Calibration of Highway Safety Manual Work Zone Crash Modification Factors, TPF-5(081), 2014

The Highway Safety Manual is the national safety manual that provides quantitative methods for analyzing highway safety. The HSM presents crash modification factors related to work zone characteristics such as work zone duration and length. These crash modification factors were based on high-impact work zones in California. Therefore there was a need to use work zone and safety data from the Midwest to calibrate these crash modification factors for use in the Midwest. Almost 11,000 Missouri freeway work zones were analyzed to derive a representative and stratified sample of 162 work zones. The 162 work zones was more than four times the number of work zones used in the HSM. This dataset was used for modeling and testing crash modification factors applicable to the Midwest. The dataset contained work zones ranging from 0.76 mile to 9.24 miles and with durations from 16 days to 590 days. A combined fatal/injury/non-injury model produced a R2 fit of 0.9079 and a prediction slope of 0.963. The resulting crash modification factors of 1.01 for duration and 0.58 for length were smaller than the values in the HSM. Two practical application examples illustrate the use of the crash modification factors for comparing alternate work zone setups.

Preliminary human factors guidelines for crash avoidance warning devices.

Federal Highway Administration, Office of Crash Avoidance Research, Washington, D.C.

Preliminary analysis of the National Crash Severity Study: factors in fatal accidents.

Mode of access: Internet.

Development of safety performance functions for empirical Bayes estimation of crash reduction factors

Crash reduction factors (CRFs) are used to estimate the potential number of traffic crashes expected to be prevented from investment in safety improvement projects. The method used to develop CRFs in Florida has been based on the commonly used before-and-after approach. This approach suffers from a widely recognized problem known as regression-to-the-mean (RTM). The Empirical Bayes (EB) method has been introduced as a means to addressing the RTM problem. This method requires the information from both the treatment and reference sites in order to predict the expected number of crashes had the safety improvement projects at the treatment sites not been implemented. The information from the reference sites is estimated from a safety performance function (SPF), which is a mathematical relationship that links crashes to traffic exposure. The objective of this dissertation was to develop the SPFs for different functional classes of the Florida State Highway System. Crash data from years 2001 through 2003 along with traffic and geometric data were used in the SPF model development. SPFs for both rural and urban roadway categories were developed. The modeling data used were based on one-mile segments that contain homogeneous traffic and geometric conditions within each segment. Segments involving intersections were excluded. The scatter plots of data show that the relationships between crashes and traffic exposure are nonlinear, that crashes increase with traffic exposure in an increasing rate. Four regression models, namely, Poisson (PRM), Negative Binomial (NBRM), zero-inflated Poisson (ZIP), and zero-inflated Negative Binomial (ZINB), were fitted to the one-mile segment records for individual roadway categories. The best model was selected for each category based on a combination of the Likelihood Ratio test, the Vuong statistical test, and the Akaike's Information Criterion (AIC). The NBRM model was found to be appropriate for only one category and the ZINB model was found to be more appropriate for six other categories. The overall results show that the Negative Binomial distribution model generally provides a better fit for the data than the Poisson distribution model. In addition, the ZINB model was found to give the best fit when the count data exhibit excess zeros and over-dispersion for most of the roadway categories. While model validation shows that most data points fall within the 95% prediction intervals of the models developed, the Pearson goodness-of-fit measure does not show statistical significance. This is expected as traffic volume is only one of the many factors contributing to the overall crash experience, and that the SPFs are to be applied in conjunction with Accident Modification Factors (AMFs) to further account for the safety impacts of major geometric features before arriving at the final crash prediction. However, with improved traffic and crash data quality, the crash prediction power of SPF models may be further improved.

Safety Edge Crash Modification Factors, Tech Brief, 2016

This study evaluated the safety impact of the Safety Edge for construction projects in 2010 and 2011 in Iowa to assess the effectiveness of the treatment in reducing crashes.

Statewide Heavy-Truck Crash Assessment, 2014

In 2010, 16.5 percent of all fatal vehicle crashes in Iowa involved large trucks compared to the national average of 7.8 percent. Only about 16 percent of these fatalities involved the occupants of the heavy vehicles, meaning that a majority of the fatalities in fatal crashes involve non-heavy-truck occupants. These statistics demonstrate the severe nature of heavy-truck crashes and underscore the serious impact that these crashes can have on the traveling public. These statistics also indicate Iowa may have a disproportionately higher safety risk compared to the nation with respect to heavy-truck safety. Several national studies, and a few statewide studies, have investigated large-truck crashes; however, no rigorous analysis of heavy-truck crashes has been conducted for Iowa. The objective of this study was to investigate and identify the causes, locations, and other factors related to heavy-truck crashes in Iowa with the goal of reducing crashes and promoting safety. To achieve this objective, this study used the most current statewide data of heavy-truck crashes in Iowa. This study also attempted to assess crash experience with respect to length of commercial driver’s license (CDL) licensure using the most recent five years of CDL data linked to the before mentioned crash data. In addition, this study used inspection and citation data from the Iowa Department of Transportation (DOT) Motor Vehicle Division and Iowa State Patrol to investigate the relationship between enforcement activities and crash experience.

Spatial Scale of Clustering of Motor Vehicle Crash Types and Appropriate Countermeasures, 2009

This project analyzes the characteristics and spatial distributions of motor vehicle crash types in order to evaluate the degree and scale of their spatial clustering. Crashes occur as the result of a variety of vehicle, roadway, and human factors and thus vary in their clustering behavior. Clustering can occur at a variety of scales, from the intersection level, to the corridor level, to the area level. Conversely, other crash types are less linked to geographic factors and are more spatially “random.” The degree and scale of clustering have implications for the use of strategies to promote transportation safety. In this project, Iowa's crash database, geographic information systems, and recent advances in spatial statistics methodologies and software tools were used to analyze the degree and spatial scale of clustering for several crash types within the counties of the Iowa Northland Regional Council of Governments. A statistical measure called the K function was used to analyze the clustering behavior of crashes. Several methodological issues, related to the application of this spatial statistical technique in the context of motor vehicle crashes on a road network, were identified and addressed. These methods facilitated the identification of crash clusters at appropriate scales of analysis for each crash type. This clustering information is useful for improving transportation safety through focused countermeasures directly linked to crash causes and the spatial extent of identified problem locations, as well as through the identification of less location-based crash types better suited to non-spatial countermeasures. The results of the K function analysis point to the usefulness of the procedure in identifying the degree and scale at which crashes cluster, or do not cluster, relative to each other. Moreover, for many individual crash types, different patterns and processes and potentially different countermeasures appeared at different scales of analysis. This finding highlights the importance of scale considerations in problem identification and countermeasure formulation.

How the full opening of the capital account to highly liquid financial markets led Latin America to two and a half cycles of ‘mania, panic and crash’

Latin America has recently experienced three cycles of capital inflows, the first two ending in major financial crises. The first took place between 1973 and the 1982 ‘debt-crisis’. The second took place between the 1989 ‘Brady bonds’ agreement (and the beginning of the economic reforms and financial liberalisation that followed) and the Argentinian 2001/2002 crisis, and ended up with four major crises (as well as the 1997 one in East Asia) — Mexico (1994), Brazil (1999), and two in Argentina (1995 and 2001/2). Finally, the third inflow-cycle began in 2003 as soon as international financial markets felt reassured by the surprisingly neo-liberal orientation of President Lula’s government; this cycle intensified in 2004 with the beginning of a (purely speculative) commodity price-boom, and actually strengthened after a brief interlude following the 2008 global financial crash — and at the time of writing (mid-2011) this cycle is still unfolding, although already showing considerable signs of distress. The main aim of this paper is to analyse the financial crises resulting from this second cycle (both in LA and in East Asia) from the perspective of Keynesian/ Minskyian/ Kindlebergian financial economics. I will attempt to show that no matter how diversely these newly financially liberalised Developing Countries tried to deal with the absorption problem created by the subsequent surges of inflow (and they did follow different routes), they invariably ended up in a major crisis. As a result (and despite the insistence of mainstream analysis), these financial crises took place mostly due to factors that were intrinsic (or inherent) to the workings of over-liquid and under-regulated financial markets — and as such, they were both fully deserved and fairly predictable. Furthermore, these crises point not just to major market failures, but to a systemic market failure: evidence suggests that these crises were the spontaneous outcome of actions by utility-maximising agents, freely operating in friendly (‘light-touch’) regulated, over-liquid financial markets. That is, these crises are clear examples that financial markets can be driven by buyers who take little notice of underlying values — i.e., by investors who have incentives to interpret information in a biased fashion in a systematic way. Thus, ‘fat tails’ also occurred because under these circumstances there is a high likelihood of self-made disastrous events. In other words, markets are not always right — indeed, in the case of financial markets they can be seriously wrong as a whole. Also, as the recent collapse of ‘MF Global’ indicates, the capacity of ‘utility-maximising’ agents operating in (excessively) ‘friendly-regulated’ and over-liquid financial market to learn from previous mistakes seems rather limited.

How the full opening of the capital account to highly liquid financial markets led Latin America to two and a half cycles of ‘mania, panic and crash’

Latin America has recently experienced three cycles of capital inflows, the first two ending in major financial crises. The first took place between 1973 and the 1982 ‘debt-crisis’. The second took place between the 1989 ‘Brady bonds’ agreement (and the beginning of the economic reforms and financial liberalisation that followed) and the Argentinian 2001/2002 crisis, and ended up with four major crises (as well as the 1997 one in East Asia) — Mexico (1994), Brazil (1999), and two in Argentina (1995 and 2001/2). Finally, the third inflow-cycle began in 2003 as soon as international financial markets felt reassured by the surprisingly neo-liberal orientation of President Lula’s government; this cycle intensified in 2004 with the beginning of a (purely speculative) commodity price-boom, and actually strengthened after a brief interlude following the 2008 global financial crash — and at the time of writing (mid-2011) this cycle is still unfolding, although already showing considerable signs of distress. The main aim of this paper is to analyse the financial crises resulting from this second cycle (both in LA and in East Asia) from the perspective of Keynesian/ Minskyian/ Kindlebergian financial economics. I will attempt to show that no matter how diversely these newly financially liberalised Developing Countries tried to deal with the absorption problem created by the subsequent surges of inflow (and they did follow different routes), they invariably ended up in a major crisis. As a result (and despite the insistence of mainstream analysis), these financial crises took place mostly due to factors that were intrinsic (or inherent) to the workings of over-liquid and under-regulated financial markets — and as such, they were both fully deserved and fairly predictable. Furthermore, these crises point not just to major market failures, but to a systemic market failure: evidence suggests that these crises were the spontaneous outcome of actions by utility-maximising agents, freely operating in friendly (light-touched) regulated, over-liquid financial markets. That is, these crises are clear examples that financial markets can be driven by buyers who take little notice of underlying values — investors have incentives to interpret information in a biased fashion in a systematic way. ‘Fat tails’ also occurred because under these circumstances there is a high likelihood of self-made disastrous events. In other words, markets are not always right — indeed, in the case of financial markets they can be seriously wrong as a whole. Also, as the recent collapse of ‘MF Global’ indicates, the capacity of ‘utility-maximising’ agents operating in unregulated and over-liquid financial market to learn from previous mistakes seems rather limited.

Deer-Vehicle Crash Patterns Across Ecoregions in Michigan

Deer-vehicle collisions (DVCs) impact the economic and social well being of humans. We examined large-scale patterns behind DVCs across 3 ecoregions: Southern Lower Peninsula (SLP), Northern Lower Peninsula (NLP), and Upper Peninsula (UP) in Michigan. A 3 component conceptual model of DVCs with drivers, deer, and a landscape was the framework of analysis. The conceptual model was parameterized into a parsimonious mathematical model. The dependent variable was DVCs by county by ecoregion and the independent variables were percent forest cover, percent crop cover, mean annual vehicle miles traveled (VMT), and mean deer density index (DDI) by county. A discriminant function analysis of the 4 independent variables by counties by ecoregion indicated low misclassification, and provided support to the groupings by ecoregions. The global model and all sub-models were run for the 3 ecoregions and evaluated using information-theoretic approaches. Adjusted R2 values for the global model increased substantially from the SLP (0.21) to the NLP (0.54) to the UP (0.72). VMT and DDI were important variables across all 3 ecoregions. Percent crop cover played an important role in DVCs in the SLP and UP. The scale at which causal factors of DVCs operate appear to be finer in southern Michigan than in northern Michigan. Reduction of DVCs will likely occur only through a reduction in deer density, a reduction in traffic volume, or in modification of sitespecific factors, such as driver behavior, sight distance, highway features, or speed limits.

Non-illness-related factors contributing to traffic safety in older drivers: a litterature review

Background/Study Context: Older drivers are at increased risk of becoming involved in car crashes. Contrary to well-studied illness-related factors contributing to crash risk, the non-illness-related factors that can influence safety of older drivers are underresearched. METHODS: Here, the authors review the literature on non-illness-related factors influencing driving in people over age 60. We identified six safety-relevant factors: road infrastructure, vehicle characteristics, traffic-related knowledge, accuracy of self-awareness, personality traits, and self-restricted driving. RESULTS: The literature suggests that vehicle preference, the quality of traffic-related knowledge, the location and time of traffic exposure, and personality traits should all be taken into account when assessing fitness-to-drive in older drivers. Studies indicate that self-rating of driving skills does not reliably predict fitness-to-drive. CONCLUSIONS: Most factors discussed are adaptable or accessible to training and collectively may have the potential to increase traffic safety for older drivers and other road users.

The Russian economy under Putin: Growth factors and impediments to economic development. OSW Study 20/2005

After a dramatic economic decline after the collapse of the Soviet Union and the financial breakdown of 1998, the Russian economy has begun to emerge from its deep crisis. The years 1999-2004 were a period of dynamic development in all sectors of Russian economy, and saw a rapid growth in GDP of over 7 per cent per year. Russia owed the excellent macroeconomic results of that period to a combination of favourable factors. The key factors were: high hydrocarbon prices on the global markets; an increase in Russia's international competitiveness thanks to the "rouble devaluation effect" (following the 1998 financial crash); and the market reforms carried out within that period. In 2004, despite very high oil and gas prices on world markets, a slowdown of the GDP growth took place. Even though the economy is still developing fairly rapidly, we are able to say that Russia is exhausting those traditional mechanisms (apart from oil and gas prices) which have hitherto stimulated GDP growth. Moreover, there are no new mechanisms which could replace the old ones. In the longer term, these unsolved structural problems may seriously impede Russia's economic growth.

Feasibility of developing training programs designed to improve deficient driver factors. Volume I: a review of existing data bases and recommendations for future data collection. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Feasibility of developing training programs designed to improve deficient driver factors. Volume II: identifying accident avoidance behaviors: a guide for accident investigators. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

A study of daytime running light design factors. Final report.

National Highway Traffic Safety Administration, Crash Avoidance Research Division, Washington, D.C.