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 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.

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.

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 Identifying Temporal Aggregation Effect on Crash-Frequency Modeling

2021 ◽  
Vol 13 (11) ◽  
pp. 6214
Author(s):  
Bumjoon Bae ◽  
Changju Lee ◽  
Tae-Young Pak ◽  
Sunghoon Lee
Keyword(s):  
Traffic Safety ◽  
Negative Binomial ◽  
Random Effect ◽  
Temporal Correlation ◽  
Negative Binomial Regression ◽  
Spatiotemporal Analysis ◽  
Parameter Estimates ◽  
Crash Frequency ◽  
Crash Frequency Modeling ◽  
Using Data

Aggregation of spatiotemporal data can encounter potential information loss or distort attributes via individual observation, which would influence modeling results and lead to an erroneous inference, named the ecological fallacy. Therefore, deciding spatial and temporal resolution is a fundamental consideration in a spatiotemporal analysis. The modifiable temporal unit problem (MTUP) occurs when using data that is temporally aggregated. While consideration of the spatial dimension has been increasingly studied, the counterpart, a temporal unit, is rarely considered, particularly in the traffic safety modeling field. The purpose of this research is to identify the MTUP effect in crash-frequency modeling using data with various temporal scales. A sensitivity analysis framework is adopted with four negative binomial regression models and four random effect negative binomial models having yearly, quarterly, monthly, and weekly temporal units. As the different temporal unit was applied, the result of the model estimation also changed in terms of the mean and significance of the parameter estimates. Increasing temporal correlation due to using the small temporal unit can be handled with the random effect models.

scholarly journals Analysis of Nonsevere Crashes on Two- and Four-Lane Urban and Rural Highways: Effects of Wet Pavement Surface Condition

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Aschalew Kassu ◽  
Michael Anderson
Keyword(s):  
Negative Binomial ◽  
Surface Condition ◽  
Negative Binomial Regression ◽  
Segment Length ◽  
Mean Values ◽  
Rural Highways ◽  
Explanatory Variables ◽  
Crash Data ◽  
Pavement Surface ◽  
Binomial Regression

This study examines the effects of wet pavement surface conditions on the likelihood of occurrences of nonsevere crashes in two- and four-lane urban and rural highways in Alabama. Initially, sixteen major highways traversing across the geographic locations of the state were identified. Among these highways, the homogenous routes with equal mean values, variances, and similar distributions of the crash data were identified and combined to form crash datasets occurring on dry and wet pavements separately. The analysis began with thirteen explanatory variables covering engineering, environmental, and traffic conditions. The principal terms were statistically identified and used in a mathematical crash frequency models developed using Poisson and negative binomial regression models. The results show that the key factors influencing nonsevere crashes on wet pavement surfaces are mainly segment length, traffic volume, and posted speed limits.

086 The rapid efficacy of natalizumab vs fingolimod in patients with active relapsing-remitting multiple sclerosis (RRMS): results from reveal, a randomised, head-to-head study

2018 ◽  
Vol 89 (6) ◽  
pp. A35.1-A35
Author(s):  
Helmut Butzkueven ◽  
Douglas Jeffery ◽  
Douglas L Arnold ◽  
Massimo Filippi ◽  
Jeroen JG Geurts ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Cox Regression ◽  
Negative Binomial Regression ◽  
Patient Characteristics ◽  
Estimating Equation ◽  
Study Patient ◽  
Cumulative Probability ◽  
Open Label ◽  
Randomised Study ◽  
Kaplan Meier

IntroductionREVEAL was designed as a 1 year, multicentre, randomised, rater- and sponsor-blinded, prospective study comparing natalizumab and fingolimod in patients with active RRMS. Although the study closed early (for non-safety/non-efficacy reasons), data permitted comparison of effects occurring shortly after treatment initiation. This analysis compares onset of efficacy with natalizumab and fingolimod in REVEAL.MethodsPatients were randomised to open-label intravenous natalizumab 300 mg every 4 weeks (n=54) or oral fingolimod 0.5 mg once daily (n=54). Magnetic resonance imaging was scheduled every 4 weeks for the first 24 weeks and at weeks 36 and 52. Analyses included Kaplan-Meier and Cox regression, negative binomial regression (annualised relapse rate [ARR] and number of T1 gadolinium-enhancing [Gd+] lesions) and a negative binomial generalised estimating equation (cumulative Gd +lesions over time).ResultsAs expected for a randomised study, patient characteristics and follow-up time (median 39 weeks) were generally similar between groups. Natalizumab patients were less likely than fingolimod patients to develop new Gd +lesions (for ≥1 lesion, cumulative probability 40.68% vs 57.99%; hazard ratio [HR]=1.678 [95% CI: 0.865 to 3.255]; p=0.1258; for ≥2 lesions, cumulative probability 11.54% vs 48.48%; HR=4.053 [95% CI: 1.474 to 11.144]; p=0.007). Natalizumab patients consistently had 63%–72% fewer Gd +lesions than fingolimod patients, with between-group differences apparent within 4 weeks and reaching significance by 12 weeks (p=0.030). ARR was 83% lower with natalizumab than with fingolimod (0.05 vs 0.29; p=0.023), and cumulative probability of relapse was 1.85% with natalizumab vs 22.28% with fingolimod (HR=12.184 [95% CI: 1.552 to 95.634]; p=0.017). Adverse events were consistent with known safety profiles.ConclusionThese results suggest that natalizumab reduces disease activity more rapidly and to a greater extent than fingolimod in patients with active RRMS. Given the early study closure, available data did not permit primary endpoint evaluation, and interpretation of these results requires caution.Study SupportBiogen.

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.

Incident Occurrence Models for Freeway Incident Management

2003 ◽  
Vol 1856 (1) ◽  
pp. 125-135 ◽  
Author(s):  
Sravanthi Konduri ◽  
Samuel Labi ◽  
Kumares C. Sinha
Keyword(s):  
Negative Binomial ◽  
Prediction Models ◽  
Negative Binomial Regression ◽  
Decision Support Tool ◽  
Cost Effective ◽  
Traffic Volume ◽  
Incident Management ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Support Tool

Incident prediction models are presented for the Interstate 80/Interstate 94 (Borman Expressway in northwestern Indiana) and Interstate 465 (northeastern Indianapolis, Indiana) freeway sections developed as a function of traffic volume, truck percentage, and weather. Separate models were developed for all incidents and noncrash incidents. Three model types were considered (Poisson regression, negative binomial regression, and nonlinear regression), and the results were compared based on magnitudes and signs of model parameter estimates and t-statistics. Least-squares estimation and maximum-likelihood methods were used to estimate the model parameters. Data from the Indiana Department of Transportation and the Indiana Climatology Database were used to establish the relationships. For a given session and incident category, the results from the Poisson and negative binomial models were found to be consistent. It was observed that, unlike section length, traffic volume is nonlinearly related to incidents, and therefore these two variables have to be considered as separate terms in the modeling process. Truck percentage was found to be a statistically significant factor affecting incident occurrence. It was also found that the weather variable (rain and snow) was negatively correlated to incidents. The freeway incident models developed constitute a useful decision support tool for implementation of new freeway patrol systems or for expansion of existing ones. They are also useful for simulating incident occurrences with a view to identifying elements of cost-effective freeway patrol strategies (patrol deployment policies, fleet size, crew size, and beat routes).

Effect of Median Design on Rural Freeway Safety

2008 ◽  
Vol 2060 (1) ◽  
pp. 29-37 ◽  
Author(s):  
Andrew P. Tarko ◽  
Natalie M. Villwock ◽  
Nicolas Blond
Keyword(s):  
Opposite Direction ◽  
Negative Binomial ◽  
Prediction Models ◽  
Negative Binomial Regression ◽  
Well Being ◽  
Crash Severity ◽  
Before And After ◽  
Single Vehicle ◽  
Median Width ◽  
The Impact

Although median barriers are an absolute means of preventing drivers from crossing road medians and colliding with vehicles moving in the opposite direction, they may cause additional crashes. This perhaps complex safety effect of median barriers has not been investigated well. Being able to predict the safety impact of most types of median barriers on rural freeways is becoming more desirable because some state departments of transportation plan to expand many of their four-lane rural freeways to six lanes to accommodate increases in traffic volume. Realistic crash prediction models sensitive to the median design would provide the needed guidance useful in designing adequate median treatments on widened freeways. The impact of median designs on crash frequency was investigated in this study through negative binomial regression and before-and-after studies based on data collected in eight participating states. The impact on crash severity was investigated with a logit model. The separate effects of changes in median geometry were quantified for single-vehicle, multiple-vehicle same direction, and multiple-vehicle opposite direction crashes. The results were significantly different and indicated that reducing the median width without adding barriers (the remaining median width is still reasonably wide) increases the severity of crashes, particularly opposite direction crashes. Further, reducing the median and installing concrete barriers eliminates opposite direction crashes but doubles the frequency of single-vehicle crashes and tends to lessen the frequency of same direction crashes. The crash severity also tends to increase.

scholarly journals Highway Design and Safety Consequences: A Case Study of Interstate Highway Vertical Grades

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Zongxin Tang ◽  
Sikai Chen ◽  
Jianchuan Cheng ◽  
Seyed Ali Ghahari ◽  
Samuel Labi
Keyword(s):  
Negative Binomial ◽  
Safety Performance ◽  
Segment Length ◽  
Crash Severity ◽  
Vertical Alignment ◽  
Design Features ◽  
Highway Design ◽  
Interstate Highway ◽  
Feature Lines ◽  
Different Levels

Vertical alignment, which includes vertical grades and lengths, is a critical aspect of highway design policy that influences safety. A full understanding of the effect of vertical grade and segment length on highway safety can help agencies to evaluate or adjust their design policies regarding vertical alignment design features (grade and length). For this reason, it is useful to assess the current relationships between design policy and safety performance. To address this task, this paper uses data from interstate segments to first establish the relationship between these design features and safety. Safety is expressed in terms of the three different levels of crash severity (fatal, injury, and property damage only). In its analysis, the paper departs from the traditional univariate models (where each crash severity is modeled separately) and instead uses a seemingly unrelated negative binomial (SUNB) technique, a multivariate model that duly accounts for the unobserved shared effects between the different levels of crash severity. In addition, the paper’s models duly recognize and account for the holistic nature of the grade and tangent length effects: the effect of the sum (interaction) of the vertical grade and length is different from the sum of their individual effects. The paper investigates the relationships for rural and urban interstate highway segments. Against the background of the developed models, the paper evaluates current design policies (specifications on vertical alignment grade and length) for similar classes of highways at a number of countries and presents a set of nomograms that feature lines representing points of equal safety performance. These charts can be used by the highway agencies to evaluate and compare their current or possible future highway design policies.

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