The Experts below are selected from a list of 9282 Experts worldwide ranked by ideXlab platform
Kara M Kockelman - One of the best experts on this subject based on the ideXlab platform.
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a multivariate poisson lognormal regression model for prediction of crash counts by severity using bayesian methods
Accident Analysis & Prevention, 2008Co-Authors: Kara M Kockelman, Paul DamienAbstract:Abstract 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.
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safety effects of speed limit changes use of panel models including speed use and Design variables
Transportation Research Record, 2005Co-Authors: Youngjun Kweon, Kara M KockelmanAbstract:This work estimates the total safety effects of speed limit changes on high-speed Roadways by using traffic detector data and Highway Safety Information System data from 1993 to 1996. To gauge the total effects, the study applies a sequential modeling approach: average speed and speed variance models are first estimated on the basis of Roadway Design, use, and speed limit information. Then, crash counts (of varying severity) are estimated on the basis of the speed estimates, Design, and use variables. The 4 years of data come from 63,937 homogeneous Roadway segments along seven Interstates and 143 state highways in Washington State. A random effects negative binomial model was selected among several alternative panel and nonpanel models for count data. Results indicate that the average road segment in the data set can be expected to exhibit lower nonfatal crash rates up to a 55 mph (88 km/h) speed limit. In contrast, fatality rates appear unresponsive to speed limit changes. Fatal and nonfatal rates fall for Design reasons, including wider shoulders and more gradual curves, which appear to be key Design variables. However, fatal and nonfatal rates move differently when traffic levels rise, with nonfatal rates remaining unchanged and fatal rates falling.
Rebecca L Sanders - One of the best experts on this subject based on the ideXlab platform.
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we can all get along the alignment of driver and bicyclist Roadway Design preferences in the san francisco bay area
Transportation Research Part A-policy and Practice, 2016Co-Authors: Rebecca L SandersAbstract:Two trends in the United States—growth in bicycling and enthusiasm for complete streets—suggest a need to understand how various Roadway users view Roadway Designs meant to accommodate multiple modes. While many studies have examined bicyclists’ Roadway Design preferences, there has been little investigation into the opinions of non-bicyclists who might bicycle in the future. Additionally, little research has explored the preferences of the motorists who share roads with cyclists—despite the fact that motorists compose the vast majority of Roadway users in the United States and similarly developed countries.
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pedestrian safety practitioners perspectives of driver yielding behavior across north america
Transportation Research Board 94th Annual Meeting, 2015Co-Authors: Robert J Schneider, Rebecca L SandersAbstract:This paper presents results from a recent Internet survey of practitioners in the pedestrian safety field about their perceptions of driver yielding behavior in cities throughout North America. As one of the first studies to attempt to understand driver and pedestrian interactions from a macro perspective, this research combined perceptions of local driver yielding rates in three crosswalk scenarios with open-ended comments to understand factors that may influence driver yielding behavior. Responses from 387 practitioners in 171 cities suggested that rates of driver yielding to pedestrians in marked crosswalks were related to characteristics such as social norms, Roadway Design, law enforcement, and pedestrian volumes. Respondents generally indicated that drivers were more likely to yield to pedestrians on Roadways with fewer lanes and slower travel speeds. However, the results also suggested notable geographic differences in yielding culture. Practitioners indicated that crosswalk laws were rarely enforc...
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examining the cycle how perceived and actual bicycling risk influence cycling frequency Roadway Design preferences and support for cycling among bay area residents
University of California Transportation Center, 2013Co-Authors: Rebecca L SandersAbstract:University of California Transportation Center UCTC Dissertation UCTC-DISS-2013-03 Examining the Cycle: How Perceived and Actual Bicycling Risk Influence Cycling Frequency, Roadway Design Preferences, and Support for Cycling Among Bay Area Residents Rebecca Lauren Sanders University of California, Berkeley
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examining the cycle how perceived and actual bicycling risk influence cycling frequency Roadway Design preferences and support for cycling among bay area residents
Research Papers in Economics, 2013Co-Authors: Rebecca L SandersAbstract:This dissertation investigates the connection between perceived and actual bicycling risk, andhow they both affect and are affected by one’s attitudes, knowledge, behavior, and experiences. Understanding bicycling risk has gained importance as efforts by the U.S. Department of Transportation, the Environmental Protection Agency, the Centers for Disease Control & Prevention, and others have urged communities to increase cycling for its health, environmental, and social equity benefits. Research has identified numerous barriers to increased bicycling in the U.S., including topography, weather, and trip distance, but the barrier that appears most consistently between studies is the perceived hazard associated with cycling near motorists. Yet, little research has fully explored the concept of risk to understand its component parts, including how 1) various driver actions affect perceived and actual cycling risk, 2) reported crash statistics reflect perceived and actual risk, 3) Roadway Design preferences are affected by perceived risk, and 4) attitudes toward cycling and cycling risk—especially among drivers—influence support for bicycling in one’s community. A deeper understanding of perceived and actual risk is critical for knowing how to address it, and, ultimately, to encourage more people to bicycle. To begin to answer these questions and demystify bicycling risk, this dissertation employs three main methods: focus groups, an online survey (n=463), and an analysis of reported crash data from the San Francisco Bay Area, one of the regions at the forefront of cycling efforts in the U.S. My findings confirm that perceived and actual cycling risk influence the decision to bicycle, but indicate that the causal pathways are more nuanced than previously understood. First, my data suggest that cyclists experience two types of Roadway risk: pervasive risk in the form of near misses that occur frequently, and acute risk that occurs when a cyclist is struck—a less frequent, but more injurious incident. Both types—but particularly near misses— significantly affect perceived risk for cyclists and their family and friends, yet we lack systematic data on near misses and are therefore almost completely ignorant about the extent and effect of their occurrence. Routinely-collected reported crash data provide only limited insight into the type and extent of risk cyclists experience. Second, Roadway Design preferences are significantly related to perceived risk, and particularly important for attracting new cyclists. Surprisingly, drivers and cyclists both prefer Roadway Designs with separated space for bicyclists, particularly if barrier-separated, regardless of cycling frequency. Shared space Designs are less popular among drivers and much less popular among cyclists, particularly for people who might consider cycling but do not currently do so: only a tiny fraction of potential cyclists feel comfortable sharing space with drivers on commercial streets. Third, perceived cycling risk extends beyond fear of danger for oneself, and is significantly related to support for cycling in one’s community. Structural equation models of perceived cycling risk, attitudes, and behavior revealed that respondents are affected by their perceived risk as cyclists, but also as drivers sharing the Roadway with cyclists they view as “scofflaws†, and the risks they project onto other cyclists—particularly those cycling with children. This multi-pronged belief in cycling risk significantly negatively affects bicycling support, including support for new bicycle facilities and public funding to encourage cycling. Based on these findings, I propose a revised theoretical framework for conceptualizing cycling risk and its influences. I conclude the dissertation with policy recommendations for addressing perceived risk.
Andrew W Howard - One of the best experts on this subject based on the ideXlab platform.
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Motor Vehicle-Pedestrian Collisions and Walking to School: The Role of the Built Environment
Pediatrics, 2014Co-Authors: Linda Rothman, Colin Macarthur, Ron Buliung, Teresa To, Andrew W HowardAbstract:OBJECTIVES: Initiatives to increase active school transportation are popular. However, increased walking to school could increase collision risk. The built environment is related to both pedestrian collision risk and walking to school. We examined the influence of the built environment on walking to school and child pedestrian collisions in Toronto, Canada. METHODS: Police-reported pedestrian collision data from 2002 to 2011 for children ages 4 to 12, proportion of children walking to school, and built environment data were mapped onto school attendance boundaries. Collision rates were calculated by using 2006 census populations and modeled by using negative binomial regression. RESULTS: There were 481 collisions with a mean collision rate of 7.4/10 000 children per year. The relationship between walking proportion and collision rate was not statistically significant after adjusting for population density and Roadway Design variables including multifamily dwelling density, traffic light, traffic calming and 1-way street density, school crossing guard presence, and school socioeconomic status. CONCLUSIONS: Pedestrian collisions are more strongly associated with built environment features than with proportions walking. Road Design features were related to higher collision rates and warrant further examination for their safety effects for children. Future policy Designed to increase children’s active transportation should be developed from evidence that more clearly addresses child pedestrian safety.
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influence of social and built environment features on children walking to school an observational study
Preventive Medicine, 2014Co-Authors: Linda Rothman, Colin Macarthur, Ron Buliung, Andrew W HowardAbstract:OBJECTIVES: To estimate the proportion of children living within walking distance who walk to school in Toronto, Canada and identify built and social environmental correlates of walking. METHODS: Observational counts of school travel mode were done in 2011, at 118 elementary schools. Built environment data were obtained from municipal sources and school field audits and mapped onto school attendance boundaries. The influence of social and built environmental features on walking counts was analyzed using negative binomial regression. RESULTS: The mean proportion observed walking was 67% (Standard Deviation=14.0). Child population (Incidence Rate Ratio (IRR) 1.36), pedestrian crossover (IRR 1.32), traffic light (IRR 1.19), and intersection densities (IRR 1.03), school crossing guard (IRR 1.14) and primary language other than English (IRR 1.20), were positively correlated with walking. Crossing guard presence was reduced the influence of other features on walking. CONCLUSIONS: This is the first large observational study examining school travel mode and the environment. Walking proportions were higher than previously reported in Toronto, with large variability. Associations between population density and several Roadway Design features and walking were confirmed. School crossing guards may override the influence of Roadway features on walking. Results have important implications for policies regarding walking promotion. KW: SR2S Language: en
Tarek Sayed - One of the best experts on this subject based on the ideXlab platform.
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collision prediction models using multivariate poisson lognormal regression
Accident Analysis & Prevention, 2009Co-Authors: Karim Elbasyouny, Tarek SayedAbstract:This paper advocates the use of multivariate Poisson-lognormal (MVPLN) regression to develop models for collision count data. The MVPLN approach presents an opportunity to incorporate the correlations across collision severity levels and their influence on safety analyses. The paper introduces a new multivariate hazardous location identification technique, which generalizes the univariate posterior probability of excess that has been commonly proposed and applied in the literature. In addition, the paper presents an alternative approach for quantifying the effect of the multivariate structure on the precision of expected collision frequency. The MVPLN approach is compared with the independent (separate) univariate Poisson-lognormal (PLN) models with respect to model inference, goodness-of-fit, identification of hot spots and precision of expected collision frequency. The MVPLN is modeled using the WinBUGS platform which facilitates computation of posterior distributions as well as providing a goodness-of-fit measure for model comparisons. The results indicate that the estimates of the extra Poisson variation parameters were considerably smaller under MVPLN leading to higher precision. The improvement in precision is due mainly to the fact that MVPLN accounts for the correlation between the latent variables representing property damage only (PDO) and injuries plus fatalities (I + F). This correlation was estimated at 0.758, which is highly significant, suggesting that higher PDO rates are associated with higher I + F rates, as the collision likelihood for both types is likely to rise due to similar deficiencies in Roadway Design and/or other unobserved factors. In terms of goodness-of-fit, the MVPLN model provided a superior fit than the independent univariate models. The multivariate hazardous location identification results demonstrated that some hazardous locations could be overlooked if the analysis was restricted to the univariate models.
Paul Damien - One of the best experts on this subject based on the ideXlab platform.
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a multivariate poisson lognormal regression model for prediction of crash counts by severity using bayesian methods
Accident Analysis & Prevention, 2008Co-Authors: Kara M Kockelman, Paul DamienAbstract:Abstract 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.
