scholarly journals Analysis of moose-vehicle collisions countermeasures in northern climates

2021 ◽  
Author(s):  
Osama Abaza ◽  
Colleen C Moran
Keyword(s):  
Regression Analysis ◽  
Environmental Conditions ◽  
Snow Depth ◽  
Department Of Transportation ◽  
Significant Drop ◽  
Vehicle Collisions ◽  
Public Facilities ◽  
Maximum Snow Depth ◽  
Two Measures ◽  
Moose Population

Abstract This research investigates the reliability of two measures intended to reduce moose-vehicle collisions (MVCs): continuous lighting and clearing/grubbing of roadway corridors. Individual analyses and a combined regression analysis were conducted to measure the effects of several combinations of variables on MVC rates, including clearing and grubbing, continuous lighting, clearing without grubbing, moose population, precipitation, snowfall, and maximum snow depth. Nine corridor improvement projects were analyzed based on the variables present. In previous studies, it has been hypothesized that MVC rates are influenced by environmental conditions such as snowfall and daylight. The Alaska Department of Transportation and Public Facilities (DOT&PF) has performed many studies on MVCs along several corridors. Some corridors showed a significant drop in the number of MVCs after the installation of continuous lighting. The results show there is a consistent drop in MVCs after clearing and grubbing, with the exception of one corridor. The combined clearing/grubbing and continuous lighting projects also resulted in a consistent drop in MVCs. The projects with clearing and grubbing as a component had varying trends in MVCs, which may indicate that DOT&PF Maintenance and Operations performed clearing of re-vegetated areas, or that older growth is less of an attractant for moose.

Analysis of Overweight Truck Permit Policy in New Jersey

2021 ◽  
pp. 036119812110266
Author(s):  
Sami Demiroluk ◽  
Hani Nassif ◽  
Kaan Ozbay ◽  
Chaekuk Na
Keyword(s):  
New Jersey ◽  
Environmental Conditions ◽  
Decision Makers ◽  
Actual Cost ◽  
Department Of Transportation ◽  
Heavy Weight ◽  
Vehicle Route ◽  
Heavy Trucks ◽  
Maintenance And Rehabilitation ◽  
The Cost

The roadway infrastructure constantly deteriorates because of environmental conditions, but other factors such as exposure to heavy trucks exacerbates the rate of deterioration. Therefore, decision-makers are constantly searching for ways to optimize allocation of the limited funds for repair, maintenance, and rehabilitation of New Jersey’s infrastructure. New Jersey legislation requires operators of overweight (OW) trucks to obtain a permit to use the infrastructure. The New Jersey Department of Transportation (NJDOT) issues a variety of permits based on the types of goods carried. These permits allow OW trucks to use the infrastructure either for a single trip or for multiple trips. Therefore, one major concern is whether the permit revenue of the agency can recoup the actual cost of damage to the infrastructure caused by these OW trucks. This study investigates whether NJDOT’s current permit fee program can collect enough revenue to meet the actual cost of damage to the infrastructure caused by these heavy-weight permit trucks. The infrastructure damage is estimated by using pavement and bridge deterioration models and New Jersey permit data from 2013 to 2018 containing vehicle configuration and vehicle route. The analysis indicates that although the cost of infrastructure damage can be recovered for certain permit types, there is room for improvement in the permit program. Moreover, based on permit rules in other states, the overall rank of the New Jersey permit program is evaluated and possible revisions are recommended for future permit policies.

Poster 461 Department of Transportation Versus Self-reported Data on Motor Vehicle Collisions and Driving Convictions for Stroke Survivors: Do They Agree?

PM&R ◽  
2011 ◽  
Vol 3 ◽  
pp. S336-S336
Author(s):  
Hillel M. Finestone ◽  
Jennifer Biggs ◽  
Linda Greene-Finestone ◽  
Meiqi Guo ◽  
Lynn Hunt ◽  
...  
Keyword(s):  
Motor Vehicle ◽  
Stroke Survivors ◽  
Department Of Transportation ◽  
Motor Vehicle Collisions ◽  
Vehicle Collisions ◽  
Reported Data

Variation of snow, winter precipitation and winter air temperature during the last century at Nagaoka, Japan

1996 ◽  
Vol 42 (140) ◽  
pp. 136-140 ◽  
Author(s):  
Tsutomu Nakamura ◽  
Masujiro Shimizu
Keyword(s):  
Air Temperature ◽  
Snow Depth ◽  
Moving Average ◽  
Winter Precipitation ◽  
Statistical Correlation ◽  
Past Century ◽  
Temperature Deviation ◽  
Linear Rate ◽  
The Past ◽  
Maximum Snow Depth

AbstractReduced amounts of snow in the eight winters from 1986-87 to 1993-94 at Nagaoka, Japan, seem to be due to a winter air-temprature rise. The winter air temprature has shown cyclic varition gradual increase in the past 100years. The linear rate of the temperature rise in the past century was calculated as 1.35°C per 100 years. Both the maximum Snow depth and winter precipitation showed an inversely positive correlation with winter mean air temperature, The square of the statistical correlation coefficient r2was calculated as 0.321 and 0.107. respectively. Statistically smoothed curves or the maximum snow depth and winter precipitation showed maximum values in 1940, Fluctuations in deviation of the maximum Snow depth showed smaller values than in precipitation. The minimum winter mean air temperature obtained from a 10 year moving average curve was found in 1942, and the deviation fom the climatic mean changed from negative to positive in 1949. The change in sign or the temperature deviation and the increase of the deviation may be attributable to global warming.

scholarly journals Wind Tunnel Tests of Wind-Induced Snow Distribution for Cubes with Holes

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Xintong Jiang ◽  
Zhixiang Yin ◽  
Hanbo Cui
Keyword(s):  
Wind Tunnel ◽  
Snow Depth ◽  
Structural Safety ◽  
Windward Side ◽  
Leeward Side ◽  
Wind Tunnel Tests ◽  
Maximum Snow Depth ◽  
Adjacent Structures ◽  
Significant Difference ◽  
Snow Distribution

The nonuniform distribution of snow around structures with holes is extremely unfavorable for structural safety, and the mechanism of wind-snow interaction between adjacent structures with holes needs to be explored. Therefore, a wind tunnel simulation was performed, in which quartz particles with an average particle size of 0.14 mm as snow particles were used, and cubes with dimensions of 100 mm × 100 mm × 100 mm each containing a hole with the size of 20 mm × 20 mm were employed as structures. Firstly, the quality of a small low-speed wind tunnel flow field was tested, and then the effects of hole orientation (hole located on the windward side, leeward side, and other vertical sides) and absence of holes on the surface of a single cube were studied. Furthermore, the effects of different hole locations (respectant position, opposite position, and dislocation) and relative spacing (50 mm, 100 mm, and 150 mm) on the surfaces of two cubes and the snow distribution around them were investigated. It was concluded that the presence and location of hole had a great influence on snow distribution around cubes. Snow distribution was favorable when hole was located on the other vertical sides of the test specimen. The most unfavorable snow distribution was obtained when the holes on the two-holed sides of the cubes were respectant with a maximum snow depth coefficient of 1.4. A significant difference was observed in the snow depths of two sides of cubes when holes were dislocated. When two holes were respectant, surrounding snow depth was decreased, and the maximum snow depth on model surface area was increased with the increase of spacing. Wind tunnel tests on holed cubes provided a reference for the prediction of snow load distribution of typical structures with holes.

Poisson Regression Analysis of Highway Fatality Accident Data in Oklahoma

2014 ◽  
Vol 5 (4) ◽  
pp. 72-86
Author(s):  
Jonathan C. Comer ◽  
Nicholas J. Rose ◽  
Leonard S. Bombom
Keyword(s):  
Regression Analysis ◽  
Rural Areas ◽  
Poisson Regression ◽  
Department Of Transportation ◽  
Highway Design ◽  
Crash Data ◽  
Poisson Regression Analysis ◽  
Accident Data ◽  
Oklahoma Department Of Transportation ◽  
Construction Practice

Analysis of fatality automobile accident data can be challenging in rural areas where a relatively small number of such accidents occurs on specific sections of highways. Combining crash data from the Fatality Analysis Reporting System (FARS) and highway networks and design specifications from the Oklahoma Department of Transportation (ODOT), this article employs Poisson regression analysis to determine what roadway characteristics (e.g. grade, geometry, and design) are most associated with fatal accidents on predominantly rural segments of highways in Oklahoma. The results provide information about what combinations of highway design traits have contributed most to past crashes and therefore can identify potentially dangerous road segments system-wide. This information will help transportation engineers evaluate current construction practice and seek ways to address design issues that are shown to contribute significantly to serious crashes.

scholarly journals Effect of Temperature-Humidity-Index on Milk Performances of Local Born Holstein Dairy Cows Under Saharan Climate

2021 ◽  
Vol 24 (2) ◽  
pp. 24-36
Author(s):  
Lazoumi Ouarfli ◽  
Abdelmadjid Chehma
Keyword(s):  
Heat Stress ◽  
Dairy Cows ◽  
Environmental Conditions ◽  
Arid Regions ◽  
Effect Of Temperature ◽  
Significant Drop ◽  
Temperature Humidity Index ◽  
Lactation Length ◽  
Humidity Index ◽  
Milking Frequency

Abstract The objective is to study the effect of heat stress on milk yield (MY) relative to milking records (n=18178) of native Holsteins (n=187), in the region of Ghardaia, according to periods of HS, using the temperature-humidity index (THI). With THI >72 during 07 months in the study area, which significantly (P<0.001) decrease the MY (-15.5% corresponding to 21.73 kg). Also, calving periods led to a significant drop (P < 0.001) in overall MY (7030.35 kg) of the order of (-14.6%), and over the lactation length (353.43 d), which explains 41% of the variations in MY. In addition, the non-significant effect (P=0.212) of the lactation range on the increase in MY, moreover, the lactation length shows a non-significant (P = 0.108) decrease (-4.68%) during heat stress (HS). Furthermore, the significant effect (P <0.001) of the interaction (Milking frequency × THI) on MY, when THI variates from < 74 to > 84, with regression of (-16.82% and -08.82%) of the MF (2X and 3X), respectively. Again, the NH cow is less sensitive to hyperthermia, so THI explains only 2% of the variation in MY levels. Thus, NH in arid regions have the ability to acclimatize to Saharan environmental conditions.

scholarly journals Local-scale uncertainty of seasonal mean and extreme values of in-situ snow depth and snow fall measurements

2021 ◽  
Author(s):  
Moritz Buchmann ◽  
Michael Begert ◽  
Stefan Brönnimann ◽  
Christoph Marty
Keyword(s):  
Extreme Events ◽  
Snow Depth ◽  
Extreme Values ◽  
Local Scale ◽  
Swiss Alps ◽  
Scale Uncertainty ◽  
Return Levels ◽  
Maximum Snow Depth ◽  
Climate Indicators ◽  
The Impact

Abstract. Measurements of snow depth and snowfall on the daily scale can vary strongly over short distances. However, it is not clear if there is a seasonal dependence in these variations and how they impact common snow climate indicators based on mean values, as well as estimated return levels of extreme events based on maximum values. To analyse the impacts of local-scale variations we compiled a unique set of parallel snow measurements from the Swiss Alps consisting of 30 station pairs with up to 77 years of parallel data. Station pairs are mostly located in the same villages (or within 3 km horizontal and 150 m vertical distances). Investigated snow climate indicators include average snow depth, maximum snow depth, sum of new snow, days with snow on the ground, days with snowfall as well as snow onset and disappearance dates, which are calculated for various seasons (December to February (DFJ), November to April (NDJFMA), and March to April (MA)). We computed relative and absolute error metrics for all these indicators at each station pair to demonstrate the potential uncertainty. We found the largest relative inter-pair differences for all indicators in spring (MA) and the smallest in DJF. Furthermore, there is hardly any difference between DJF and NDJFMA which show median uncertainties of less than 5 % for all indicators. Local-scale uncertainty ranges between less than 24 % (DJF) and less than 43 % (MA) for all indicators and 75 % of all station pairs. Highest (lowest) percentage of station pairs with uncertainty less than 15 % is observed for days with snow on the ground with 90 % (average snow depth, 30 %). Median differences of snow disappearance dates are rather small (three days) and similar to the ones found for snow onset dates (two days). An analysis of potential sunshine duration could not explain the higher uncertainties in spring. To analyse the impact of local-scale variations on the estimation of extreme events, 50-year return levels were quantified for maximum snow depth and maximum 3-day new snow sum, which are often used for prevention measures. The found return levels are within each other’s 95 % confidence intervals for all (but two) station pairs, revealing no striking differences. The findings serve as an important basis for our understanding of uncertainties of commonly used snow indicators and extremal indices. Knowledge about such uncertainties in combination with break-detection methods is the groundwork in view of any homogenization efforts regarding snow time series.

scholarly journals A Study on the Prediction of Daily Snowmelt Depth using Multiple Linear Regression

2020 ◽  
Vol 20 (6) ◽  
pp. 311-321
Author(s):  
YeoungRok Oh ◽  
Gyumin Lee ◽  
Kyung Soo Jun ◽  
Wooyeon Sunwoo ◽  
SeungWoo Baek ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Regression Model ◽  
Multiple Regression Analysis ◽  
Multiple Regression ◽  
Snow Depth ◽  
Meteorological Data ◽  
Multiple Regression Model ◽  
Independent Variables ◽  
Fresh Snow ◽  
Humidity Range

In this study, daily snowmelt was predicted using observed meteorological data and multiple regression analysis. Five observation stations (located in Daegwallyeong, Gwangju, Seosan, Mokpo, and Jeonju) were selected to analyze fresh snow depth from 2000 to 2010. The dependent variable used in the multiple regression analysis was daily snowmelt depth, and the independent variables were fresh snow depth, diurnal temperature range, temperature interception, diurnal humidity range, humidity intercept, and solar radiation. Seventy percent of the total observed data was used to develop a multiple regression model and the regression model was verified using the 30% of remaining data. The adjusted R-squared and Root Mean Square Deviation (RMSE) were used to examine the developed regression model. As a result, the adjusted R-squared was higher than 0.769 (except Daegwallyeong); thus the developed model represented well the daily snowmelt depth. Even Jeonju had an adjusted R-squared of 0.869. Also, the RMSE in all of the five stations was lower than 2.5 cm. The lowest value in Seosan was 1.7 cm. From the two types of verification, the developed multiple regression model was judged to be suitable to predict the daily snowmelt depth. However, multicollinearity should be explained, as rapid increases in temperature and sustained high temperature could not be reflected in the model. Therefore, if the limitations were resolved in further research, the model could be used to predict the amount of daily snowmelt depth more reliably.

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