scholarly journals Collective Risk Ranking of Highway Segments on the Basis of Severity-Weighted Crash Rates

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Dawei Li ◽  
Mustafa F. M. Al-Mahamda
Keyword(s):  
Empirical Bayes ◽  
Safety Information ◽  
Crash Severity ◽  
Regression To The Mean ◽  
Factors Affecting ◽  
Crash Data ◽  
Crash Rates ◽  
Risk Ranking ◽  
Collective Risk ◽  
Major Factors

This study is intended to focus on the major factors affecting traffic crash rates and severity levels, in addition to identifying crash-prone locations (i.e., black spots) based on the two indicators. The available crash data for different road segments used for the analysis were obtained from the Washington state database provided by the Highway Safety Information System (HSIS) for the years 2006 to 2011. A Random Forest (RF) classifier was used to predict the outcome level of crash severity, while crash rates were predicted by applying RF regressor. Certain features were selected for each model besides the abstraction of new features to check if there are unobserved correlations affecting the independent variables, such as accounting for the number and weight of crashes within 1 km2 area by implementing the Getis-Ord Gi∗ index. Moreover, to calculate the collective risk (CR) score, crash rates were adjusted to incorporate crash severity weights (cost per severity type) and regression-to-the-mean (RTM) bias via Empirical Bayes (EB) method. Finally, segments were ranked according to their CR score.

Effects of Rural Highway Median Treatments and Access

2005 ◽  
Vol 1931 (1) ◽  
pp. 99-107 ◽  
Author(s):  
J. L. Gattis ◽  
Ramasubramaniyan Balakumar ◽  
Lynette K. Duncan
Keyword(s):  
Land Use ◽  
Crash Severity ◽  
Crash Frequency ◽  
Crash Data ◽  
Crash Rates ◽  
Access Frequency ◽  
Stop Sign ◽  
Median Volume ◽  
Rural Highway ◽  
Median Width

The safety records of rural and suburban four-lane highways in Arkansas as a function of median treatment and access density were examined. The study excluded roadways with posted speeds lower than 64 km/h (40 mph) and excluded fully controlled access roadways. When entering an urban area, the segments were normally terminated when the first traffic signal or stop sign was encountered. By using 3 years of crash data, the analyses revealed a number of relationships relating crash frequency to median, volume, and access frequency attributes. Crash severity and crash type were also examined. As median width increased, there was a weak but statistically significant decline in the crash rate. There was a weak but statistically significant increase in the crash rate as access density increased. The roadways with shoulders and depressed medians had the lowest crash rates, and the roadways with no median (i.e., painted centerline) and curbs had the worst safety record. An inspection of these data suggests that there may be a correlation between median type and land use type: certain types of median are more likely to be present in certain land use environments. This raises the possibility that in this and in other studies of the safety effects of median treatments, the findings may be influenced or skewed by correlations between median type and land use or surroundings or by other factors.

scholarly journals Safety evaluation of roundabouts at freeway ramp terminals and HSM calibration

2017 ◽  
Author(s):  
◽  
Jacob A. Berry
Keyword(s):  
Empirical Bayes ◽  
Safety Evaluation ◽  
Crash Severity ◽  
Site Specific ◽  
Crash Data ◽  
Highway Safety Manual ◽  
Time Period ◽  
Using Data ◽  
Crash Types ◽  
Project Level

Although the conventional understanding is that roundabouts have the potential to improve safety at intersections, particularly by reducing more severe crash types, there is limited knowledge on their safety impacts when utilized as part of an interchange system. This research utilizes multiple years of crash, traffic, and geometric data across a variety of roundabout interchange sites in Missouri to develop a two-level Empirical Bayes (EB) safety analysis at the project (entire interchange) and site-specific (ramp terminal only) levels. The results were then aggregated for both single-lane and dual-lane roundabout terminals. The project-level results for single-lane sites showed a decrease in fatal/injury crashes (FI) by 20.3 percent, property damage only (PDO) crashes by 26.0 percent and total crashes by 24.5 percent. The site-specific results for single-lane roundabout terminals indicated a reduction of FI crashes by 32.8 percent, PDO crashes by 23.1 percent, and total crashes by 24.4 percent. At the project-level, dual-lane roundabouts were shown to reduce FI crashes by 2.3 percent while increasing PDO and total crashes by 16.9 percent and 12.2 percent, respectively. The dual-lane site-specific results indicated an increase in FI crashes by 34.7 percent, PDO crashes by 53.9 percent, and total crashes by 50.0 percent at the ramp terminal specifically. This analysis directly compares observed crash data after roundabout implementation to the expected number of crashes over that time period if the roundabout were never constructed. Therefore, this indicates that single-lane roundabouts performed significantly better than their original configurations, while the dual-lane roundabout terminals were shown to be less-suitable for certain applications. Additionally, it was found that a high proportion of the roundabout crashes occurred at the exit ramp approach legs; this was illustrated in the collision diagrams and crash type distributions that were produced for the common observed crashes at both single and dual-lane roundabout terminals. As a separate part of this thesis research, an improved calibration of the Highway Safety Manual safety performance functions of four facility types was performed using data from 2012-2014 to calibrate for local Missouri conditions. Crash severity and crash type distributions were also developed for each facility type.

scholarly journals Negative Binomial Regression Model for Road Crash Severity Prediction

2018 ◽  
Vol 12 (4) ◽  
pp. 38 ◽  
Author(s):  
Hana Naghawi
Keyword(s):  
Prediction Model ◽  
Negative Binomial ◽  
Descriptive Analysis ◽  
Negative Binomial Regression ◽  
Negative Binomial Regression Model ◽  
Crash Severity ◽  
Contributing Factors ◽  
Factors Affecting ◽  
Crash Data ◽  
Severity Prediction

In this paper, the Negative Binominal Regression (NBR) technique was used to develop crash severity prediction model in Jordan. The primary crash data needed were obtained from Jordan Traffic Institute for the year 2014. The collected data included number and severity of crashes. The data were organized into eight crash contributing factors including: age, age and gender, drivers’ faults, environmental factors, crash time, roadway defects and vehicle defects. First of all, descriptive analysis of the crash contributing factors was done to identify and quantify factors affecting crash severity, then the NBR technique using R-statistic software was used for the development of the crash prediction model that linked crash severities to the identified factors. The NBR model results indicated that severe crashes decreased significantly as the age of both male and female drivers increased. They significantly decreased as the environmental conditions improved. In addition, sever crashes were significantly higher during weekdays than weekends and in the morning than in the evening. The results also indicated that sever crashes significantly increased as drivers have faults while driving. In addition, mirror and brake deficits were found to be the only factors among all possible vehicle deficits factors that contributed significantly to severe crashes. Finally, it was found that the results of the NBR model are in agreement with the descriptive analysis of the crash contributing factors.

scholarly journals Analysis of Factors Affecting Real-Time Ridesharing Vehicle Crash Severity

2019 ◽  
Vol 11 (12) ◽  
pp. 3334 ◽  
Author(s):  
Bei Zhou ◽  
Xinfen Zhang ◽  
Shengrui Zhang ◽  
Zongzhi Li ◽  
Xin Liu
Keyword(s):  
Real Time ◽  
Classification Tree ◽  
Weather Condition ◽  
Crash Severity ◽  
Operating Characteristics ◽  
Lighting Condition ◽  
Factors Affecting ◽  
Crash Data ◽  
Vehicle Crash ◽  
Original Dataset

The popular real-time ridesharing service has promoted social and environmental sustainability in various ways. Meanwhile, it also brings some traffic safety concerns. This paper aims to analyze factors affecting real-time ridesharing vehicle crash severity based on the classification and regression tree (CART) model. The Chicago police-reported crash data from January to December 2018 is collected. Crash severity in the original dataset is highly imbalanced: only 60 out of 2624 crashes are severe injury crashes. To fix the data imbalance problem, a hybrid data preprocessing approach which combines the over- and under-sampling is applied. Model results indicate that, by resampling the crash data, the successfully predicted severe crashes are increased from 0 to 40. Besides, the G-mean is increased from 0% to 73%, and the AUC (area under the receiver operating characteristics curve) is increased from 0.73 to 0.82. The classification tree reveals that following variables are the primary indicators of real-time ridesharing vehicle crash severity: pedestrian/pedalcyclist involvement, number of passengers, weather condition, trafficway type, vehicle manufacture year, traffic control device, driver gender, lighting condition, vehicle type, driver age and crash time. The current study could provide some valuable insights for the sustainable development of real-time ridesharing services and urban transportation.

scholarly journals Analysis of Crash Severity for Hazard Material Transportation Using Highway Safety Information System Data

SAGE Open ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 215824402093992
Author(s):  
Xiuguang Song ◽  
Jianqing Wu ◽  
Hongbo Zhang ◽  
Rendong Pi
Keyword(s):  
Information System ◽  
Safety Information ◽  
Highway Safety ◽  
Crash Severity ◽  
Great Loss ◽  
Factors Affecting ◽  
Routing And Scheduling ◽  
Adverse Weather ◽  
System Data ◽  
Material Transportation

Crash severity, as a major concern in the routing and scheduling of hazardous material shipments, has caused great loss of lives and property damage every year. Although abundant studies have been conducted to identify the relationship between different factors on crash severity, the analysis of the severity of hazard material transportation (HMT) crashes is very limited. Factors including road, vehicle, driver, and environment are not well considered in previous studies. This article analyzed the influence of various factors on HMT crash severity using Highway Safety Information System data. The random forest combined with the ordered logistic model is used for factor analysis. The results showed that annual average daily traffic, fatigues/asleep, number of lanes, speeding, adverse weather, and light are the six most important factors affecting HMT crash severity. Different from the non-HMT crashes, driver factor (e.g., driver age, gender, and drug/alcohol influence) was found to be not significantly related to crash severity. Speeding should be strictly forbidden for HMT drivers, considering the potential increased crash severity. Increasing the level of lighting can help reduce the number of severe crashes. The corresponding recommendations were provided based on the regression results.

scholarly journals Relationship between Land Use Mix and Walking Choice in High-Density Cities: A Review of Walking in Seoul, South Korea

2021 ◽  
Vol 13 (2) ◽  
pp. 810
Author(s):  
Eun Yeong Seong ◽  
Nam Hwi Lee ◽  
Chang Gyu Choi
Keyword(s):  
Land Use ◽  
Neighborhood Environment ◽  
Single Family ◽  
Housing Type ◽  
General Belief ◽  
Factors Affecting ◽  
Land Use Mix ◽  
Family Housing ◽  
Mixed Land Use ◽  
Major Factors

This study confirmed the general belief of urban planners that mixed land use promotes walking in Seoul, a metropolis in East Asia, by analyzing the effect of mixed land use on the travel mode choice of housewives and unemployed people who make non-commuting trips on weekdays. Using binomial logistic regression of commuting data, it was found that the more mixed a neighborhood environment’s uses are, the more the pedestrians prefer to walk rather than drive. The nonlinear relationship between the land use mix index and the choice to walk was also confirmed. Although mixed land use in neighborhoods increased the probability of residents choosing walking over using cars, when the degree of complexity increased above a certain level, the opposite effect was observed. As the density of commercial areas increased, the probability of selecting walking increased. In addition to locational characteristics, income and housing type were also major factors affecting the choice to walk; i.e., when the residents’ neighborhood environment was controlled for higher income and living in an apartment rather than multi-family or single-family housing, they were more likely to choose driving over walking.

Are Roundabouts Safe and Economically Viable Replacing Conventional Diamond Interchange Ramp Terminals?

2021 ◽  
pp. 036119812110088
Author(s):  
Boris Claros ◽  
Beau Burdett ◽  
Madhav Chitturi ◽  
Andrea Bill ◽  
David A. Noyce
Keyword(s):  
Empirical Bayes ◽  
Methodological Approach ◽  
Cost Savings ◽  
Weighted Distribution ◽  
Crash Rates ◽  
Design Considerations ◽  
Rate Analysis ◽  
The Cost ◽  
Benefit Cost ◽  
Service Life Cycle

Roundabout implementations at traditional intersections have been shown to be effective at reducing severe crashes. Roundabouts have also been implemented at interchange ramp terminals; however, limited research is available. In this study, 25 roundabout ramp terminal implementations were evaluated. The methodological approach consisted of Empirical Bayes for safety effectiveness and crash cost changes, crash type weighted distribution, crash rate analysis of bypass configuration, and cost of implementation. Roundabouts were effective at reducing fatal and injury crashes when replacing existing interchange diamond ramp terminals: 65% reduction for roundabouts replacing stop-controlled ramp terminals and 41% reduction for roundabouts replacing signal-controlled ramp terminals. Observed crash type weighted distributions are provided to visualize the frequency and location of crashes within roundabout ramp terminals for design considerations. Exit ramp and outside crossroad approaches with right-turn bypass showed significantly lower crash rates than designs without bypass. The crash cost analysis showed that roundabouts replacing diamond ramp terminals yielded crash cost savings of between $95,000 and $253,000 per site per year (69% to 54% decrease in crash costs). Considering crash costs savings only, the cost of implementation should be less than $1.9 million for a roundabout replacing a stop-controlled ramp terminal and less than $5.1 million for a roundabout replacing a signal-controlled ramp terminal to accomplish benefit-cost ratios greater than one for a service life cycle of 20 years. Costs are in 2019 dollars.

Examining Driver Injury Severity in Single-Vehicle Road Departure Crashes Involving Large Trucks

2021 ◽  
pp. 036119812110101
Author(s):  
Mehdi Hosseinpour ◽  
Kirolos Haleem
Keyword(s):  
Rural Areas ◽  
Injury Severity ◽  
Factors Affecting ◽  
Crash Data ◽  
Natural Logarithm ◽  
Concrete Barriers ◽  
Local Roads ◽  
Single Vehicle ◽  
Safety Countermeasures ◽  
Injury Severities

Road departure (RD) crashes are among the most severe crashes that can result in fatal or serious injuries, especially when involving large trucks. Most previous studies neglected to incorporate both roadside and median hazards into large-truck RD crash severity analysis. The objective of this study was to identify the significant factors affecting driver injury severity in single-vehicle RD crashes involving large trucks. A random-parameters ordered probit (RPOP) model was developed using extensive crash data collected on roadways in the state of Kentucky between 2015 and 2019. The RPOP model results showed that the effect of local roadways, the natural logarithm of annual average daily traffic (AADT), the presence of median concrete barriers, cable barrier-involved collisions, and dry surfaces were found to be random across the crash observations. The results also showed that older drivers, ejected drivers, and drivers trapped in their truck were more likely to sustain severe single-vehicle RD crashes. Other variables increasing the probability of driver injury severity have included rural areas, dry road surfaces, higher speed limits, single-unit truck types, principal arterials, overturning-consequences, truck fire occurrence, segments with median concrete barriers, and roadside fixed object strikes. On the other hand, wearing seatbelt, local roads and minor collectors, higher AADT, and hitting median cable barriers were associated with lower injury severities. Potential safety countermeasures from the study findings include installing median cable barriers and flattening steep roadside embankments along those roadway stretches with high history of RD large-truck-related crashes.

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