without following minimum design standards. The recent increase in industrial/mineral activities in the State of Wyoming required developing a pavement management system (PMS) for local paved roads. The developed PMS used the pavement serviceability index (PSI) as a pavement performance parameter. The proposed PSI model for local roads is based on: international roughness index, pavement condition index (PCI) and rut depth for flexible pavements only. Ten panellists from Wyoming rated 30 pavement sections that were randomly selected at different distresses’ levels; using two vehicles (SUV and Sedan). The statistical analysis indicated that the seating position, age and gender are not significant to the rating process. However, the vehicle’s type found to be significant. The newly developed model from this study explains 80% of the variations in the PSI values of county roads (adjusted R² = 0.80). In addition, the new model seems to provide more realistic representation of the conditions of county roads than the statewide model used on the state’s highway system.     

A multinomial logit model-Bayesian network hybrid approach for driver injury severity analyses in rear-end crashes

Rear-end crash is one of the most common types of traffic crashes in the U.S. A good understanding of its characteristics and contributing factors is of practical importance. Previously, both multinomial Logit models and Bayesian network methods have been used in crash modeling and analysis, respectively, although each of them has its own application restrictions and limitations. In this study, a hybrid approach is developed to combine multinomial logit models and Bayesian network methods for comprehensively analyzing driver injury severities in rear-end crashes based on state-wide crash data collected in New Mexico from 2010 to 2011. A multinomial logit model is developed to investigate and identify significant contributing factors for rear-end crash driver injury severities classified into three categories: no injury, injury, and fatality. Then, the identified significant factors are utilized to establish a Bayesian network to explicitly formulate statistical associations between injury severity outcomes and explanatory attributes, including driver behavior, demographic features, vehicle factors, geometric and environmental characteristics, etc. The test results demonstrate that the proposed hybrid approach performs reasonably well. The Bayesian network reference analyses indicate that the factors including truck-involvement, inferior lighting conditions, windy weather conditions, the number of vehicles involved, etc. could significantly increase driver injury severities in rear-end crashes. The developed methodology and estimation results provide insights for developing effective countermeasures to reduce rear-end crash injury severities and improve traffic system safety performance.     

Medical treatment and traffic fatality reductions in industrialized countries

Reductions in traffic-related fatalities in developed industrialized countries have been substantial in the last 30 years. Most analyses have attributed this reduction to changes in vehicle design, better road design, increased seat-belt use, and reductions in driving under the influence of alcohol. This paper analyses the impact of improvements in medical treatment and technology. Data from the International Road and Traffic Accident Database (IRTAD), which includes all developed countries, was used in combination with Organization for Economic Cooperation and Development (OECD) Health Care data. Using proxy measures to account for improvements in medical treatment and technology it is found that these proxy variables are significant and capture much of the residual time trend in the data when they are omitted. Changes in age cohorts, such as fewer young people, also have contributed to a reduction in fatalities. These results suggest that medical technology improvements are associated with reductions in traffic-related fatalities over time.     

A family of numerical schemes for kinematic flows with discontinuous flux

Multiphase flows of suspensions and emulsions are frequently approximated by spatially one-dimensional kinematic models, in which the velocity of each species of the disperse phase is an explicitly given function of the vector of concentrations of all species. The continuity equations for all species then form a system of conservation laws which describes spatial segregation and the creation of areas of different composition. This class of models also includes multi-class traffic flow, where vehicles belong to different classes according to their preferential velocities. Recently, these models were extended to fluxes that depend discontinuously on the spatial coordinate, which appear in clarifier–thickener models, in duct flows with abruptly varying cross-sectional area, and in traffic flow with variable road surface conditions. This paper presents a new family of numerical schemes for such kinematic flows with a discontinuous flux. It is shown how a very simple scheme for the scalar case, which is adapted to the “concentration times velocity” structure of the flux, can be extended to kinematic models with phase velocities that change sign, flows with two or more species (the system case), and discontinuous fluxes. In addition, a MUSCL-type upgrade in combination with a Runge–Kutta-type time discretization can be devised to attain second-order accuracy. It is proved that two particular schemes within the family, which apply to systems of conservation laws, preserve an invariant region of admissible concentration vectors, provided that all velocities have the same sign. Moreover, for the relevant case of a multiplicative flux discontinuity and a constant maximum density, it is proved that one scalar version converges to a BV _t entropy solution of the model. In the latter case, the compactness proof involves a novel uniform but local estimate of the spatial total variation of the approximate solutions. Numerical examples illustrate the performance of all variants within the new family of schemes, including applications to problems of sedimentation, traffic flow, and the settling of oil-in-water emulsions.     

A mixed-Weibull regression model for the analysis of automotive warranty data

This paper presents a case study regarding the reliability analysis of some automotive components based on field failure warranty data. The components exhibit two different failure modes, namely early and wearout failures, and are mounted on different vehicles, which differ among themselves for car model and engine type, thus involving different operating conditions. Hence, the failure time of each component is a random variable with a bimodal pdf which also depends upon a vector of covariates that indexes the specific operating condition. Then, a mixed-Weibull distribution, where the pdf of each subpopulation (namely the ‘weak’ and ‘strong’ subpopulation) depends on the covariates through the scale parameter, is used to analyze the component lifetime. A Fortran algorithm for the maximum likelihood estimation of model parameters has been implemented and a stepwise procedure, in its backwards version, has been used to test the significance of covariates and to construct the regression model. The presence of a weak subpopulation has been verified and the fraction of weak units in the population has also been estimated. Finally, the adequacy of the proposed model to fit the observed data has been assessed.     

A semiparametric negative binomial generalized linear model for modeling over-dispersed count data with a heavy tail: Characteristics and applications to crash data

Crash data can often be characterized by over-dispersion, heavy (long) tail and many observations with the value zero. Over the last few years, a small number of researchers have started developing and applying novel and innovative multi-parameter models to analyze such data. These multi-parameter models have been proposed for overcoming the limitations of the traditional negative binomial (NB) model, which cannot handle this kind of data efficiently. The research documented in this paper continues the work related to multi-parameter models. The objective of this paper is to document the development and application of a flexible NB generalized linear model with randomly distributed mixed effects characterized by the Dirichlet process (NB-DP) to model crash data. The objective of the study was accomplished using two datasets. The new model was compared to the NB and the recently introduced model based on the mixture of the NB and Lindley (NB-L) distributions. Overall, the research study shows that the NB-DP model offers a better performance than the NB model once data are over-dispersed and have a heavy tail. The NB-DP performed better than the NB-L when the dataset has a heavy tail, but a smaller percentage of zeros. However, both models performed similarly when the dataset contained a large amount of zeros. In addition to a greater flexibility, the NB-DP provides a clustering by-product that allows the safety analyst to better understand the characteristics of the data, such as the identification of outliers and sources of dispersion.     

A modified Wenzel model for hydrophobic behavior of nanostructured surfaces

A modified Wenzel model was proposed to explore the influence of pore size distributions (PSDs) on water repellency of nanostructured surfaces. Rough surfaces with different porous structures, including surface areas and PSDs, were fabricated by stacking different solid ratios of TiO₂ nanoparticles. These fluorinated surfaces exhibited an excellent hydrophobic performance with the highest value of contact angle ∼ 165°. The PSDs of these surfaces, determined from Dubinin-Stoeckli equation, were found to vary with the solid ratios. The modified Wenzel model incorporated with the PSDs gave a fairly good prediction in describing the variation of contact angle with surface roughness, which is very close to the experimental data. These results demonstrated that the heterogeneity of surfaces caused by different PSDs would induce the hydrophobic behavior.     

The likelihood of achieving quantified road safety targets: A binary logistic regression model for possible factors

In past several decades, many countries have set quantified road safety targets to motivate transport authorities to develop systematic road safety strategies and measures and facilitate the achievement of continuous road safety improvement. Studies have been conducted to evaluate the association between the setting of quantified road safety targets and road fatality reduction, in both the short and long run, by comparing road fatalities before and after the implementation of a quantified road safety target. However, not much work has been done to evaluate whether the quantified road safety targets are actually achieved. In this study, we used a binary logistic regression model to examine the factors – including vehicle ownership, fatality rate, and national income, in addition to level of ambition and duration of target – that contribute to a target’s success. We analyzed 55 quantified road safety targets set by 29 countries from 1981 to 2009, and the results indicate that targets that are in progress and with lower level of ambitions had a higher likelihood of eventually being achieved. Moreover, possible interaction effects on the association between level of ambition and the likelihood of success are also revealed.     

A disaggregate model for quantifying the safety effects of winter road maintenance activities at an operational level

This research presents a disaggregated modeling approach for investigating the link between winter road collision occurrence, weather, road surface conditions, traffic exposure, temporal trends and site-specific effects. This approach is unique as it allows for quantification of the safety effects of different winter road maintenance activities at an operational level. Different collision frequency models are calibrated using hourly data collected from 31 different highway routes across Ontario, Canada. It is found that factors such as visibility, precipitation intensity, air temperature, wind speed, exposure, month of the winter season, and storm hour have statistically significant effects on winter road safety. Most importantly, road surface conditions are identified as one of the major contributing factors, representing the first contribution showing the empirical relationship between safety and road surface conditions at such a disaggregate level. The applicability of the modeling framework is demonstrated using several examples, such as quantification of the benefits of alternative maintenance operations and evaluation of the effects of different service standards using safety as a performance measure.     

非热弹性马氏体相变的细观本构模型

为了更清楚地认识铁基合金经受非热弹性马氏体相变的本征特性,在细观尺度对非热弹性马氏体相变进行了研究.基于马氏体相变晶体学和内变量本构理论建立了非热弹性马氏体相变的细观本构模型.该模型采用微区相变应变、奥氏体及马氏体的塑性应变表征宏观的非弹性响应,把奥氏体和马氏体变体的等效塑性应变率和体积分数变化率作为内变量描述微观结构变化.模型采用J2流动理论描述微区塑性流动,与采用晶体塑性的描述方法相比模型更简单,且更适用于工程计算.单晶奥氏体单变体简单剪切的模拟结果表明:随着应变的增加,先发生奥氏体塑性变形,进而发生相变,马氏体体积分数与应变呈线性关系;温度较低时易发生马氏体相变并使得材料的强度提高.     

Synthesis of a simulation model for an electromagnetic flowmeter

The principles of constructing a simulation model for an electromagnetic flowmeter are considered by means of which it is possible to carry out research into the metrological characteristics of instruments. __________ Translated from Izmeritel’naya Tekhnika, No. 5, pp. 38–43, May, 2006.     

A modified drag model for power-law fluid-particle flow used in computational fluid dynamics simulation

A modified drag model for the power-law fluid-particle flow considering effects of rheological properties was proposed. At high particle concentrations (ε_s ≥ 0.2), based on the Ergun equation, the cross-sectional shape and the tortuosity of the pore channel are considered, and the apparent flow behavior index and consistency coefficient of the power-law fluid at the surface of the particles are corrected. At low particle concentrations (ε_s < 0.2), based on the Wen-Yu drag model, the modified Reynolds number for power-law fluid and the relational expression between drag coefficient for single particle and Reynolds number that considers the effect of the flow behavior index are adopted. Numerical simulations for the power-law fluid-particle flow in the fluidized bed were carried out using the non-Newtonian drag model. The effects of rheological parameters on the drag coefficient were analyzed. The comparisons of simulation and experiment show that the modified drag model predicts reasonable void fraction under different rheological parameters, particle diameters, and liquid velocities in both low particle concentrations and high particle concentrations. The increase in flow behavior index and consistency coefficient increases the drag coefficient between the two phases and decreases the average particle concentration within the bed.     

A flexible brand choice model based on neural net methodology A comparison to the linear utility multinomial logit model and its latent class extension

Brand choice models as a rule have a linear (deterministic) utility function, i.e. they conceive utility as linear combination of predictors like price, sales promotion variables, brand name and other product attributes. To discover nonlinear effects on brands' utilities in a flexible way we specify deterministic utility by means of a certain type of neural net. This feedforward multilayer perceptron is able to approximate any continuous multivariate function and its derivatives with the desired level of precision. In an empirical study the neural net based choice model leads to better out-of-sample results than homogeneous and heterogeneous versions of linear utility MNL models. On the other hand the latent class variant of the linear utility MNL model attains better fit values for estimation data than the neural net model. The neural net approach implies different choice elasticities for most predictors and identifies nonlinear effects (like interaction effects, thresholds, saturation effects). Received: November 3, 2000 / Accepted: March 7, 2002     

A Correlation Study for the Positional Dependence of Friction Factor in Dilute Phase Flows

Results of a comprehensive study on the flow friction factor data collected on suspensions of solid granular particles of semolina, fly ash, and alumina having a size range of 75.5?µm ≤ d_p ≤ 275?µm in air are presented. The experimental study was composed of 250 separate test cases in which airflow Reynolds number Re = UD/ν and loading ratio M_p/M_a were varied systematically covering the ranges of 50,000 ≤ Re ≤ 100,000 and 5% ≤ M_p/M_a ≤ 25%. An upward flow field with variable inclinations α of 10°, 20°, and 30° with the horizontal plane and a horizontal flow field, i.e, 0° inclination, were used to determine the influence of gravitational forces. Local static pressure gradients measured along the flow field with distance x were used to determine local air-particle friction factor f_{p + a}, which was observed to be under the influence of particle type, M_p/M_a, Re, and flow line inclination, α. The proposed correlation for the variation of f_{p + a} with x/D was evaluated to reach a generalized form exhibiting interactive influence of Re, M_p/M_a?, α, and particle type.     

The impact of driver age on lost life years for other road users in France: A population based study of crash-involved road users

BACKGROUND: One of the concerns in road safety is the threat older drivers may pose to other road users. Using the rate of lost life years, the present study provides a public health approach to quantify this potential threat. METHODS: A total of 1570686 motorised vehicle drivers or motorcycle riders and 652246 non-drivers, i.e. vehicle passengers, pedestrians and cyclists involved in injury crashes in France between 1996 and 2004, were included in a population based cross-sectional study. Fatality rates and rate of lost life years for each crash-involved driver age class were calculated for the drivers themselves and for other road users. RESULTS: The study has shown a significant reduction in the rate of lost life years for crash-involved other road users (whether passengers, pedestrians, cyclists or opposing drivers) as driver age increases. Other road users lost half as many years of life when involved in crashes with drivers aged over 85 than with drivers under 65 (1.26 and 2.32 per 100 expected remaining life years, respectively). CONCLUSION: Our findings suggest that among road users involved in injury crashes, older drivers are less dangerous for the other road users. By attributing other road users' lost life years to each driver age, this study represents a new contribution to the debate about ageing and road safety.     

A fractal model for the static coefficient of friction at the fiber-matrix interface

The physical, geometrical, and mechanical properties at the fiber/matrix interface of a fiber-reinforced composite material have a dominant effect on the overall mechanical behavior of these materials. Specifically, the toughening of these materials is largely attributed to the energy dissipation due to the frictional sliding of fibers at their interface with the matrix material. The micromechanisms involved with interfacial failure and sliding are currently not entirely understood, and the failure threshold is generally predicted using macro-scale friction laws which neglect the micromechanical aspects. The objective of this study is to explore the derivation of a macro-scale static coefficient of friction at the interface of a previously debonded fiber based on the micro-scale properties of the contacting surfaces. Presented results illustrate that the macro-scale static coefficient of friction obtained from the proposed micro-scale model is independent of the normal load and is therefore consistent with the classical Amontons-Coulomb phenomenological laws of friction.     

A model with nonzero rise time for AE signals

Acoustic emission (AE) signals are conventionally modelled as damped or decaying sinusoidal functions. A major drawback of this model is its negligible or zero rise time. This paper proposes an alternative model, which provides for the rising part of the signal without sacrificing the analytical tractability and simplicity of the conventional model. Signals obtained from the proposed model through computer programs are illustrated for demonstrating their parity with actual AE signals. Analytic expressions for the time-domain parameters, viz., peak amplitude and rise time used in conventional AE signal analysis, are also derived. The model is believed to be also of use in modelling the output signal of any transducer that has finite rise time and fall time.