Safety Performance of One-Way Arterials

2020 ◽  
Vol 2674 (10) ◽  
pp. 548-559
Author(s):  
Srinivas R. Geedipally ◽  
Dominique Lord ◽  
Michael P. Pratt ◽  
Kay Fitzpatrick ◽  
Eun Sug Park
Keyword(s):  
Predictive Models ◽  
Traffic Control ◽  
Injury Severity ◽  
Negative Binomial ◽  
Prediction Models ◽  
Distribution Functions ◽  
Safety Performance ◽  
Safety Performance Function ◽  
Traffic Characteristics ◽  
Wide Range

Safety analysts are generally interested in understanding the differences in the safety performance when a two-way street is converted to a one-way operation or vice-versa. Literature exists to understand and predict the safety of two-way streets. However, safety prediction procedures are currently not available for assessing the safety performance of one-way arterials. This research was undertaken to develop safety prediction models for one-way arterials. To accomplish this objective, data collected in California, Illinois, Michigan, Oregon, and Texas were assembled that included a wide range of geometric design features, traffic control features, traffic characteristics, and crash records. The data were used to calibrate predictive models, each of which included a safety performance function (SPF) and several crash modification factors (CMFs). Separate SPFs were developed for fatal and injury crashes (i.e., fatal, incapacitating injury, non-incapacitating injury, and possible injury crash) and property-damage-only crashes. The SPFs were estimated using the negative binomial modeling structure. Severity distribution functions (SDFs) were also calibrated using the fatal and injury data. These functions can be used with the predictive models to estimate the expected crash frequency for each of four injury severity levels.

Examining the Safety Performance and Injury Severity Characteristics of Rural County Roadways

2019 ◽  
Vol 2673 (10) ◽  
pp. 405-415 ◽  
Author(s):  
Srinivas R. Geedipally ◽  
Timothy J. Gates ◽  
Steven Stapleton ◽  
Anthony Ingle ◽  
Raul E. Avelar
Keyword(s):  
Injury Severity ◽  
Negative Binomial ◽  
Random Effect ◽  
Multinomial Logit Model ◽  
Distribution Functions ◽  
Safety Performance ◽  
Occurrence Rate ◽  
Rural County ◽  
Rural Highways ◽  
Traffic Volumes

Much of the earlier work on rural safety focused on state-maintained roadways and little is known about the safety performance of low-volume county-maintained roads. This study involved the estimation of safety performance for rural county roadways (paved and gravel). This was accomplished through the development of safety performance functions (SPFs) to estimate the number of annual crashes at a given highway segment, crash modification factors to determine the impacts associated with various roadway and geometric characteristics, and severity distribution functions (SDFs) to predict the crash severity. County road segment data were collected across a sample of 30 counties representing all regions of Michigan. Because of the overwhelming proportion of deer crashes, only non-deer-related crashes were considered. To minimize the influence of variability among counties, the random effect negative binomial model was used to develop SPFs. In addition, a multinomial logit model was used to develop SDFs. Paved county roadways showed approximately double the crash occurrence rate of typical state-maintained two-lane rural highways, and gravel roadways showed a substantially greater crash occurrence rate than paved county roadways across the equivalent range of traffic volumes. The economic analysis showed that it is beneficial to pave a gravel road when the traffic volume is greater than 600 vehicles per day. The random effect variable is significant in all the calibrated models, which shows that there is a considerable variability among counties that cannot be captured with the available variables. Not considering the random effects will result in biased estimation of crashes.

scholarly journals Safety Evaluation of Freeway Speed-Change Lanes Based on Crashes and Simulated Conflicts

2021 ◽  
Author(s):  
Lei Qin
Keyword(s):  
Traffic Control ◽  
Goodness Of Fit ◽  
Linear Models ◽  
Negative Binomial ◽  
Prediction Models ◽  
Control Strategies ◽  
Safety Evaluation ◽  
Safety Performance ◽  
Assessment Model ◽  
Crash Prediction Models

To facilitate the evaluation of the safety performance of freeway merge, diverge, and weave areas, conventional crash-based Safety Performance Functions (SPFs) were developed using generalized linear models (GLM) with a negative binomial (NB) error structure. However, crash-based SPFs may not take into account all factors that contribute to the crashes. The use of simulated conflicts as a surrogate safety measure to predict crashes can address this issue and provide recommendations for the designs and traffic control strategies. This approach was explored by using Surrogate Safety Assessment Model (SSAM) and VISSIM software to generate and analyze conflicts for merge areas on Ontario freeways. Crash-conflict integrated SPFs with different Time to Collision (TTC) thresholds were then developed and compared. Their predictive capabilities were also evaluated. To complement this analysis, the transferability of US crash prediction models to Ontario data was evaluated and the goodness-of-fit of these models was explored.

scholarly journals Safety Evaluation of Freeway Speed-Change Lanes Based on Crashes and Simulated Conflicts

2021 ◽  
Author(s):  
Lei Qin
Keyword(s):  
Traffic Control ◽  
Goodness Of Fit ◽  
Linear Models ◽  
Negative Binomial ◽  
Prediction Models ◽  
Control Strategies ◽  
Safety Evaluation ◽  
Safety Performance ◽  
Assessment Model ◽  
Crash Prediction Models

To facilitate the evaluation of the safety performance of freeway merge, diverge, and weave areas, conventional crash-based Safety Performance Functions (SPFs) were developed using generalized linear models (GLM) with a negative binomial (NB) error structure. However, crash-based SPFs may not take into account all factors that contribute to the crashes. The use of simulated conflicts as a surrogate safety measure to predict crashes can address this issue and provide recommendations for the designs and traffic control strategies. This approach was explored by using Surrogate Safety Assessment Model (SSAM) and VISSIM software to generate and analyze conflicts for merge areas on Ontario freeways. Crash-conflict integrated SPFs with different Time to Collision (TTC) thresholds were then developed and compared. Their predictive capabilities were also evaluated. To complement this analysis, the transferability of US crash prediction models to Ontario data was evaluated and the goodness-of-fit of these models was explored.

scholarly journals The variability of urban safety performance functions for different road elements: an Italian case study

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Paolo Intini ◽  
Nicola Berloco ◽  
Gabriele Cavalluzzi ◽  
Dominique Lord ◽  
Vittorio Ranieri ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Prediction Models ◽  
Safety Performance ◽  
Common Trends ◽  
Main Research ◽  
Pavement Markings ◽  
Italian Case ◽  
Sight Distance ◽  
Safety Performance Functions ◽  
Urban Safety

Abstract Background Urban safety performance functions are used to predict crash frequencies, mostly based on Negative Binomial (NB) count models. They could be differentiated for considering homogeneous subsets of segments/intersections and different predictors. Materials and methods The main research questions concerned: a) finding the best possible subsets for segments and intersections for safety modelling, by discussing the related problems and inquiring into the variability of predictors within the subsets; b) comparing the modelling results with the existing literature to highlight common trends and/or main differences; c) assessing the importance of additional crash predictors, besides traditional variables. In the context of a National research project, traffic volumes, geometric, control and additional variables were collected for road segments and intersections in the City of Bari, Italy, with 1500 fatal+injury related crashes (2012–2016). Six NB models were developed for: one/two-way homogeneous segments, three/four-legged, signalized/unsignalized intersections. Results Crash predictors greatly vary within the different subsets considered. The effect of vertical signs on minor roads/driveways, critical sight distance, cycle crossings, pavement/markings maintenance was specifically discussed. Some common trends but also differences in both types and effect of crash predictors were found by comparing results with literature. Conclusion The disaggregation of urban crash prediction models by considering different subsets of segments and intersections helps in revealing the specific influence of some predictors. Local characteristics may influence the relationships between well-established crash predictors and crash frequencies. A significant part of the urban crash frequency variability remains unexplained, thus encouraging research on this topic.

scholarly journals Safety Analysis of Interchange Functional Areas

2018 ◽  
Vol 2672 (30) ◽  
pp. 120-130
Author(s):  
Akinfolarin Abatan ◽  
Peter T. Savolainen
Keyword(s):  
Random Effects ◽  
Traffic Control ◽  
Negative Binomial ◽  
Research Literature ◽  
Safety Performance ◽  
Random Effects Models ◽  
Traffic Crash ◽  
Design Alternatives ◽  
Traffic Conflicts ◽  
Functional Areas

Limited access facilities, such as freeways and expressways, are generally designed to the highest standards among public roads. Consequently, these facilities demonstrate crash, injury, and fatality rates that are significantly lower than other road facility types. However, these rates are generally elevated in the immediate vicinity of interchanges because of increases in traffic conflicts precipitated by weaving, merging, and diverging traffic. Given the extensive costs involved in interchange construction, it is important to discern the expected operational and safety impacts of various design alternatives. To this end, the objective of this study was to analyze safety performance within the functional areas of interchanges. The study involves the integration of traffic crash, volume, and roadway geometric data from 2010 to 2014 in the state of Iowa. Separate analyses were conducted for the freeway mainline and ramp connections. A series of safety performance functions (SPFs) were estimated for both the mainline and ramps. Random effects negative binomial models were estimated, which account for correlation in crash counts at the same location over time. The results show the frequency of crashes to vary based on traffic volume, interchange configuration, speed limit, and traffic control at the ramp terminal. The random effects models are shown to significantly outperform pooled models, which suggest there are several important location-specific factors that are not included in the analysis dataset. The SPFs from this study are also compared with several reference models from the extant research literature.

scholarly journals Deep Learning Prediction of Adverse Drug Reactions in Drug Discovery Using Open TG–GATEs and FAERS Databases

2021 ◽  
Vol 1 ◽  
Author(s):  
Attayeb Mohsen ◽  
Lokesh P. Tripathi ◽  
Kenji Mizuguchi
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Adverse Drug Reactions ◽  
Predictive Models ◽  
Prediction Models ◽  
Expression Profiles ◽  
Fine Tuning ◽  
Machine Learning Techniques ◽  
Drug Reactions ◽  
Wide Range

Machine learning techniques are being increasingly used in the analysis of clinical and omics data. This increase is primarily due to the advancements in Artificial intelligence (AI) and the build-up of health-related big data. In this paper we have aimed at estimating the likelihood of adverse drug reactions or events (ADRs) in the course of drug discovery using various machine learning methods. We have also described a novel machine learning-based framework for predicting the likelihood of ADRs. Our framework combines two distinct datasets, drug-induced gene expression profiles from Open TG–GATEs (Toxicogenomics Project–Genomics Assisted Toxicity Evaluation Systems) and ADR occurrence information from FAERS (FDA [Food and Drug Administration] Adverse Events Reporting System) database, and can be applied to many different ADRs. It incorporates data filtering and cleaning as well as feature selection and hyperparameters fine tuning. Using this framework with Deep Neural Networks (DNN), we built a total of 14 predictive models with a mean validation accuracy of 89.4%, indicating that our approach successfully and consistently predicted ADRs for a wide range of drugs. As case studies, we have investigated the performances of our prediction models in the context of Duodenal ulcer and Hepatitis fulminant, highlighting mechanistic insights into those ADRs. We have generated predictive models to help to assess the likelihood of ADRs in testing novel pharmaceutical compounds. We believe that our findings offer a promising approach for ADR prediction and will be useful for researchers in drug discovery.

scholarly journals Definite Probabilities from Division of Zero by Itself Perspective

2020 ◽  
pp. 1-26
Author(s):  
Wangui Patrick Mwangi
Keyword(s):  
Probability Distribution ◽  
Negative Binomial ◽  
Random Variable ◽  
Distribution Functions ◽  
Point Of View ◽  
Basic Knowledge ◽  
Probability Distribution Functions ◽  
New Findings ◽  
Wide Range ◽  
New Finding

Over the years, the issues surrounding the division of zero by itself remained a mystery until year 2018 when the mystery was solved in numerous ways. Afterwards, the same solutions provided opened many other doors in academic space and one of the applications is in sure probabilities. This research is all about the sure probabilities computed from the zero divided by itself point of view. The solutions obtained in the computations are in harmony with logic and basic knowledge. A wide range of already existing probability distribution functions has been applied in different scenarios to compute the sure probabilities unanimously and new findings have also been encountered along the way. Some of the discrete and continuous probability distribution functions involved are the binomial, hypergeometric, negative binomial, Poisson, normal and exponential among others. It has been found in this work that sure probabilities can be evaluated from the division of zero by itself perspective. Another new finding is that in case of combinatorial, if the numerator is smaller than the denominator, then the solutions tend to zero when knowledge in gamma functions, integrations and factorials is applied. Again, if the case of continuous pdf involves integration and random variable specified in the direction of the parameter, then indirect computation of such probabilities should be applied. Finally, it has been found that the expansion of the domains of some of the parameters in some existing probability distribution functions can be considered and the restriction in conditional probabilities can be revised.

scholarly journals Prediction of motorcyclist traffic crashes in Cartagena (Colombia): development of a safety performance function

2021 ◽  
Author(s):  
Holman Ospina-Mateus ◽  
Leonardo Augusto Quintana Jiménez ◽  
Francisco J. Lopez-Valdes ◽  
Shib Sankar Sana
Keyword(s):  
Road Safety ◽  
Traffic Accidents ◽  
Negative Binomial ◽  
Road Traffic ◽  
Negative Binomial Regression ◽  
Safety Performance ◽  
Traffic Crashes ◽  
Performance Function ◽  
Safety Performance Function ◽  
Significant Factors

Motorcyclists account for more than 380,000 deaths annually worldwide from road traffic accidents. Motorcyclists are the most vulnerable road users worldwide to road safety (28% of global fatalities), together with cyclists and pedestrians. Approximately 80% of deaths are from low- or middle-income countries. Colombia has a rate of 9.7 deaths per 100,000 inhabitants, which places it 10th in the world. Motorcycles in Colombia correspond to 57% of the fleet and generate an average of 51% of fatalities per year. This study aims to identify significant factors of the environment, traffic volume, and infrastructure to predict the number of accidents per year focused only on motorcyclists. The prediction model used a negative binomial regression for the definition of a Safety Performance Function (SPF) for motorcyclists. In the second stage, Bayes' empirical approach is implemented to identify motorcycle crash-prone road sections. The study is applied in Cartagena, one of the capital cities with more traffic crashes and motorcyclists dedicated to informal transportation (motorcycle taxi riders) in Colombia. The data of 2,884 motorcycle crashes between 2016 and 2017 are analyzed. The proposed model identifies that crashes of motorcyclists per kilometer have significant factors such as the average volume of daily motorcyclist traffic, the number of accesses (intersections) per kilometer, commercial areas, and the type of road and it identifies 55 critical accident-prone sections. The research evidences coherent and consistent results with previous studies and requires effective countermeasures for the benefit of road safety for motorcyclists.

scholarly journals Including Social and Behavioral Determinants in Predictive Models: Trends, Challenges, and Opportunities (Preprint)

2020 ◽  
Author(s):  
Marissa Tan ◽  
Elham Hatef ◽  
Delaram Taghipour ◽  
Kinjel Vyas ◽  
Hadi Kharrazi ◽  
...  
Keyword(s):  
Health Care ◽  
Predictive Models ◽  
Health Care Disparities ◽  
Prediction Models ◽  
Predictive Analytics ◽  
The United States ◽  
Digital Data ◽  
Behavioral Determinants ◽  
Wide Range ◽  
Improve Health

UNSTRUCTURED In an era of accelerated health information technology capability, health care organizations increasingly use digital data to predict outcomes such as emergency department use, hospitalizations, and health care costs. This trend occurs alongside a growing recognition that social and behavioral determinants of health (SBDH) influence health and medical care use. Consequently, health providers and insurers are starting to incorporate new SBDH data sources into a wide range of health care prediction models, although existing models that use SBDH variables have not been shown to improve health care predictions more than models that use exclusively clinical variables. In this viewpoint, we review the rationale behind the push to integrate SBDH data into health care predictive models and explore the technical, strategic, and ethical challenges faced as this process unfolds across the United States. We also offer several recommendations to overcome these challenges to reach the promise of SBDH predictive analytics to improve health and reduce health care disparities.

scholarly journals Investigation of model calibration issues in the safety performance assessment of Ontario highways

2021 ◽  
Author(s):  
S.M. Morjina Ara Begum
Keyword(s):  
Negative Binomial ◽  
Prediction Models ◽  
Temporal Correlation ◽  
Negative Binomial Regression ◽  
Estimating Equation ◽  
Repeated Measurements ◽  
Safety Performance ◽  
Dispersion Parameter ◽  
Segment Length ◽  
Linear Modeling

A set of Safety Performance Function (SPFs) commonly known as accident prediction models, were developed for evaluating the safety of Highway segments under the jurisdiction of Ministry of Transportation, Ontario (MTO). A generalized linear modeling approach was used in which negative binomial regression models were delevoped separately for total accidents and for three severity types (Property Damage Only accidents, Fatal and Injury accidents) as a function of traffic volume AADT. The SPFs were calibrated from 100m homogenous segments as well as for variable length continuous segments that are homogeneous with respect to measured traffic and geometric characteristics. For the models calibrated for Rural 2-Lane Kings Highways, the variables that had significant effects on accident occurrence were the terrain, shoulder width and segment lenght. It was observed that the disperson parameter of the negative binomial districution is large for 100m segments and smaller for longer segments. Further investigation of the dispersion parameter for Rural 2-Lane Kings Highways showed that the models calibrated with a separate dispersion parameter for each site depending on the segment length performed better that the model calibrated considering fixed dispersion parameter for all sites. For Rural 2-Lane Kings Highways, a model was calibrated with trend considering each year as a separate observation. The GEE (Generalized Estimating Equation) procedure was use to develop these models since it incorporated the temporal correlation that exists in repeated measurements. Results showed that integration of time trend and temporal correlation in the model improves the model fit.

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