Association between Driveway Land Use and Safety Performance on Rural Highways

2020 ◽  
pp. 036119812096523
Author(s):  
Meghna Chakraborty ◽  
Timothy J. Gates
Keyword(s):  
Land Use ◽  
Traffic Safety ◽  
Negative Binomial ◽  
Unobserved Heterogeneity ◽  
Transportation Network ◽  
Negative Binomial Regression ◽  
Safety Performance ◽  
Land Utilization ◽  
Design Standards ◽  
Rural Highways

Rural roads are a critical component of the transportation network in the U.S., including Michigan, where county roads comprise of a majority of the state’s roadway mileage. The rates of fatal crashes on rural highways are substantially higher than that on urban roads. Previous research has investigated the safety impacts of driveway density, but the effects of driveway land use on rural roadway safety performance, particularly for county roadways, remains under-researched. This study analyzed the safety impacts of various classifications of driveway land utilization on rural two-lane state and county roads. Non-animal segment crashes from 2011 to 2015 were analyzed along with roadway data for over 5,556 mi of state highways and 5,890 mi of paved county segments from across Michigan. To account for the unobserved heterogeneity associated with varied county design standards and site characteristics, mixed-effects negative binomial regression with county- and site-specific random effects was utilized. Separate models were developed for state highways and paved county roads. The results indicated that commercial driveways possess a stronger effect on crash occurrence than other driveway land use types, including residential and industrial driveways. The effect of driveway density on crash frequency was also found to be stronger on state highways compared with the county roads. This study contributes to the limited body of knowledge in relation to the relationship between traffic safety and driveway land use for rural roadway segments, particularly for county roads, which typically possess design and travel characteristics that are considerably different from those of state highways.

Safety Performance Functions for Rural Two-Lane County Road Segments

2018 ◽  
Vol 2672 (52) ◽  
pp. 226-237 ◽  
Author(s):  
Steven Y. Stapleton ◽  
Anthony J. Ingle ◽  
Meghna Chakraborty ◽  
Timothy J. Gates ◽  
Peter T. Savolainen
Keyword(s):  
Negative Binomial ◽  
Unobserved Heterogeneity ◽  
Strong Association ◽  
Random Effect ◽  
Safety Performance ◽  
Federal Aid ◽  
Rural County ◽  
Design Standards ◽  
Safety Performance Functions ◽  
Lane County

Safety performance functions (SPFs) were developed for rural two-lane county roadway segments in Michigan. Five years of crash data (2011 to 2015) were analyzed for greater than 6,500 mi of rural county roadways, covering 29 of Michigan’s 83 counties and representing all regions of the state. Three separate models were developed to estimate annual deer-excluded total and injury crashes on rural county roadways: 1) paved federal-aid segments, 2) paved non-federal-aid segments, and 3) paved and gravel non-federal-aid segments with fewer than 400 vpd. To account for the unobserved heterogeneity associated with differing county design standards, mixed effects negative binomial models with a county-specific random effect were utilized. Not surprisingly, the county segment SPFs generally differed from traditional models generated using data from state-maintained roadways. County federal-aid roadways general showed greater crash occurrence than county non-federal-aid roadways, the Highway Safety Manual (HSM) two-lane rural roadways model, and rural state highways in Michigan. County non-federal-aid paved roadways showed crash occurrence rates that were remarkably similar to the HSM base rural two-lane roadway model, whereas gravel roadways showed greater crash occurrence rates. The presence of horizontal curves with design speeds below 55 mph had a strong association with the occurrence of total and injury crashes across all county road classes. Increasing driveway density was also found to be associated with increased crash occurrence. However, lane width, roadway surface width, and paved shoulder width had little to no impact on total or injury crashes.

Safety Performance Functions for Low-Volume Rural Stop-Controlled Intersections

2019 ◽  
Vol 2673 (5) ◽  
pp. 660-669 ◽  
Author(s):  
Steven Y. Stapleton ◽  
Timothy J. Gates ◽  
Raul Avelar ◽  
Srinivas R. Geedipally ◽  
Ramin Saedi
Keyword(s):  
Negative Binomial ◽  
Unobserved Heterogeneity ◽  
Random Effect ◽  
Negative Binomial Regression ◽  
Safety Performance ◽  
Cross Sectional ◽  
Minor Road ◽  
Safety Performance Functions ◽  
Traffic Volumes ◽  
Low Volume

This study involved the development of safety performance functions for rural, low-volume, minor road stop-controlled intersections in Michigan. Facility types included three-leg stop-controlled (3ST) and four-leg stop-controlled (4ST) intersections under state or county jurisdiction and were sampled from each of Michigan’s 83 counties. To isolate lower-volume rural intersections, major roadway traffic volumes were limited to the range of 400–2,000 vehicles per day (vpd). Data were compiled from several sources for 2,023 intersections statewide. These data included traffic crashes, volumes, roadway classification, geometry, cross-sectional features, and other site characteristics covering the period of 2011–2015. Random effects negative binomial regression models were specified for each stop-controlled intersection type considering factors such as driveway density, lighting presence, turn lane presence, and intersection skew, in addition to volume. To account for the unobserved heterogeneity between counties, mixed effects negative binomial models with a county-specific random effect were utilized. Furthermore, unobserved temporal effects were controlled through the use of a year-specific random effect. Separate models were developed for fatal/injury crashes, property damage crashes, and select target crash types. The analysis found that skew angles of greater than five degrees led to significantly greater crash occurrence for both 3ST and 4ST intersections, while greater than two driveways near the intersection led to significantly greater angle crashes at 4ST intersections. Other factors were found to have little impact on crash occurrence. Comparison with the Highway Safety Manual (HSM) base models showed that the HSM models over-predict crashes on 4ST intersections and 3ST intersections with volumes between 1,200 and 2,000 vpd.

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.

scholarly journals Prediction of motorcyclist traffic crashes in Cartagena (Colombia): development of a safety performance function

2021 ◽  
Author(s):  
Holman Ospina-Mateus ◽  
Leonardo Augusto Quintana Jiménez ◽  
Francisco J. Lopez-Valdes ◽  
Shib Sankar Sana
Keyword(s):  
Road Safety ◽  
Traffic Accidents ◽  
Negative Binomial ◽  
Road Traffic ◽  
Negative Binomial Regression ◽  
Safety Performance ◽  
Traffic Crashes ◽  
Performance Function ◽  
Safety Performance Function ◽  
Significant Factors

Motorcyclists account for more than 380,000 deaths annually worldwide from road traffic accidents. Motorcyclists are the most vulnerable road users worldwide to road safety (28% of global fatalities), together with cyclists and pedestrians. Approximately 80% of deaths are from low- or middle-income countries. Colombia has a rate of 9.7 deaths per 100,000 inhabitants, which places it 10th in the world. Motorcycles in Colombia correspond to 57% of the fleet and generate an average of 51% of fatalities per year. This study aims to identify significant factors of the environment, traffic volume, and infrastructure to predict the number of accidents per year focused only on motorcyclists. The prediction model used a negative binomial regression for the definition of a Safety Performance Function (SPF) for motorcyclists. In the second stage, Bayes' empirical approach is implemented to identify motorcycle crash-prone road sections. The study is applied in Cartagena, one of the capital cities with more traffic crashes and motorcyclists dedicated to informal transportation (motorcycle taxi riders) in Colombia. The data of 2,884 motorcycle crashes between 2016 and 2017 are analyzed. The proposed model identifies that crashes of motorcyclists per kilometer have significant factors such as the average volume of daily motorcyclist traffic, the number of accesses (intersections) per kilometer, commercial areas, and the type of road and it identifies 55 critical accident-prone sections. The research evidences coherent and consistent results with previous studies and requires effective countermeasures for the benefit of road safety for motorcyclists.

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.

Examining the Safety Performance and Injury Severity Characteristics of Rural County Roadways

2019 ◽  
Vol 2673 (10) ◽  
pp. 405-415 ◽  
Author(s):  
Srinivas R. Geedipally ◽  
Timothy J. Gates ◽  
Steven Stapleton ◽  
Anthony Ingle ◽  
Raul E. Avelar
Keyword(s):  
Injury Severity ◽  
Negative Binomial ◽  
Random Effect ◽  
Multinomial Logit Model ◽  
Distribution Functions ◽  
Safety Performance ◽  
Occurrence Rate ◽  
Rural County ◽  
Rural Highways ◽  
Traffic Volumes

Much of the earlier work on rural safety focused on state-maintained roadways and little is known about the safety performance of low-volume county-maintained roads. This study involved the estimation of safety performance for rural county roadways (paved and gravel). This was accomplished through the development of safety performance functions (SPFs) to estimate the number of annual crashes at a given highway segment, crash modification factors to determine the impacts associated with various roadway and geometric characteristics, and severity distribution functions (SDFs) to predict the crash severity. County road segment data were collected across a sample of 30 counties representing all regions of Michigan. Because of the overwhelming proportion of deer crashes, only non-deer-related crashes were considered. To minimize the influence of variability among counties, the random effect negative binomial model was used to develop SPFs. In addition, a multinomial logit model was used to develop SDFs. Paved county roadways showed approximately double the crash occurrence rate of typical state-maintained two-lane rural highways, and gravel roadways showed a substantially greater crash occurrence rate than paved county roadways across the equivalent range of traffic volumes. The economic analysis showed that it is beneficial to pave a gravel road when the traffic volume is greater than 600 vehicles per day. The random effect variable is significant in all the calibrated models, which shows that there is a considerable variability among counties that cannot be captured with the available variables. Not considering the random effects will result in biased estimation of crashes.

scholarly journals Modelling the Safety Performance of Roadways for Specific Collision Types

2021 ◽  
Author(s):  
Tony Chiu
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Constant Factor ◽  
Safety Performance ◽  
Annual Average Daily Traffic ◽  
Collision Type ◽  
Binomial Regression ◽  
Safety Performance Functions ◽  
The Individual ◽  
Screening Approaches

Safety performance functions (SPFs) are rarely developed for specific accident types because this can be very lengthy especially when relevant data are unavailable. Because of this constraint, a factor is applied along with the SPF for all accident types to estimate the safety for specific accidents types. This factor is the proportion of the individual collision type in the entire population of all accidents. However, there is no reason to believe that this factor is a constant which is independent of Annual Average Daily Traffic (AADT). Accordingly, a constant factor and the proportion model are applied to the SPF for all accident types combined to estimate the SPF for specific accident types on both rural road segments and Two-Way Stop-Controlled (TWSC) intersections. The validity of these factors are tested using the state-of-the-art network screening approaches. Furthermore, a detailed investigation on Property Damaage Only (PDO) estimates is carried out on certain aspects of safety performance functions, using negative binomial regression. PDO estimates are then evaluated based on three different approaches.

scholarly journals Modelling the Safety Performance of Roadways for Specific Collision Types

2021 ◽  
Author(s):  
Tony Chiu
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Constant Factor ◽  
Safety Performance ◽  
Annual Average Daily Traffic ◽  
Collision Type ◽  
Binomial Regression ◽  
Safety Performance Functions ◽  
The Individual ◽  
Screening Approaches

Safety performance functions (SPFs) are rarely developed for specific accident types because this can be very lengthy especially when relevant data are unavailable. Because of this constraint, a factor is applied along with the SPF for all accident types to estimate the safety for specific accidents types. This factor is the proportion of the individual collision type in the entire population of all accidents. However, there is no reason to believe that this factor is a constant which is independent of Annual Average Daily Traffic (AADT). Accordingly, a constant factor and the proportion model are applied to the SPF for all accident types combined to estimate the SPF for specific accident types on both rural road segments and Two-Way Stop-Controlled (TWSC) intersections. The validity of these factors are tested using the state-of-the-art network screening approaches. Furthermore, a detailed investigation on Property Damaage Only (PDO) estimates is carried out on certain aspects of safety performance functions, using negative binomial regression. PDO estimates are then evaluated based on three different approaches.

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