A Methodology for Exploring the Relationship Between Intersection Form Factors and Traffic Crashes Using Geographically Weighted Regression PDF Download
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Author: Reginald Pierre-Jean
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ISBN:
Category :
Languages : en
Pages :
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ABSTRACT: Crashes at intersections are a prominent and problematic traffic safety issue. Crashes at intersections have been studied using global linear regression and before-and-after analytical methods. The Intersection form factors measured are; intersection Legs, traffic signals, traffic calming devices, corners preset, curbs present, sidewalks, percent slope, bridge intersection, park intersection, lane width, number of lanes, and traffic volume. Geographically Weighted Regression (GWR) is novel methodological approach in intersection analysis that models the relationships of these form factors to crash rates within a spatial context. GWR proves to be a more accurate modeling method overall than global linear regression. In addition to higher model performance that GWR exhibits, GWR shows the strength and variation in relationships along the data distribution for each observation. GWR also produces a visual representation of the relationships this allows for greater interpretation of the explanatory variables. In future GWR models with higher specification will produce crash rate prediction layers that will help aid in crash intersection analysis.
Author: Reginald Pierre-Jean
Publisher:
ISBN:
Category :
Languages : en
Pages :
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ABSTRACT: Crashes at intersections are a prominent and problematic traffic safety issue. Crashes at intersections have been studied using global linear regression and before-and-after analytical methods. The Intersection form factors measured are; intersection Legs, traffic signals, traffic calming devices, corners preset, curbs present, sidewalks, percent slope, bridge intersection, park intersection, lane width, number of lanes, and traffic volume. Geographically Weighted Regression (GWR) is novel methodological approach in intersection analysis that models the relationships of these form factors to crash rates within a spatial context. GWR proves to be a more accurate modeling method overall than global linear regression. In addition to higher model performance that GWR exhibits, GWR shows the strength and variation in relationships along the data distribution for each observation. GWR also produces a visual representation of the relationships this allows for greater interpretation of the explanatory variables. In future GWR models with higher specification will produce crash rate prediction layers that will help aid in crash intersection analysis.
Author:
Publisher:
ISBN: 9780309369367
Category : Crash injuries
Languages : en
Pages : 0
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"TRB?s Transportation Research Record: Journal of the Transportation Research Board, No. 2514, explores 19 papers related to safety data, analysis, and evaluation in the transportation sector, including: Exploring Driver Error at Intersections: Key Contributors and Solutions; Level of Service of Safety Revisited; Longitudinal Analysis of Rural Interstate Fatalities in Relation to Speed Limit Policies; Predicting Crashes on Expressway Ramps with Real-Time Traffic and Weather Data; Multilevel Logistic Regression Modeling for Crash Mapping in Metropolitan Areas; Simulated Traffic Conflicts: Do They Accurately Represent Field-Measured Conflicts?; Assessing Safety Improvements to Pedestrian Crossings Using Automated Conflict Analysis; Understanding Factors Affecting Frequency of Traffic Conflicts Between Electric Bicycles and Motorized Vehicles at Signalized Intersections; Comparative Analysis of Injury Severity Resulting from Pedestrian?Motor Vehicle and Bicycle?Motor Vehicle Crashes on Roadways in Alabama; Validation of Crash Modification Factors Derived from Cross-Sectional Studies with Regression Models; Fault Determination for Crashes in Vermont: Implications of Distance from Home; Crash Patterns at Signalized Intersections; Analyses of Multiyear Statewide Secondary Crash Data and Automatic Crash Report Reviewing; Assessment of Pedestrian Risk at Crossings with Kinematic?Probabilistic Model; Predicting Driver Injury Severity in Single-Vehicle and Two-Vehicle Crashes with Boosted Regression Trees; Effects of Geodemographic Profiles of Drivers on Their Injury Severity from Traffic Crashes Using Multilevel Mixed-Effects Ordered Logit Model; Copula-Based Joint Model of Injury Severity and Vehicle Damage in Two-Vehicle Crashes; Identifying Optimal High-Risk Driver Segments for Safety Messaging: Geodemographic Modeling Approach; Evaluation of Signalized-Intersection Crash Screening Methods Based on Distance from Intersection."--Publisher's description.
Author: Becky P. Y. Loo
Publisher: CRC Press
ISBN: 1498766528
Category : Mathematics
Languages : en
Pages : 287
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Examine the Prevalence and Geography of Road CollisionsSpatial Analysis Methods of Road Traffic Collisions centers on the geographical nature of road crashes, and uses spatial methods to provide a greater understanding of the patterns and processes that cause them. Written by internationally known experts in the field of transport geography, the bo
Author: Frank A. Haight
Publisher:
ISBN:
Category : Traffic accidents
Languages : en
Pages : 110
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Author: National Research Council (U.S.). Transportation Research Board
Publisher:
ISBN:
Category : Traffic accidents
Languages : en
Pages : 148
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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: Yunteng Lao
Publisher:
ISBN:
Category : Traffic accidents
Languages : en
Pages : 175
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Book Description
Traffic collisions are a worldwide issue that can cause injury and death, which leads to billions of dollars in damages every year. Significant research efforts have been undertaken to develop and utilize statistical modeling techniques for analyzing the characteristics of crash count data. While these modeling techniques have been providing meaningful outputs, improvements on these modeling methods still need to better understand the crash risk and the contributing factors. Five important issues in crash data modeling are identified in this research. The first two issues are over or under dispersion with crash data and excess zeros within crash records. Considering that they have been well studied in the previous research, this study focuses on the remaining three major issues. The first one is relevant to the partial observations of multiple processes, i.e. crash data may be collected by different agencies that create multiple data sources and may be inconsistent. A modeling mechanism that takes advantage of all datasets for better estimation results is highly desirable. The second one is an interaction issue. Some collisions are single vehicle crashes, such as off-road crashes and rollover incidents, and some collisions involve interaction behavior, such as the Animal-Vehicle Collision (AVC) and the Vehicle-Vehicle Collision. The characteristics of crashes with interaction behavior are different from those with only one vehicle involved. It is challenging to develop a crash modeling scheme that can capture the interaction behavior. The last one is the nonlinear relationship issue. Most previous collision models are Generalized Linear Model-based (GLM-based) approaches. Such GLM-based approaches are constrained by their linear model specifications because, in most situations, the relationship between the crash rate and its contributing factors are not linear or may not even be monotonic. Thus, finding a way to model the collision data with nonlinear and non-monotonic relationships is of utmost importance. To address the issues of inconsistent observations, two techniques are developed. A fuzzy logic-based data mapping algorithm is proposed as the first technique to match data from two datasets so that duplicate crash records can be removed when combining these datasets. The membership functions of the fuzzy logic algorithm are established based on survey inputs collected from experts of the Washington State Department of Transportation (WSDOT). As verified by expert judgment collected through another survey, the accuracy of this algorithm was approximately 90%. Applying this algorithm to the two WSDOT datasets relevant to AVC, reported AVC data and the Carcass Removal (CR) data, the combined dataset has 15% -22% more records compared to the original CR dataset. The proposed algorithm is proven effective for merging the Reported AVC data and the CR data, with a combined dataset being more complete for wildlife safety studies and countermeasure evaluations. The second technique is a diagonal inflated bivariate Poisson regression (DIBP) method. It is an inflated version of bivariate Poisson regression model adopted to directly fit two datasets together. The proposed model technique was also applied to the reported AVC and CR data sets collected in Washington State between 2002 and 2006. The diagonal inflated bivariate Poisson model not only can model paired data with correlation, but also handle under- or over- dispersed data sets. Compared with three other types of models; double Poisson, bivariate Poisson, and zero-inflated double Poisson; the diagonal inflated bivariate Poisson model demonstrates its capability of fitting two datasets with remarkable overlapping portions resulting from the same stochastic process. Therefore, the diagonal inflated bivariate Poisson model provides researchers another new approach to investigating paired data sources from a different perspective. To address the issues with the interaction issue, a new occurrence mechanism-based probability model, an interaction-based model, which explicitly formulates the interactions between the objects, is introduced. The proposed method was applied to the AVC data and this method can explicitly formulate the interactions between animals and drivers to better capture the relationships among drivers' and animals' attributes, roadway and environmental factors, and AVCs. Findings of this study show that the proposed occurrence mechanism-based probability model better capture the impact of drivers' and animals' attributes on the AVC. This method can be further developed to model other types of collisions with interaction behavior. To address the nonlinear relationship issue, a Generalized Nonlinear Model (GNM)-based approach is put forward. The GNM-based approach is developed to utilize a nonlinear regression function to better elaborate non-monotonic relationships between the independent and dependent variables. Previous studies focused mainly on causal factor identification and crash risk modeling using Generalized Linear Models (GLMs), such as Poisson regression, and logistic regression among others. However, their basic assumption of a generalized linear relationship between the dependent variable (for example, crash rate) and independent variables (for example, contributing factors to crashes) established via a link function can often be violated in reality. Consequently, the GLM-based modeling results could provide biased findings and conclusions when the contributing factors have parabolic impact on the crashes. In this research, a GNM-based approach is applied with the rear end accident data and the AVC data collected from ten highway routes starting in 2002 and ending in 2006. For the rear-end collision application, the results show that truck percentage and grade have a parabolic impact: both items increase crash risks initially, but decrease risks after certain thresholds. Similarly, Annual Average Daily Traffic (AADT) and grade also have a parabolic impact on the AVC rate. Such non-monotonic relationships cannot be captured by regular GLM's, which further demonstrates the flexibility of GNM-based approaches in modeling the nonlinear relationship among data and providing more reasonable explanations. The superior GNM-based model interpretations better explain the parabolic impacts of some specific contributing factors and help in selecting and evaluating rear-end crash safety improvement plans. In Summary, these solutions proposed to address the three major issues in crash modeling are important for crash studies. The fuzzy-logic based data mapping algorithm can combine partial observations from different processes to form up a more complete dataset for a thorough analysis. The diagonal inflated bivariate Poisson models can directly take two data observation processes into account. The occurrence mechanism based probability models and GNM based models are effective methods for handling the interaction issue and non-linear relationships between dependent and independent variables.
Author: David L. Harkey
Publisher: Transportation Research Board
ISBN: 0309117380
Category : Transportation
Languages : en
Pages : 85
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TRB¿s National Cooperative Highway Research Program (NCHRP) Report 617: Accident Modification Factors for Traffic Engineering and ITS Improvements explores the development of accident modification factors (AMFs) for traffic engineering and intelligent transportation system improvements. AMFs, also known as crash reduction factors, are designed to provide a simple and quick way of estimating the safety impacts of various types of engineering improvements, encompassing the areas of signing, alignment, channelization, and other traffic engineering solutions.
Author:
Publisher: AASHTO
ISBN: 1560512717
Category : CD-ROMS.
Languages : en
Pages : 142
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Author: United States. Public Roads Bureau
Publisher:
ISBN:
Category : Accidents
Languages : en
Pages : 108
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Principal findings of this study were that geometrics alone account for only a small portion of the variance in accidents and that no relationship could be established between fatalities and the geometrics studied. The geometrics studied include several types of interchanges, paved shoulders, sight distance, delineators, surface types, and other variables. Mathematical models were developed which can provide estimates of the average number of accidents on a particular type of highway or interchange, using the appropriate variables.
Author: Hans C. Joksch
Publisher:
ISBN:
Category : Traffic accident investigation
Languages : en
Pages : 104
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