Severity Prediction and Time-Series Analysis of Vehicle Accidents Using Statistical Models PDF Download
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Author: Lisa Kaunitz
Publisher:
ISBN:
Category :
Languages : en
Pages : 47
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Book Description
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.
Author: Lisa Kaunitz
Publisher:
ISBN:
Category :
Languages : en
Pages : 47
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Book Description
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.
Author: National Research Council (U.S.). Transportation Research Board
Publisher:
ISBN:
Category : Traffic accidents
Languages : en
Pages : 148
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Book Description
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).
Author: Phyllis A. Gimotty, Kennet L. Campbell, Thipatai chirachavala, Oliver Carsten, Jame O'jay
Publisher:
ISBN:
Category :
Languages : en
Pages : 492
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Book Description
Author: Hoong Chor Chin
Publisher: Nova Science Publishers
ISBN: 9781606922705
Category : Bayesian statistical decision theory
Languages : en
Pages : 0
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Book Description
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.
Author:
Publisher:
ISBN: 9780309104463
Category : Roads
Languages : en
Pages : 264
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Book Description
Author: V.A Profillidis
Publisher: Elsevier
ISBN: 0128115149
Category : Social Science
Languages : en
Pages : 500
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Book Description
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
Author: Thomas M. England
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
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.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 200
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Book Description
Author:
Publisher:
ISBN:
Category : Traffic accidents
Languages : en
Pages : 91
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Book Description
Author: Azad Salim Abdulhafedh
Publisher:
ISBN:
Category :
Languages : en
Pages : 243
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Book Description
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.
