Macrolevel Accident Prediction Models for Evaluating Safety of Urban Transportation Systems

2003 ◽  
Vol 1840 (1) ◽  
pp. 87-95 ◽  
Author(s):  
Alireza Hadayeghi ◽  
Amer S. Shalaby ◽  
Bhagwant Persaud
Keyword(s):  
Negative Binomial ◽  
Prediction Models ◽  
Negative Binomial Regression ◽  
Transportation Systems ◽  
Linear Modeling ◽  
Traffic Demand ◽  
Accident Prediction ◽  
Estimated Parameters ◽  
Binomial Regression ◽  
Intersection Density

A series of macrolevel prediction models that would estimate the number of accidents in planning zones in the city of Toronto, Ontario, Canada, as a function of zonal characteristics were developed. A generalized linear modeling approach was used in which negative binomial regression models were developed separately for total accidents and for severe (fatal and nonfatal injury) accidents as a function of socio-economic and demographic, traffic demand, and network data variables. The variables that had significant effects on accident occurrence were the number of households, the number of major road kilometers, the number of vehicle kilometers traveled, intersection density, posted speed, and volume-capacity ratio. The geographic weighted regression approach was used to test spatial variations in the estimated parameters from zone to zone. Mixed results were obtained from that analysis.

Traffic accident modeling: some statistical issues

2006 ◽  
Vol 33 (9) ◽  
pp. 1115-1124 ◽  
Author(s):  
Z Sawalha ◽  
T Sayed
Keyword(s):  
Road Safety ◽  
Regression Models ◽  
Model Building ◽  
Negative Binomial ◽  
Prediction Models ◽  
Negative Binomial Regression ◽  
Outlier Analysis ◽  
Accident Prediction ◽  
Binomial Regression ◽  
Statistical Issues

Accident prediction models are invaluable tools that have many applications in road safety analysis. However, there are certain statistical issues related to accident modeling that either deserve further attention or have not been dealt with adequately in the road safety literature. This paper discusses and illustrates how to deal with two statistical issues related to modeling accidents using Poisson and negative binomial regression. The first issue is that of model building or deciding which explanatory variables to include in an accident prediction model. The study differentiates between applications for which it is advisable to avoid model over-fitting and other applications for which it is desirable to fit the model to the data as closely as possible. It then suggests procedures for developing parsimonious models, i.e., models that are not over-fitted, and best-fit models. The second issue discussed in the paper is that of outlier analysis. The study suggests a procedure for the identification and exclusion of extremely influential outliers from the development of Poisson and negative binomial regression models. The procedures suggested for model building and conducting outlier analysis are more straightforward to apply in the case of Poisson regression models because of an added complexity presented by the shape parameter of the negative binomial distribution. The paper, therefore, presents flowcharts detailing the application of the procedures when modeling is carried out using negative binomial regression. The described procedures are then applied in the development of negative binomial accident prediction models for the urban arterials of the cities of Vancouver and Richmond located in the province of British Columbia, Canada. Key words: accident prediction models, overfitting, parsimony, outlier analysis, Poisson regression, negative binomial regression.

scholarly journals Effect of Traffic and Geometric Characteristics of Rural Two Lane Roads on Traffic Safety: a case study of Ilesha-Akure-Owo road, South-West, Nigeria

2018 ◽  
Vol 3 (2) ◽  
Author(s):  
Monsuru O Popoola ◽  
Oladapo S Abiola ◽  
Simeon O Odunfa
Keyword(s):  
Road Safety ◽  
Traffic Accident ◽  
Negative Binomial ◽  
Prediction Models ◽  
Negative Binomial Regression ◽  
Accident Prediction ◽  
Geometric Characteristics ◽  
South West ◽  
Binomial Regression ◽  
Accident Data

Road safety engineering involves identifying influencing factors causing traffic crashes through accident data, carrying out detailed accident studies at different locations and implementing relevant remedial measures. This study was carried out to establish relationship between traffic accident characteristics (frequency and severity) and traffic and road design characteristics on a two-lane highway. Statistical models applied in traffic accident modeling are Poisson regression, Negative Binomial regression (NB), and Zero-Inflated Negative Binomial regression (ZINB).; Traffic flow and road geometry related variables were the independent variables of the models. Using Ilesha-Akure-Owo highway, South-West, Nigeria accident prediction models were developed on the basis of accident data obtained from Federal Road Safety Commission (FRSC) during a 4-year monitoring period extending between 2012 and 2015. Curve radius (CR), lane width (LW), shoulder factor (SF), access road (CHAR), average annual daily traffic (AADT), parentage heavy good vehicle (HGV) and traffic sign posted (TSP) were the identified effective factors on crash occurrence probability. Finally, a comparison of the three models developed proved the efficiency of ZINB models against traditional Poisson and NB models. Keywords— Traffic accidents. Single carriageway, accident prediction model, road geometric characteristics.

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.

Pitfalls of Using R2 to Evaluate Goodness of Fit of Accident Prediction Models

1996 ◽  
Vol 1542 (1) ◽  
pp. 6-13 ◽  
Author(s):  
Shaw-Pin Miaou ◽  
An Lu ◽  
Harry S. Lum
Keyword(s):  
Goodness Of Fit ◽  
Traffic Accidents ◽  
Negative Binomial ◽  
Prediction Models ◽  
Negative Binomial Regression ◽  
Alternative Measure ◽  
Accident Prediction ◽  
A Value ◽  
Alternative Measures ◽  
Roadway Design

In developing statistical models of traffic accidents, flow, and roadway design, the R2 goodness-of-fit measure has been used for many years to (a) determine the overall quality and usability of the model, (b) select covariates for inclusion in the model, (c) make decisions as to whether it would be worthwhile to collect additional covariates, and (d) compare the relative quality of models developed from different studies. The pitfalls of using R2 to make these decisions and comparisons are demonstrated through computer simulations of commonly used accident prediction models, including the Poisson and negative binomial regression models. Because accident prediction models are nonnormal and functional forms are typically nonlinear, it is shown that R2 is not an appropriate measure to make any of the decisions and comparisons mentioned. Also, three properties are identified as desirable for any alternative measure to appropriately evaluate these models: (a) it should be bounded between 0 and 1—a value of 0 if no covariate is included in the model and a value of 1 if all the necessary covariates are included; (b) it should increase proportionally as equally important, independent covariates are added to the model one at a time, regardless of their order of selection; and (c) it should be invariant with respect to the mean (i.e., the value of the measure should not change by simply increasing or decreasing the value of the intercept term). Finally, two recent research efforts aimed at developing alternative measures with such properties are briefly reported.

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.

scholarly journals Is area deprivation associated with greater impacts of COVID-19 in care homes across England? A preliminary analysis of COVID-19 outbreaks and deaths

2021 ◽  
pp. jech-2020-215039 ◽  
Author(s):  
Anders Malthe Bach-Mortensen ◽  
Michelle Degli Esposti
Keyword(s):  
Negative Binomial ◽  
Care Home ◽  
Negative Binomial Regression ◽  
Population Characteristics ◽  
Care Homes ◽  
Future Research ◽  
Area Deprivation ◽  
Binomial Regression ◽  
Index Of Multiple Deprivation

IntroductionThe COVID-19 pandemic has disproportionately impacted care homes and vulnerable populations, exacerbating existing health inequalities. However, the role of area deprivation in shaping the impacts of COVID-19 in care homes is poorly understood. We examine whether area deprivation is linked to higher rates of COVID-19 outbreaks and deaths among care home residents across upper tier local authorities in England (n=149).MethodsWe constructed a novel dataset from publicly available data. Using negative binomial regression models, we analysed the associations between area deprivation (Income Deprivation Affecting Older People Index (IDAOPI) and Index of Multiple Deprivation (IMD) extent) as the exposure and COVID-19 outbreaks, COVID-19-related deaths and all-cause deaths among care home residents as three separate outcomes—adjusting for population characteristics (size, age composition, ethnicity).ResultsCOVID-19 outbreaks in care homes did not vary by area deprivation. However, COVID-19-related deaths were more common in the most deprived quartiles of IDAOPI (incidence rate ratio (IRR): 1.23, 95% CI 1.04 to 1.47) and IMD extent (IRR: 1.16, 95% CI 1.00 to 1.34), compared with the least deprived quartiles.DiscussionThese findings suggest that area deprivation is a key risk factor in COVID-19 deaths among care home residents. Future research should look to replicate these results when more complete data become available.

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