Safety Performance Functions for Rural Two-Lane County Road Segments

Stapleton, Steven; Ingle, Anthony; Chakraborty, Meghna; Gates, Timothy J.; Savolainen, Peter T.

doi:10.1177/0361198118799035

Cited by 16 publications

(14 citation statements)

References 10 publications

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“…The results of the analysis of state highway segments revealed several interesting findings. First, the relationship between the number of traffic crashes and AADT is non-linear and slightly inelastic, with parameter estimates ranging between 0.84 and 0.89 depending on severity, a finding consistent with other studies ( 30 , 31 ). With regard to the safety effects of driveway density across the various driveway land use type categories on state road segments, the results show that densities for all driveway types are positively associated with crash frequency (i.e., greater driveway densities result in higher crash occurrence), and this association is statistically significant for each driveway type.…”

Section: Resultssupporting

confidence: 89%

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Association between Driveway Land Use and Safety Performance on Rural Highways

Chakraborty

Gates

2020

Transportation Research Record: Journal of the Transportation R

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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.

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Section: Resultssupporting

confidence: 89%

“…The residential and commercial driveway densities exhibited a stronger effect on FI crashes than on total and PDO crashes. Overall, these results are aligned with prior research that found crash frequency to be influenced by various driveway configurations and their land uses ( 6 , 30 , 32 ).…”

Section: Resultssupporting

confidence: 88%

Association between Driveway Land Use and Safety Performance on Rural Highways

Chakraborty

Gates

2020

Transportation Research Record: Journal of the Transportation R

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“…Furthermore, county roadways account for approximately 57% of all rural traffic crashes and 62% of all deer crashes in Michigan ( 3 ). Previous research has found SPFs developed for county-owned roads have a different model shape than SPFs developed for state-owned roads ( 4 – 6 ). In rural portions of Michigan, deer crashes account for approximately 1/3 of all crashes on unpaved roads and 2/3 of all crashes on paved two-lane roads, each of which greatly exceeds the deer crash proportion (0.121) for the data from Washington state that were used to develop the HSM’s rural two-lane/two-way model ( 3 ).…”

mentioning

confidence: 92%

“…Collectively, these issues have significant implications on the transferability of the HSM rural segment models and associated CMFs for use in Michigan, even if calibration is performed. Furthermore, while Michigan-specific SPFs and CMFs have been developed for rural county and state highways, DVCs were categorically excluded from these models to improve the prediction capabilities of the roadway-related factors ( 4 , 5 ). Thus, there is a clear need for further research on the impacts that roadway characteristics have on deer crash occurrence across the primary classes of rural roadways, including county roadways and unpaved roadways.…”

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confidence: 99%

Factors Contributing to Deer–Vehicle Crashes on Rural Two-Lane Roadways in Michigan

Stapleton

Ingle

Gates

2019

Transportation Research Record: Journal of the Transportation R

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Deer–vehicle crashes (DVCs) continue to be a problem in the United States, with 1.2 million such crashes occurring annually. DVCs are a particular issue on two-lane rural highways in Michigan, accounting for more than 60% of crashes. Such a high proportion of DVCs limits the transferability of existing safety models, including those found in the Highway Safety Manual (HSM), that are often based on data from states with considerably lower proportions of deer crashes. To counter this, a cross-sectional analysis of deer crashes was performed using data from Michigan. The data were analyzed across four categories of rural two-lane roadways, including: state highways, federal aid county roadways, non-federal aid county roadways, and unpaved (gravel) county roadways. Mixed effects negative binomial regression models utilizing spatial and temporal random effects were generated separately for each of the rural two-lane roadway types. Results showed speed-related factors, including lane width, shoulder width, horizontal curvature, and peak level of service, had a significant effect on DVC occurrence for most types of rural two-lane roadways in Michigan. Wider lanes were associated with a greater occurrence of deer crashes, perhaps because of higher prevailing travel speeds. Conversely, horizontal curves with design speeds lower than the statutory speed limit were associated with fewer deer crashes, perhaps because of lower travel speeds through curves. Wider shoulders, which afford greater separation between the travel lanes and the roadside, were found to have significantly lower deer crash occurrence. The number of available hunting licenses did not have a consistent effect on DVCs.

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“…With the availability of such large-scale data, this study applies an analytical framework that helps to understand how different factors impacted the daily trips of the U.S. population during the COVID-19 pandemic utilizing regularization regression techniques. Regression analysis is widely used in data analysis and machine learning (11), and is applied extensively in various applications, including transportation (12,13). Among the various regression algorithms, linear regression is computationally one of the most efficient techniques and is often used as a starting point for many different problems, although in many cases the linear regression model may be suboptimal.…”

Section: Introductionmentioning

confidence: 99%

Linear Regularization-based Analysis and Prediction of Human Mobility in the U.S. during the COVID-19 Pandemic

Chakraborty¹,

Mahmud²,

Gates³

et al. 2020

Preprint

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Since the increasing spread of COVID-19 in the U.S., with currently the highest number of confirmed cases and deaths in the world, most states in the nation have enforced travel restrictions resulting in drastic reductions in mobility and travel. However, the overall impact and long-term implications of this crisis to mobility still remain uncertain. To this end, this study develops an analytical framework that determines the most significant factors impacting human mobility and travel in the U.S. during the pandemic. In particular, we use Least Absolute Shrinkage and Selection Operator (LASSO) to identify the significant variables influencing human mobility and utilize linear regularization algorithms, including Ridge, LASSO, and Elastic Net modeling techniques to model and predict human mobility and travel. State-level data were obtained from various open-access sources for the period from January 1, 2020 to June 13, 2020. The entire data set was divided into a training data-set and a test data-set and the variables selected by LASSO were used to train four different models by ordinary linear regression, Ridge regression, LASSO and Elastic Net regression algorithms, using the training data-set. Finally, the prediction accuracy of the developed models was examined on the test data. The results indicate that among all models, the Ridge regression provides the most superior performance with the least error, while both LASSO and Elastic Net performed better than the ordinary linear model.

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Safety Performance Functions for Rural Two-Lane County Road Segments

Cited by 16 publications

References 10 publications

Association between Driveway Land Use and Safety Performance on Rural Highways

Association between Driveway Land Use and Safety Performance on Rural Highways

Factors Contributing to Deer–Vehicle Crashes on Rural Two-Lane Roadways in Michigan

Linear Regularization-based Analysis and Prediction of Human Mobility in the U.S. during the COVID-19 Pandemic

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