Severity Prediction And Time Series Analysis Of Vehicle Accidents Using Statistical Models

Download Severity Prediction And Time Series Analysis Of Vehicle Accidents Using Statistical Models full books in PDF, epub, and Kindle. Read online Severity Prediction And Time Series Analysis Of Vehicle Accidents Using Statistical Models ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!

Severity Prediction and Time-Series Analysis of Vehicle Accidents Using Statistical Models

Author : Lisa Kaunitz
Publisher :
ISBN 13 :
Total Pages : 47 pages
Book Rating : 4.:/5 (132 download)

DOWNLOAD NOW!

Book Synopsis Severity Prediction and Time-Series Analysis of Vehicle Accidents Using Statistical Models by : Lisa Kaunitz

Download or read book Severity Prediction and Time-Series Analysis of Vehicle Accidents Using Statistical Models written by Lisa Kaunitz and published by . This book was released on 2022 with total page 47 pages. Available in PDF, EPUB and Kindle. Book excerpt: This study explores factors that effect vehicle accidents, predicts the severity of accidents through logistic regression, and forecasts the number of future accidents to occur using time-series analysis. From insights gathered during exploration, a final dataset is prepared for the use of a logistic regression model. The final model predicts whether or not an accident will be severe with an accuracy of 82%, and reveals the three main features that statistically contribute to the odds of an accident having a severe impact on traffic. Finally, a time-series analysis is run in order to model the number of accidents that can occur on a given day using historical data. This paper evaluates the dataset in ways that have yet to be explored, and provides a great baseline understanding of what is possible for the future of transportation.

Highway and Traffic Safety

Author : National Research Council (U.S.). Transportation Research Board
Publisher :
ISBN 13 :
Total Pages : 148 pages
Book Rating : 4.X/5 (4 download)

DOWNLOAD NOW!

Book Synopsis Highway and Traffic Safety by : National Research Council (U.S.). Transportation Research Board

Download or read book Highway and Traffic Safety written by National Research Council (U.S.). Transportation Research Board and published by . This book was released on 2000 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: Transportation Research Record contains the following papers: Method for identifying factors contributing to driver-injury severity in traffic crashes (Chen, WH and Jovanis, PP); Crash- and injury-outcome multipliers (Kim, K); Guidelines for identification of hazardous highway curves (Persaud, B, Retting, RA and Lyon, C); Tools to identify safety issues for a corridor safety-improvement program (Breyer, JP); Prediction of risk of wet-pavement accidents : fuzzy logic model (Xiao, J, Kulakowski, BT and El-Gindy, M); Analysis of accident-reduction factors on California state highways (Hanley, KE, Gibby, AR and Ferrara, T); Injury effects of rollovers and events sequence in single-vehicle crashes (Krull, KA, Khattack, AJ and Council, FM); Analytical modeling of driver-guidance schemes with flow variability considerations (Kaysi, I and Ail, NH); Evaluating the effectiveness of Norway's speak out! road safety campaign : The logic of causal inference in road safety evaluation studies (Elvik, R); Effect of speed, flow, and geometric characteristics on crash frequency for two-lane highways (Garber, NJ and Ehrhart, AA); Development of a relational accident database management system for Mexican federal roads (Mendoza, A, Uribe, A, Gil, GZ and Mayoral, E); Estimating traffic accident rates while accounting for traffic-volume estimation error : a Gibbs sampling approach (Davis, GA); Accident prediction models with and without trend : application of the generalized estimating equations procedure (Lord, D and Persaud, BN); Examination of methods that adjust observed traffic volumes on a network (Kikuchi, S, Miljkovic, D and van Zuylen, HJ); Day-to-day travel-time trends and travel-time prediction form loop-detector data (Kwon, JK, Coifman, B and Bickel, P); Heuristic vehicle classification using inductive signatures on freeways (Sun, C and Ritchie, SG).

STATISTICAL ANALYSIS OF THE NATIOAL CRASH SEVERITY STUDY DATA

Author : Phyllis A. Gimotty, Kennet L. Campbell, Thipatai chirachavala, Oliver Carsten, Jame O'jay
Publisher :
ISBN 13 :
Total Pages : 492 pages
Book Rating : 4.L/5 (4 download)

DOWNLOAD NOW!

Book Synopsis STATISTICAL ANALYSIS OF THE NATIOAL CRASH SEVERITY STUDY DATA by : Phyllis A. Gimotty, Kennet L. Campbell, Thipatai chirachavala, Oliver Carsten, Jame O'jay

Download or read book STATISTICAL ANALYSIS OF THE NATIOAL CRASH SEVERITY STUDY DATA written by Phyllis A. Gimotty, Kennet L. Campbell, Thipatai chirachavala, Oliver Carsten, Jame O'jay and published by . This book was released on 1980 with total page 492 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Modeling Multilevel Data in Traffic Safety

Author : Hoong Chor Chin
Publisher : Nova Science Publishers
ISBN 13 : 9781606922705
Total Pages : 0 pages
Book Rating : 4.9/5 (227 download)

DOWNLOAD NOW!

Book Synopsis Modeling Multilevel Data in Traffic Safety by : Hoong Chor Chin

Download or read book Modeling Multilevel Data in Traffic Safety written by Hoong Chor Chin and published by Nova Science Publishers. This book was released on 2013 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Background: In the study of traffic system safety, statistical models have been broadly applied to establish the relationships between the traffic crash occurrence and various risk factors. Most of the existing methods, such as the generalised linear regression models, assume that each observation (e.g. a crash or a vehicle involvement) in the estimation procedure corresponds to an individual situation. Hence, the residuals from the models exhibit independence. Problem: However, this "independence" assumption may often not hold true since multilevel data structures exist extensively because of the data collection and clustering process. Disregarding the possible within-group correlations may lead to production of models with unreliable parameter estimates and statistical inferences. Method: Following a literature review of crash prediction models, this book proposes a 5 T-level hierarchy, viz. (Geographic region level -- Traffic site level -- Traffic crash level -- Driver-vehicle unit level -- Vehicle-occupant level) Time level, to establish a general form of multilevel data structure in traffic safety analysis. To model properly the potential between-group heterogeneity due to the multilevel data structure, a framework of hierarchical models that explicitly specify multilevel structure and correctly yield parameter estimates is employed. Bayesian inference using Markov chain Monte Carlo algorithm is developed to calibrate the proposed hierarchical models. Two Bayesian measures, viz. the Deviance Information Criterion and Cross-Validation Predictive Densities, are adapted to establish the model suitability. Illustrations: The proposed method is illustrated using two case studies in Singapore: 1) a crash-frequency prediction model which takes into account Traffic site level and Time level; 2) a crash-severity prediction model which takes into account Traffic crash level and Driver-vehicle unit level. Conclusion: Comparing the predictive abilities of the proposed models against those of traditional methods, the study demonstrates the importance of accounting for the within-group correlations and illustrates the flexibilities and effectiveness of the Bayesian hierarchical approach in modelling multilevel structure of traffic safety data.

Statistical Methods, Safety Data, Analysis, and Evaluation, 2007

Author :
Publisher :
ISBN 13 : 9780309104463
Total Pages : 264 pages
Book Rating : 4.1/5 (44 download)

DOWNLOAD NOW!

Book Synopsis Statistical Methods, Safety Data, Analysis, and Evaluation, 2007 by :

Download or read book Statistical Methods, Safety Data, Analysis, and Evaluation, 2007 written by and published by . This book was released on 2007 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Modeling of Transport Demand

Author : V.A Profillidis
Publisher : Elsevier
ISBN 13 : 0128115149
Total Pages : 500 pages
Book Rating : 4.1/5 (281 download)

DOWNLOAD NOW!

Book Synopsis Modeling of Transport Demand by : V.A Profillidis

Download or read book Modeling of Transport Demand written by V.A Profillidis and published by Elsevier. This book was released on 2018-10-23 with total page 500 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modeling of Transport Demand explains the mechanisms of transport demand, from analysis to calculation and forecasting. Packed with strategies for forecasting future demand for all transport modes, the book helps readers assess the validity and accuracy of demand forecasts. Forecasting and evaluating transport demand is an essential task of transport professionals and researchers that affects the design, extension, operation, and maintenance of all transport infrastructures. Accurate demand forecasts are necessary for companies and government entities when planning future fleet size, human resource needs, revenues, expenses, and budgets. The operational and planning skills provided in Modeling of Transport Demand help readers solve the problems they face on a daily basis. Modeling of Transport Demand is written for researchers, professionals, undergraduate and graduate students at every stage in their careers, from novice to expert. The book assists those tasked with constructing qualitative models (based on executive judgment, Delphi, scenario writing, survey methods) or quantitative ones (based on statistical, time series, econometric, gravity, artificial neural network, and fuzzy methods) in choosing the most suitable solution for all types of transport applications. Presents the most recent and relevant findings and research - both at theoretical and practical levels - of transport demand Provides a theoretical analysis and formulations that are clearly presented for ease of understanding Covers analysis for all modes of transportation Includes case studies that present the most appropriate formulas and methods for finding solutions and evaluating results

Road Traffic Crash Severity Prediction Using Multi-State Data

Author : Thomas M. England
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (125 download)

DOWNLOAD NOW!

Book Synopsis Road Traffic Crash Severity Prediction Using Multi-State Data by : Thomas M. England

Download or read book Road Traffic Crash Severity Prediction Using Multi-State Data written by Thomas M. England and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The socioeconomic burden of road traffic crashes is immense. Safer roads and vehicular mechanisms to reduce distracted driving help reduce collisions. Additionally, computational models can be used to understand the reasons for crashes and devise interventions. We study models predicting the severity of a crash based on the data reported at the crash scene. Many U.S. states have developed traffic safety programs to make the anonymized crash data publicly available. These datasets aid researchers in the creation of predictive models for crashes. While many states make data from collisions publicly available, each state reports data differently. There is a lack of standardization. As a result, it is difficult for researchers to develop machine learning algorithms to process data from multiple states without adequate preprocessing. Currently, the vast majority of projects in this field of study utilize a dataset of a single city, road, or state. This limits the use of the developed model to a region. This project aims to create a large crash database that will allow researchers to develop algorithms that utilize data from across the country. Additionally, we want to examine if the use of data from multiple states is effective in increasing the accuracy of machine learning models. In order to achieve these goals, we develop software to find common data categories from state reports and combine them into one large dataset. The data categories were selected based on reports from previous projects that identified variables having a large impact on model accuracy. In order to test the effectiveness of the new multi-state dataset, we used two models (neural network-based and decision tree-based) to predict crash injury severity. We trained and tested these models on datasets from a single state, combined two-state datasets, and a combined multi-state dataset. The results of this research reveal that there is a drop in accuracy when data from multiple states are combined. This trend is present in both the models tested, with the trend being more pronounced in the decision tree. There are some cases in the neural network model where multi-state data lead to a higher accuracy compared to the single-state experiments. We also observe a downward trend between neural network accuracy and the distance between the states present in the dataset. This implies that the closer the states are together geographically, the better the accuracy will be using the neural network model. In the decision tree model, there is a positive correlation between overall accuracy and the number of features present in the dataset. This observation means that the more features states have in common, the better the accuracy will be for a decision tree classifier. The software artifacts from this project are open-sourced.

National Center for Statistics and Analysis Collected Technical Studies. Volume II: Accident Data Analysis of Occupant Injuries and Crash Characteristics - Eight Papers

Download National Center for Statistics and Analysis Collected Technical Studies. Volume II: Accident Data Analysis of Occupant Injuries and Crash Characteristics - Eight Papers PDF Online Free

Author :
Publisher :
ISBN 13 :
Total Pages : 200 pages
Book Rating : 4.3/5 (91 download)

DOWNLOAD NOW!

Book Synopsis National Center for Statistics and Analysis Collected Technical Studies. Volume II: Accident Data Analysis of Occupant Injuries and Crash Characteristics - Eight Papers by :

Download or read book National Center for Statistics and Analysis Collected Technical Studies. Volume II: Accident Data Analysis of Occupant Injuries and Crash Characteristics - Eight Papers written by and published by . This book was released on 1981 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Statistical Methods and Crash Prediction Modeling

Author :
Publisher :
ISBN 13 :
Total Pages : 91 pages
Book Rating : 4.:/5 (279 download)

DOWNLOAD NOW!

Book Synopsis Statistical Methods and Crash Prediction Modeling by :

Download or read book Statistical Methods and Crash Prediction Modeling written by and published by . This book was released on 2006 with total page 91 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Crash Severity Modeling in Transportation Systems

Author : Azad Salim Abdulhafedh
Publisher :
ISBN 13 :
Total Pages : 243 pages
Book Rating : 4.:/5 (987 download)

DOWNLOAD NOW!

Book Synopsis Crash Severity Modeling in Transportation Systems by : Azad Salim Abdulhafedh

Download or read book Crash Severity Modeling in Transportation Systems written by Azad Salim Abdulhafedh and published by . This book was released on 2016 with total page 243 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modeling crash severity is an important component of reasoning about the issues that may affect highway safety. A better understanding of the factors underlying crash severity can be used to reduce the degree of crash severity injury, locate road hazardous sites, and adopt suitable countermeasures. In order to provide insights on the mechanism and behavior of the crash severity injury, a variety of statistical approaches have been utilized to model the relationship between crash severity and potential risk factors. Many of the traditional approaches for analyzing crash severity are limited in that they are based on the assumption that all observations are independent of each other. However, given the reality of vehicle movement in networked systems, the assumption of independence of crash incidence is not likely valid. For instance, spatial and temporal autocorrelations are important sources of dependency among observations that may bias estimates if not considered in the modeling process. Moreover, there are other aspects of vehicular travel that may influence crash severity that have not been explored in traditional analysis approaches. One such aspect is the roadway visibility that is available to a driver at a given time that can impact their ability to react to changing traffic conditions, a characteristics known as sight distance. Accounting for characteristics such as sight distance in crash severity modeling involve moving beyond statistical analysis and modeling the complex geospatial relationships between the driver and the surrounding landscape. To address these limitations of traditional approaches to crash severity modeling, this dissertation first details a framework for detecting temporal and spatial autocorrelation in crash data. An approach for evaluating the sight distance available to drivers along roadways is then proposed. Finally, a crash severity model is developed based upon a multinomial logistic regression approach that incorporates the available sight distance and spatial autocorrelation as potential risk factors, in addition to a wide range of other factors related to road geometry, traffic volume, driver's behavior, environment, and vehicles. To demonstrate the characteristics of the proposed model, an analysis of vehicular crashes (years 2013-2015) along the I-70 corridor in the state of Missouri (MO) and on roadways in Boone County MO is conducted. To assess existing stopping sight distance and decision sight distance on multilane highways, a geographic information system (GIS)-based viewshed analysis is developed to identify the locations that do not conform to AASHTO (2011) criteria regarding stopping and decision sight distances, which could then be used as potential risk factors in crash prediction. Moreover, this method provides a new technique for estimating passing sight distance along two-lane highways, and locating the passing zones and no-passing zones. In order to detect the existence of temporal autocorrelation and whether it's significant in crash data, this dissertation employs the Durbin-Watson (DW) test, the Breusch-Godfrey (LM) test, and the Ljung-Box Q (LBQ) test, and then describes the removal of any significant amount of temporal autocorrelation from crash data using the differencing procedure, and the Cochrane-Orcutt method. To assess whether vehicle crashes are spatially clustered, dispersed, or random, the Moran's I and Getis-Ord Gi* statistics are used as measures of spatial autocorrelation among vehicle incidents. To incorporate spatial autocorrelation in crash severity modeling, the use of the Gi* statistic as a potential risk factor is also explored. The results provide firm evidence on the importance of accounting for spatial and temporal autocorrelation, and sight distance in modeling traffic crash data.

Statistical Methods and Modeling and Safety Data, Analysis, and Evaluation

Author : National Research Council (U.S.). Transportation Research Board
Publisher :
ISBN 13 :
Total Pages : 212 pages
Book Rating : 4.:/5 ( download)

DOWNLOAD NOW!

Book Synopsis Statistical Methods and Modeling and Safety Data, Analysis, and Evaluation by : National Research Council (U.S.). Transportation Research Board

Download or read book Statistical Methods and Modeling and Safety Data, Analysis, and Evaluation written by National Research Council (U.S.). Transportation Research Board and published by . This book was released on 2003 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt: Covers empirical approaches to outlier detection in intelligent transportation systems data, modeling of traffic crash-flow relationships for intersections, profiling of high-frequency accident locations by use of association rules, analysis of rollovers and injuries with sport utility vehicles, and automated accident detection at intersections via digital audio signal processing.

Statistical Methods and Accident Analysis for Highway and Traffic Safety

Author :
Publisher :
ISBN 13 :
Total Pages : 60 pages
Book Rating : 4.:/5 (298 download)

DOWNLOAD NOW!

Book Synopsis Statistical Methods and Accident Analysis for Highway and Traffic Safety by :

Download or read book Statistical Methods and Accident Analysis for Highway and Traffic Safety written by and published by . This book was released on 1996 with total page 60 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Road Accident Prediction Using LSTM GRU Neural Networks

Author : Prudvi Saisaran Ponduru
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (137 download)

DOWNLOAD NOW!

Book Synopsis Road Accident Prediction Using LSTM GRU Neural Networks by : Prudvi Saisaran Ponduru

Download or read book Road Accident Prediction Using LSTM GRU Neural Networks written by Prudvi Saisaran Ponduru and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Traffic accidents are a significant source of mortality and economic damage on a global scale. If it is not possible to make the roads safer, there is a need for better algorithms to forecast the severity of incidents. It is possible to divide the outcomes of a car accident into three categories depending on the severity of the injuries sustained by the driver: property damage, probable injury, and death (Salanova Grau et al., 2018). In order to solve the problem of identifying patterns in the severity of accidents, researchers have turned to deep learning, statistics, and even physical modelling (Liu, Liu, Song, & Liu, 2017). In order to convert an input vector into an output vector, deep learning models often use a sequence of nonlinear functions. Input vectors for accident severity include driver conduct and road, vehicle, and environmental elements. The resulting table covers the spectrum of potential accident outcomes. It is possible to train computer models using deep learning methods.

Advanced Statistical Modeling of the Frequency and Severity of Traffic Crashes on Rural Highways

Author : Irfan Uddin Ahmed
Publisher :
ISBN 13 :
Total Pages : 222 pages
Book Rating : 4.4/5 (387 download)

DOWNLOAD NOW!

Book Synopsis Advanced Statistical Modeling of the Frequency and Severity of Traffic Crashes on Rural Highways by : Irfan Uddin Ahmed

Download or read book Advanced Statistical Modeling of the Frequency and Severity of Traffic Crashes on Rural Highways written by Irfan Uddin Ahmed and published by . This book was released on 2022 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: The primary objective of practitioners working on traffic safety is to reduce the number and severity of crashes. The Highway Safety Manual (HSM) provides practitioners with analytical tools and techniques to estimate the expected crash frequency and severity with the aim to identify and evaluate safety countermeasures. Expected crash frequency can be estimated using Safety Performance Functions (SPFs) provided in Part C of the HSM. The HSM provides simple SPFs which are developed using the most frequently used crash counts model, the negative binomial regression model. The rural nature of Wyoming highways coupled with the mountainous terrain (i.e., challenging roadway geometry) make the HSM basic SPFs unsuitable to determine crash contributing factors for Wyoming conditions. In this regard, the objective of this study is to implement advanced statistical methods such as the different functional forms of Negative Binomial, and Bayesian approach, to develop crash prediction models, investigate crash contributing factors, and determine the impact of safety countermeasures. Bayesian statistics in combination with the power of Markov Chain Monte Carlo (MCMC) sampling techniques provide frameworks to model small sample datasets and complex models at the same time, where the traditional Maximum Likelihood Estimation (MLE) based methods tend to fail. As such, a novel No-U-Turn Sampler for Hamiltonian Monte Carlo (NUTS HMC) sampling technique in a Bayesian framework was utilized to investigate the crash frequency, injury severity of crashes on the interstate freeways and some rural highways in Wyoming. The Poisson and the Negative Binomial (NB) models are the most commonly used regression models in traffic safety analysis. The advantage of the NB model can be further enhanced by providing different functional forms of the variance and the dispersion structure. The NB-2 is the most common form of the NB model, typically used in developing safety performance functions (SPFs) largely due to the mean-variance quadratic relationship. However, studies in the literature have shown that the mean-variance relationship could be unrestrained. Another introduced formulation of the NB model is NB-1, which assumes that there is a constant ratio linking the mean and the variance of the crash frequencies. A more general type of the NB model is the NB-P model, which does not constrain the mean-variance relationship. Thus, leveraging the power of this unrestrained mean-variance relationship, more accurate safety models could be developed, and these would lead to more accurate estimation of crash risk and benefits of potential solutions. This study will help practitioners to implement advanced methodologies to solve traffic safety problems of rural highways that have plagued the researchers for a long time now. The methodologies proposed in this study will help practitioners to replace the outdated and inefficient traditional models and obtain more accurate traffic safety models to predict crashes and the resulting crash injury severity. Moreover, this research quantified the safety effectiveness of some unique countermeasures on rural highways.

Application of Artificial Neural Networks in Geoinformatics

Author : Saro Lee
Publisher : MDPI
ISBN 13 : 303842742X
Total Pages : 229 pages
Book Rating : 4.0/5 (384 download)

DOWNLOAD NOW!

Book Synopsis Application of Artificial Neural Networks in Geoinformatics by : Saro Lee

Download or read book Application of Artificial Neural Networks in Geoinformatics written by Saro Lee and published by MDPI. This book was released on 2018-04-09 with total page 229 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a printed edition of the Special Issue "Application of Artificial Neural Networks in Geoinformatics" that was published in Applied Sciences

An Application of Data Analytics to Outcomes of Missouri Motor Vehicle Crashes

Author :
Publisher :
ISBN 13 :
Total Pages : 215 pages
Book Rating : 4.:/5 (919 download)

DOWNLOAD NOW!

Book Synopsis An Application of Data Analytics to Outcomes of Missouri Motor Vehicle Crashes by :

Download or read book An Application of Data Analytics to Outcomes of Missouri Motor Vehicle Crashes written by and published by . This book was released on 2015 with total page 215 pages. Available in PDF, EPUB and Kindle. Book excerpt: Motor vehicle crashes are a leading cause of death in the United States, cost Americans $277 billion annually, and generate serious psychological burdens. As a result, extensive vehicle safety research focusing on the explanatory factors of crash severity is undertaken using a wide array of methodological techniques including traditional statistical models and contemporary data mining approaches. This study advances the methodological frontier of crash severity research by completing an empirical investigation that compares the performance of popular, longstanding techniques of multinomial logit and ordinal probit models with more recent methods of decision tree and artificial neural network models. To further the investigation of the benefits of data analytics, individual models are combined into model ensembles using three popular combinatory techniques. The models are estimated using 2002 to 2012 crash data from the Missouri State Highway Patrol Traffic Division - Statewide Traffic Accident Records System database, and variables examined include various driver characteristics, temporal factors, weather conditions, road characteristics, crash type, crash location, and injury severity levels. The accuracy and discriminatory power of explaining crash severity outcomes among all methods are compared using classification tables, lift charts, ROC curves, and AUC values. The CHAID decision tree model is found to have the greatest accuracy and discriminatory power relative to all evaluated modeling approaches. The modeling reveals that the presence of alcohol, driving at speeds that exceed the limit, failing to yield, driving on the wrong side of the road, violating a stop sign or signal, and driving while physically impaired lead to a large number of fatalities each year. Yet, the effect of these factors on the probability of a severe outcome is dependent upon other variables, including number of occupants involved in the crash, speed limit, lighting condition, and age of the driver. The CHAID decision tree is used in conjunction with prior literature and the current Missouri rules of the road to provide better formulated driving policies. This study concludes that policy makers should consider the interaction of conditions and driver related contributing factors when crafting future legislation or proposing modifications in driving statues.

Statistical Methods

Author : National Research Council (U.S.). Transportation Research Board
Publisher :
ISBN 13 :
Total Pages : 254 pages
Book Rating : 4.:/5 ( download)

DOWNLOAD NOW!

Book Synopsis Statistical Methods by : National Research Council (U.S.). Transportation Research Board

Download or read book Statistical Methods written by National Research Council (U.S.). Transportation Research Board and published by . This book was released on 2005 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Transportation Research Record contains 28 papers dealing with statistical methods in highway safety research; highway safety data, analysis, and evaluation; occupant protection; and systematic reviews and meta-analysis. The papers address such topics as risk and crash prediction models, crashes on freeways and at signalized intersections, multivehicle crash prediction, speed and safety, red light running crashes, freeway lane closures, ramp design, accident exposure, rumble strip benefits, collisions with median trees, intersection safety, accident reconstruction, safety effects of speed limit changes, geometric design and head-on crashes, deer-vehicle crashes, sport utility vehicle rollover, vehicle occupancy and crash risk, a logit model for studying injury severity, abdominal injuries in rail passengers, healthy transport policies, and meta-analysis.