Crash reconstruction and crash modification factors

This paper addresses the following question: Under what conditions can reconstructed road crashes be used to estimate the effect of a safety-related countermeasure? Results developed by Pearl and his associates are used to draw two main conclusions. First, when one can (1) identify a structural equation describing a type of crash, (2) identify an additional structural equation describing the countermeasure's impact, and (3) estimate the initiating conditions for a set of reconstructed crashes, then a lower bound for a crash modification factor can be estimated by simulating whether or not each of the reconstructed crashes would still have occurred had the countermeasure been present. If the countermeasure's effect is monotonic this bound becomes tight. Second, in situations where it is not possible to reliably identify the structural equations needed for simulation, but where one can (1) identify a set of crash inputs which, when given, make the crash outcome conditionally independent of the countermeasure, and (2) predict how the distribution of these inputs will change in response to the countermeasure, then nonparametric estimation of the countermeasure's crash modification factor is possible. When it is not possible to predict the countermeasure's effect on the conditioning variables it may still be possible to identify constraints or specifications which the countermeasure should satisfy in order to realize a target crash modification.     

Exploration and comparison of crash modification factors for multiple treatments on rural multilane roadways

As multiple treatments (or countermeasures) are simultaneously applied to roadways, there is a need to assess their combined safety effects. Due to a lack of empirical crash modification factors (CMFs) for multiple treatments, the Highway Safety Manual (HSM) and other related studies developed various methods of combining multiple CMFs for single treatments. However, the literature did not evaluate the accuracy of these methods using CMFs obtained from the same study area. Thus, the main objectives of this research are: (1) develop CMFs for two single treatments (shoulder rumble strips, widening shoulder width) and one combined treatment (shoulder rumble strips + widening shoulder width) using before–after and cross-sectional methods and (2) evaluate the accuracy of the combined CMFs for multiple treatments estimated by the existing methods based on actual evaluated combined CMFs. Data was collected for rural multi-lane highways in Florida and four safety performance functions (SPFs) were estimated using 360 reference sites for two crash types (All crashes and Single Vehicle Run-off Roadway (SVROR) crashes) and two severity levels (all severity (KABCO) and injury (KABC)).     

Finite mixture modeling approach for developing crash modification factors in highway safety analysis

This study aimed to investigate the relative performance of two models (negative binomial (NB) model and two-component finite mixture of negative binomial models (FMNB-2)) in terms of developing crash modification factors (CMFs). Crash data on rural multilane divided highways in California and Texas were modeled with the two models, and crash modification functions (CMFunctions) were derived. The resultant CMFunction estimated from the FMNB-2 model showed several good properties over that from the NB model. First, the safety effect of a covariate was better reflected by the CMFunction developed using the FMNB-2 model, since the model takes into account the differential responsiveness of crash frequency to the covariate. Second, the CMFunction derived from the FMNB-2 model is able to capture nonlinear relationships between covariate and safety. Finally, following the same concept as those for NB models, the combined CMFs of multiple treatments were estimated using the FMNB-2 model. The results indicated that they are not the simple multiplicative of single ones (i.e., their safety effects are not independent under FMNB-2 models). Adjustment Factors (AFs) were then developed. It is revealed that current Highway Safety Manual’s method could over- or under-estimate the combined CMFs under particular combination of covariates. Safety analysts are encouraged to consider using the FMNB-2 models for developing CMFs and AFs.     

Transferability of calibrated microsimulation model parameters for safety assessment using simulated conflicts

Several studies have investigated the relationship between field-measured conflicts and the conflicts obtained from micro-simulation models using the Surrogate Safety Assessment Model (SSAM). Results from recent studies have shown that while reasonable correlation between simulated and real traffic conflicts can be obtained especially after proper calibration, more work is still needed to confirm that simulated conflicts provide safety measures beyond what can be expected from exposure. As well, the results have emphasized that using micro-simulation model to evaluate safety without proper model calibration should be avoided. The calibration process adjusts relevant simulation parameters to maximize the correlation between field-measured and simulated conflicts.The main objective of this study is to investigate the transferability of calibrated parameters of the traffic simulation model (VISSIM) for safety analysis between different sites. The main purpose is to examine whether the calibrated parameters, when applied to other sites, give reasonable results in terms of the correlation between the field-measured and the simulated conflicts. Eighty-three hours of video data from two signalized intersections in Surrey, BC were used in this study. Automated video-based computer vision techniques were used to extract vehicle trajectories and identify field-measured rear-end conflicts. Calibrated VISSIM parameters obtained from the first intersection which maximized the correlation between simulated and field-observed conflicts were used to estimate traffic conflicts at the second intersection and to compare the results to parameters optimized specifically for the second intersection. The results show that the VISSIM parameters are generally transferable between the two locations as the transferred parameters provided better correlation between simulated and field-measured conflicts than using the default VISSIM parameters. Of the six VISSIM parameters identified as important for the safety analysis, two parameters were directly transferable, three parameters were transferable to some degree, and one parameter was not transferable.     

Methodology to develop crash modification functions for road safety treatments with fully specified and hierarchical models

Crash modification factors (CMFs) for road safety treatments are developed as multiplicative factors that are used to reflect the expected changes in safety performance associated with changes in highway design and/or the traffic control features. However, current CMFs have methodological drawbacks. For example, variability with application circumstance is not well understood, and, as important, correlation is not addressed when several CMFs are applied multiplicatively. These issues can be addressed by developing safety performance functions (SPFs) with components of crash modification functions (CM-Functions), an approach that includes all CMF related variables, along with others, while capturing quantitative and other effects of factors and accounting for cross-factor correlations. CM-Functions can capture the safety impact of factors through a continuous and quantitative approach, avoiding the problematic categorical analysis that is often used to capture CMF variability. There are two formulations to develop such SPFs with CM-Function components – fully specified models and hierarchical models. Based on sample datasets from two Canadian cities, both approaches are investigated in this paper. While both model formulations yielded promising results and reasonable CM-Functions, the hierarchical model was found to be more suitable in retaining homogeneity of first-level SPFs, while addressing CM-Functions in sub-level modeling. In addition, hierarchical models better capture the correlations between different impact factors.     

Development of crash modification factors for changing lane width on roadway segments using generalized nonlinear models

This study evaluates the effectiveness of changing lane width in reducing crashes on roadway segments. To consider nonlinear relationships between crash rate and lane width, the study develops generalized nonlinear models (GNMs) using 3-years crash records and road geometry data collected for all roadway segments in Florida. The study also estimates various crash modification factors (CMFs) for different ranges of lane width based on the results of the GNMs. It was found that the crash rate was highest for 12-ft lane and lower for the lane width less than or greater than 12 ft. GNMs can extrapolate this nonlinear continuous effect of lane width and estimate the CMFs for any lane width, not only selected lane widths, unlike generalized linear models (GLMs) with categorical variables. The CMFs estimated using GNMs reflect that crashes are less likely to occur for narrower lanes if the lane width is less than 12 ft whereas crashes are less likely to occur for wider lanes if the lane width is greater than 12 ft. However, these effects varied with the posted speed limits as the effect of interaction between lane width and speed limit was significant. The estimated CMFs show that crashes are less likely to occur for lane widths less than 12 ft than the lane widths greater than 12 ft if the speed limit is higher than or equal to 40 mph. It was also found from the CMFs that crashes at higher severity levels (KABC and KAB) are less likely to occur for lane widths greater or less than 12 ft compared to 12-ft lane. The study demonstrates that the CMFs estimated using GNMs clearly reflect variations in crashes with lane width, which cannot be captured by the CMFs estimated using GLMs. Thus, it is recommended that if the relationship between crash rate and lane width is nonlinear, the CMFs are estimated using GNMs.     

Generalized nonlinear models for rear-end crash risk analysis

A generalized nonlinear model (GNM)-based approach for modeling highway rear-end crash risk is formulated using Washington State traffic safety data. Previous studies majorly focused on causal factor identification and crash risk modeling using Generalized linear Models (GLMs), such as Poisson regression, Logistic regression, etc. However, their basic assumption of a generalized linear relationship between the dependent variable (for example, crash rate) and independent variables (for example, contribute factors to crashes) established via a link function can be often violated in reality. Consequently, the GLM-based modeling results could provide biased findings and conclusions. In this research, a GNM-based approach is developed to utilize a nonlinear regression function to better elaborate non-monotonic relationships between the independent and dependent variables using the rear end accident data collected from 10 highway routes from 2002 through 2006. The results show for example that truck percentage and grade have a parabolic impact: they increase crash risks initially, but decrease them after the certain thresholds. Such non-monotonic relationships cannot be captured by regular GLMs which further demonstrate the flexibility of GNM-based approaches in the nonlinear relationship among data and providing more reasonable explanations. The superior GNM-based model interpretations help better understand the parabolic impacts of some specific contributing factors for selecting and evaluating rear-end crash safety improvement plans.     

Using multivariate adaptive regression splines (MARS) to develop crash modification factors for urban freeway interchange influence areas

Crash modification factors (CMFs) are used to measure the safety impacts of changes in specific geometric characteristics. Their development has gained much interest following the adoption of CMFs by the recently released Highway Safety Manual (HSM) and SafetyAnalyst tool in the United States. This paper describes a study to develop CMFs for interchange influence areas on urban freeways in the state of Florida. Despite the very different traffic and geometric conditions that exist in interchange influence areas, most previous studies have not separated them from the rest of the freeway system in their analyses. In this study, a promising data mining method known as multivariate adaptive regression splines (MARS) was applied to develop CMFs for median width and inside and outside shoulder widths for “total” and “fatal and injury” (FI) crashes. In addition, CMFs were also developed for the two most frequent crash types, i.e., rear-end and sideswipe. MARS is characterized by its ability to accommodate the nonlinearity in crash predictors and to allow the impact of more than one geometric variable to be simultaneously considered. The methodology further implements crash predictions from the model to identify changes in geometric design features. Four years of crashes from 2007 to 2010 were used in the analysis and the results showed that MARS's prediction capability and goodness-of-fit statistics outperformed those of the negative binomial model. The influential variables identified included the outside and inside shoulder widths, median width, lane width, traffic volume, and shoulder type. It was deduced that a 2-ft increase in the outside and inside shoulders (from 10 ft to 12 ft) reduces FI crashes by 10% and 33%, respectively. Further, a 42-ft reduction in the median width (from 64 ft to 22 ft) increases the rear-end, total, and FI crashes by 473%, 263%, and 223%, respectively.     

Accounting for heterogeneity among treatment sites and time trends in developing crash modification functions

Collision modification factors (CMFs) are commonly used to quantify the impact of safety countermeasures. The CMFs obtained from observational before–after (BA) studies are usually estimated by averaging the safety impact (i.e., index of effectiveness) for a group of treatment sites. The heterogeneity among the treatment locations, in terms of their characteristics, and the effect of this heterogeneity on safety treatment effectiveness are usually ignored. This is in contrast to treatment evaluations in other fields like medical statistics where variations in the magnitude (or in the direction) of response to the same treatment given to different patients are considered.     

Assessment of safety effects for widening urban roadways in developing crash modification functions using nonlinearizing link functions

Since a crash modification factor (CMF) represents the overall safety performance of specific treatments in a single fixed value, there is a need to explore the variation of CMFs with different roadway characteristics among treated sites over time. Therefore, in this study, we (1) evaluate the safety performance of a sample of urban four-lane roadway segments that have been widened with one through lane in each direction and (2) determine the relationship between the safety effects and different roadway characteristics over time. Observational before–after analysis with the empirical Bayes (EB) method was assessed in this study to evaluate the safety effects of widening urban four-lane roadways to six-lanes. Moreover, the nonlinearizing link functions were utilized to achieve better performance of crash modification functions (CMFunctions). The CMFunctions were developed using a Bayesian regression method including the estimated nonlinearizing link function to incorporate the changes in safety effects of the treatment over time. Data was collected for urban arterials in Florida, and the Florida-specific full SPFs were developed and used for EB estimation.     

Developing crash modification functions to assess safety effects of adding bike lanes for urban arterials with different roadway and socio-economic characteristics

Although many researchers have estimated crash modification factors (CMFs) for specific treatments (or countermeasures), there is a lack of studies that explored the heterogeneous effects of roadway characteristics on crash frequency among treated sites. Generally, the CMF estimated by before–after studies represents overall safety effects of the treatment in a fixed value. However, as each treated site has different roadway characteristics, there is a need to assess the variation of CMFs among the treated sites with different roadway characteristics through crash modification functions (CMFunctions). The main objective of this research is to determine relationships between the safety effects of adding a bike lane and the roadway characteristics through (1) evaluation of CMFs for adding a bike lane using observational before–after with empirical Bayes (EB) and cross-sectional methods, and (2) development of simple and full CMFunctions which are describe the CMF in a function of roadway characteristics of the sites. Data was collected for urban arterials in Florida, and the Florida-specific full SPFs were developed. Moreover, socio-economic parameters were collected and included in CMFunctions and SPFs (1) to capture the effects of the variables that represent volume of bicyclists and (2) to identify general relationship between the CMFs and these characteristics. In order to achieve better performance of CMFunctions, data mining techniques were used.     

Traffic analysis zone level crash estimation models based on land use characteristics

The objective of this paper is to develop crash estimation models at traffic analysis zone (TAZ) level as a function of land use characteristics. Crash data and land use data for the City of Charlotte, Mecklenburg County, North Carolina were used to illustrate the development of TAZ level crash estimation models. Negative binomial count models (with log-link) were developed as data was observed to be over-dispersed. Demographic/socio-economic characteristics such as population, the number of household units and employment, traffic indicators such as trip productions and attractions, and, on-network characteristics such as center-lane miles by speed limit were observed to be correlated to land use characteristics, and, hence were not considered in the development of TAZ level crash estimation models. Urban residential commercial, rural district and mixed use district land use variables were observed to be correlated to other land use variables and were also not considered in the development of the models. Results obtained indicate that land use characteristics such as mixed use development, urban residential, single-family residential, multi-family residential, business and, office district are strongly associated and play a statistically significant role in estimating TAZ level crashes. The coefficient for single-family residential area was observed to be negative, indicating a decrease in the number of crashes with an increase in single-family residential area. Models were also developed to estimate these crashes by severity (injury and property damage only crashes). The outcomes can be used in safety conscious planning, land use decisions, long range transportation plans, and, to proactively apply safety treatments in high risk TAZs.     

A combined M5P tree and hazard-based duration model for predicting urban freeway traffic accident durations

The duration of freeway traffic accidents duration is an important factor, which affects traffic congestion, environmental pollution, and secondary accidents. Among previous studies, the M5P algorithm has been shown to be an effective tool for predicting incident duration. M5P builds a tree-based model, like the traditional classification and regression tree (CART) method, but with multiple linear regression models as its leaves. The problem with M5P for accident duration prediction, however, is that whereas linear regression assumes that the conditional distribution of accident durations is normally distributed, the distribution for a “time-to-an-event” is almost certainly nonsymmetrical. A hazard-based duration model (HBDM) is a better choice for this kind of a “time-to-event” modeling scenario, and given this, HBDMs have been previously applied to analyze and predict traffic accidents duration. Previous research, however, has not yet applied HBDMs for accident duration prediction, in association with clustering or classification of the dataset to minimize data heterogeneity. The current paper proposes a novel approach for accident duration prediction, which improves on the original M5P tree algorithm through the construction of a M5P-HBDM model, in which the leaves of the M5P tree model are HBDMs instead of linear regression models. Such a model offers the advantage of minimizing data heterogeneity through dataset classification, and avoids the need for the incorrect assumption of normality for traffic accident durations. The proposed model was then tested on two freeway accident datasets. For each dataset, the first 500 records were used to train the following three models: (1) an M5P tree; (2) a HBDM; and (3) the proposed M5P-HBDM, and the remainder of data were used for testing. The results show that the proposed M5P-HBDM managed to identify more significant and meaningful variables than either M5P or HBDMs. Moreover, the M5P-HBDM had the lowest overall mean absolute percentage error (MAPE).     

A multivariate Poisson-lognormal regression model for prediction of crash counts by severity, using Bayesian methods

Numerous efforts have been devoted to investigating crash occurrence as related to roadway design features, environmental factors and traffic conditions. However, most of the research has relied on univariate count models; that is, traffic crash counts at different levels of severity are estimated separately, which may neglect shared information in unobserved error terms, reduce efficiency in parameter estimates, and lead to potential biases in sample databases. This paper offers a multivariate Poisson-lognormal (MVPLN) specification that simultaneously models crash counts by injury severity. The MVPLN specification allows for a more general correlation structure as well as overdispersion. This approach addresses several questions that are difficult to answer when estimating crash counts separately. Thanks to recent advances in crash modeling and Bayesian statistics, parameter estimation is done within the Bayesian paradigm, using a Gibbs Sampler and the Metropolis–Hastings (M–H) algorithms for crashes on Washington State rural two-lane highways. Estimation results from the MVPLN approach show statistically significant correlations between crash counts at different levels of injury severity. The non-zero diagonal elements suggest overdispersion in crash counts at all levels of severity. The results lend themselves to several recommendations for highway safety treatments and design policies. For example, wide lanes and shoulders are key for reducing crash frequencies, as are longer vertical curves.     

An overview of research in the fields of laser surface modification and laser machining at the Integrated Manufacturing Technologies Institute, NRC

At the Integrated Manufacturing Technologies Institute (IMTI), National Research Council Canada, research in laser material processing has been underway for more than a decade. Topics being investigated include the development of laser welding, cutting, and drilling applications, surface modification techniques, laser depth controlled machining, stereolithography (SLA), and selective laser sintering (SLS). This paper reviews the activities in laser surface modification and laser depth controlled machining of ceramics.     

Elastic notch stress intensity factors for sharply V-notched rounded bars under torsion

The paper deals with the determination of analytical expressions for the mode III notch stress intensity factors for circumferentially-sharply-notched rounded bars under torsion loading, starting from the theoretical stress concentration factors of the corresponding notch problem.An exact, closed-form solution for the NSIFs is obtained for deep notches; subsequently the solution is extended also to finite notched components taking advantage of a shape function determined by a numerical best fitting procedure.     

Integrated decision support framework for distribution network design

In the current business environment of competition shifting from company-to-company to supply chain against supply chain, there is an increasing need for logistics providers (LSP) to gain cost effectiveness with no compromise on service levels. One key initiative that LSP can undertake is to allocate and utilise their storage and transportation assets optimally. The current work is an attempt in that direction and provides a hands-on decision support framework that integrates MCDM, network optimisation, and discrete event simulation to address distribution network design and transport optimisation. The use case of PT Pos Indonesia in the metropolitan area of Greater Surabaya highlights the benefits of combining ICT tools with well-established best practices in supply chain management. Findings of this work highlight that the number of distribution facilities for the case at hand should be reduced from nine to four. Compared to the existing, the identified network configuration unlocks potential cost saving in transportation and warehousing of 18%–22%, reduces CO₂ emissions by nearly 30%, with no deterioration in service level. Managerial implications about transportation policies are highlighted in the conclusive part of this paper.     

Engineering the tube size for an inner surface modification by plasma-based ion implantation

In order to apply the inner surface modification of the tube component by plasma-based ion implantation (PBII) technique, the tube size has been characterized by introducing a characteristic parameter - the critical radius of tube (CRT) to optimize the process parameters of a grid-enhanced PBII technique for the nitrogen ion implantation onto the inner surface of an Fe-Cr-Ni stainless steel tube under the process conditions, including the plasma density of central plasma source, the steady pulse voltage, the grid electrode radius, and the processing pressure. The temporal sheath dynamics of the ion matrix sheath on the inner surface of the tube component modified by PBII were demonstrated by the collisional fluid model using the equations of ion continuity and ion motion, Poisson’s equation, and Boltzmann’s relationship of electron to determine the effective range of the process parameters. The optimum process parameters were found by the effect factors of the CRT which was bounded by the two important process parameters, i.e. the ion implantation dose and the processing time, for the engineering practice due to the available dependence on the surface modification effect in suitable costs.     

Structural relationship model for design defect and influencing factors in the concurrent design process

Concurrent design is a systematic approach that aims to help developers consider all elements related to the product life cycle, including quality, cost, schedule and user requirement. This approach is challenging because designers conduct these elements manually in the design process. In addition, studies on the formation mechanism of design defects are limited. To minimise the difficulties of product designers and prevent design defects, a structural relationship model is presented for the concurrent design process. The structural relationship model, which incorporates design defects, design process and influencing factors, is marked through polychromatic sets. The interval analytic hierarchy process is used to determine the weight of each influencing factor. Furthermore, a brief model instruction for concurrent design is proposed to guarantee the model application’s validity. The proposed method enables designers to concentrate on functionality and performance design, and the findings are expected to be theoretically and practically useful for preventing and controlling defects in concurrent design.     

Recursive modeling of loss of control in human and organizational processes: A systemic model for accident analysis

A recursive model of accident investigation is proposed by exploiting earlier work in systems thinking. Safety analysts can understand better the underlying causes of decision or action flaws by probing into the patterns of breakdown in the organization of safety. For this deeper analysis, a cybernetic model of organizational factors and a control model of human processes have been integrated in this article (i.e., the viable system model and the extended control model). The joint VSM–ECOM framework has been applied to a case study to help safety practitioners with the analysis of patterns of breakdown with regard to how operators and organizations manage goal conflicts, monitor work progress, recognize weak signals, align goals across teams, and adapt plans on the fly. The recursive accident representation brings together several organizational issues (e.g., the dilemma of autonomy versus compliance, or the interaction between structure and strategy) and addresses how operators adapt to challenges in their environment by adjusting their modes of functioning and recovery. Finally, it facilitates the transfer of knowledge from diverse incidents and near misses within similar domains of practice.