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 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.

scholarly journals Developing a Clustering-Based Empirical Bayes Analysis Method for Hotspot Identification

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Yajie Zou ◽  
Xinzhi Zhong ◽  
John Ash ◽  
Ziqiang Zeng ◽  
Yinhai Wang ◽  
...  
Keyword(s):  
Empirical Bayes ◽  
Reference Group ◽  
Analysis Method ◽  
Crash Frequency ◽  
Crash Data ◽  
The Road ◽  
Safety Performance Functions ◽  
Bayes Analysis ◽  
Rural Highway ◽  
Selection Of

Hotspot identification (HSID) is a critical part of network-wide safety evaluations. Typical methods for ranking sites are often rooted in using the Empirical Bayes (EB) method to estimate safety from both observed crash records and predicted crash frequency based on similar sites. The performance of the EB method is highly related to the selection of a reference group of sites (i.e., roadway segments or intersections) similar to the target site from which safety performance functions (SPF) used to predict crash frequency will be developed. As crash data often contain underlying heterogeneity that, in essence, can make them appear to be generated from distinct subpopulations, methods are needed to select similar sites in a principled manner. To overcome this possible heterogeneity problem, EB-based HSID methods that use common clustering methodologies (e.g., mixture models, K-means, and hierarchical clustering) to select “similar” sites for building SPFs are developed. Performance of the clustering-based EB methods is then compared using real crash data. Here, HSID results, when computed on Texas undivided rural highway cash data, suggest that all three clustering-based EB analysis methods are preferred over the conventional statistical methods. Thus, properly classifying the road segments for heterogeneous crash data can further improve HSID accuracy.

Impact of Congestion and Traffic Flow on Crash Frequency and Severity: Application of Smartphone-Collected GPS Travel Data

2017 ◽  
Vol 2659 (1) ◽  
pp. 43-54 ◽  
Author(s):  
Joshua Stipancic ◽  
Luis Miranda-Moreno ◽  
Nicolas Saunier
Keyword(s):  
Traffic Flow ◽  
Traffic Congestion ◽  
Quebec City ◽  
Crash Severity ◽  
Crash Frequency ◽  
Crash Data ◽  
Instrumented Vehicles ◽  
Minor Injuries ◽  
The Impact ◽  
The Relationship

Mobility and safety are the two greatest priorities within any transportation system. Ideally, traffic flow enhancement and crash reductions could occur simultaneously, although their relationship is likely complex. The impact of traffic congestion and flow on road safety requires more empirical evidence to determine the direction and magnitude of the relationship. The study of this relationship is an ideal application for instrumented vehicles and surrogate safety measures (SSMs). The purpose of this paper is to correlate quantitative measures of congestion and flow derived from smartphone-collected GPS data with collision frequency and severity at the network scale. GPS travel data were collected in Quebec City, Quebec, Canada, and the sample for this study contained data for more than 4,000 drivers and 20,000 trips. The extracted SSMs, the congestion index (CI), average speed ( V), and the coefficient of variation of speed (CVS) were compared with crash data collected over an 11-year period from 2000 to 2010 with the use of Spearman’s correlation coefficient and pairwise Kolmogorov–Smirnov tests. The correlations with crash frequency were weak to moderate. CI was shown to be positively correlated with crash frequency, and the relationship to crash severity was found to be nonmonotonous. Higher congestion levels were related to crashes with major injuries, whereas low congestion levels were related to crashes with minor injuries and fatalities. Surprisingly, V was found to be negatively correlated with crash frequency and had no conclusive statistical relationship to crash severity. CVS was positively correlated with crash frequency and statistically related to increased crash severity. Future work will focus on the development of a network screening model that incorporates these SSMs.

scholarly journals Urban Intersections and Traffic Safety in the City of San Antonio

2019 ◽  
Vol 271 ◽  
pp. 06003
Author(s):  
Qasim Adegbite ◽  
Khondoker Billah ◽  
Hatim Sharif ◽  
Samer Dessouky
Keyword(s):  
Traffic Safety ◽  
Traffic Control ◽  
San Antonio ◽  
Binary Logistic Regression ◽  
Crash Severity ◽  
Binary Logistic Regression Model ◽  
Crash Frequency ◽  
Crash Rates ◽  
Traffic Control System ◽  
The City

Intersections are high-risk locations on roadways and often experience high incidence of crashes. Better understanding of the factors contributing to crashes and deaths at intersections is crucial. This study analyzed the factors related to crash incidence and crash severity at intersections in San Antonio for crashes from 2013 to 2017 and identified hotspot locations based on crash frequency and crash rates. Binary logistic regression model was considered for the analysis using crash severity as the response variable. Factors found to be significantly associated with the severity of intersection crashes include age of driver, day of the week, month, road alignment, and traffic control system. The crashes occurred predominantly in the highdensity center of the city (downtown area). Overall, the identification of risk factors and their impact on crash severity would be helpful for road safety policymakers to develop proactive mitigation plans to reduce the frequency and severity of intersection crashes.

Combined Crash Frequency–Crash Severity Evaluation of Geometric Design Decisions

2015 ◽  
Vol 2521 (1) ◽  
pp. 54-63 ◽  
Author(s):  
M. Scott Shea ◽  
Thanh Q. Le ◽  
Richard J. Porter
Keyword(s):  
Regression Models ◽  
Negative Binomial ◽  
High Sensitivity ◽  
Negative Binomial Regression ◽  
Crash Severity ◽  
Crash Frequency ◽  
Design Decisions ◽  
Crash Data ◽  
Safety Performance Function ◽  
Traffic Characteristics

This paper quantified the effects of freeway ramp spacing and auxiliary lane presence on crash frequency and crash severity. Crash frequencies were predicted with a safety performance function, and crash severities were estimated with what was termed a “severity distribution function.” The paper then demonstrated how to combine quantitative knowledge related to the effects of ramp spacing and auxiliary lane presence on both crash frequency and severity into a framework for assessing the overall crash cost for different ramp configurations. Geometric features, traffic characteristics, and crash data were collected for 404 freeway segments in California and Washington State. Negative binomial regression models and multinomial logit regression models were used to estimate the effects of ramp spacing and auxiliary lane presence on expected crash frequencies and crash severities, respectively. Results showed that expected multiple-vehicle crash frequency increased as ramp spacing decreased. Meanwhile, there was a decrease in the proportion of severe crashes (fatal, incapacitating injury) with a decrease in ramp spacing, even though the overall frequency of these severe crashes remained relatively unchanged. Providing an auxiliary lane was expected to decrease crash frequency, although this reduction appeared to be primarily in crashes that were less severe (possible injury and property damage only). The findings appeared to effectively capture the complex relationships between geometric designs and operations and the high sensitivity between speed and crash severity. The paper provided quantitative tools for making informed freeway and interchange design decisions where ramp spacing and auxiliary lanes were considerations.

scholarly journals Comparison of Factors Affecting Crash Severities in Hit-and-Run and Non-Hit-and-Run Crashes

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Bei Zhou ◽  
Zongzhi Li ◽  
Shengrui Zhang
Keyword(s):  
Comparison Group ◽  
Reference Group ◽  
Multinomial Logistic Regression ◽  
Crash Severity ◽  
Property Damage ◽  
Average Percentage ◽  
Crash Data ◽  
Hit And Run ◽  
The Impact ◽  
Average Percentage Deviation

A hit-and-run (HR) crash occurs when the driver of the offending vehicle flees the crash scene without reporting it or aiding the victims. The current study aimed at contributing to existing literatures by comparing factors which might affect the crash severity in HR and non-hit-and-run (NHR) crashes. The data was extracted from the police-reported crash data from September 2017 to August 2018 within the City of Chicago. Two multinomial logistic regression models were established for the HR and NHR crash data, respectively. The odds ratio (OR) of each variable was used to quantify the impact of this variable on the crash severity. In both models, the property damage only (PDO) crash was selected as the reference group, and the injury and fatal crash were chosen as the comparison group. When the injury crash was taken as the comparison group, it was found that 12 variables contributed to the crash severities in both HR and NHR model. The average percentage deviation of OR for these 12 variables was 34%, indicating that compared with property damage, HR crashes were 34% more likely to result in injuries than NHR crashes on average. When fatal crashes were chosen as the comparison group, 2 variables were found to be statistically significant in both the HR and the NHR model. The average percentage deviation of OR for these 2 variables was 127%, indicating that compared with property damage, HR crashes were 127% more likely to result in fatalities than NHR crashes on average.

scholarly journals A Crash Surrogate Metric considering Traffic Flow Dynamics in a Motorway Corridor

2018 ◽  
Vol 2018 ◽  
pp. 1-7
Author(s):  
Xu Wang ◽  
Kai Liu
Keyword(s):  
Traffic Flow ◽  
Risk Index ◽  
Simple Calculation ◽  
Flow Dynamics ◽  
Crash Data ◽  
Time To Collision ◽  
Crash Rates ◽  
Crash Surrogate ◽  
Surrogate Measures ◽  
Levels Of Service

We proposed a new crash surrogate metric, i.e., the maximum disturbance that a car following scenario can accommodate, to represent potential crash risks with a simple closed form. The metric is developed in consideration of traffic flow dynamics. Then, we compared its performance in predicting the rear-end crash risks for motorway on-ramps with other two surrogate measures (time to collision and aggregated crash index). To this end, a one-lane on-ramp of Pacific Motorway, Australia, was selected for this case study. Due to the lack of crash data on the study site, historical crash counts were merged according to levels of service (LOS) and then converted into crash rates. In this study, we used the societal risk index to represent the crash surrogate indicators and built relationships with crash rates. The final results show that (1) the proposed metric and aggregated crash index are superior to the time to collision in predicting the rear-end crash risks for on-ramps; (2) they have a relatively similar performance, but due to the simple calculation, the proposed metric is more applicable to some real-world cases compared with the aggregated crash index.

Examining Safety on Two-Lane and Multilane Highways in Consideration of Access Spacing

2019 ◽  
Vol 2673 (11) ◽  
pp. 388-397
Author(s):  
Raha Hamzeie ◽  
Megat-Usamah Megat-Johari ◽  
Iftin Thompson ◽  
Timothy P. Barrette ◽  
Trevor Kirsch ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Random Effect ◽  
Management Strategies ◽  
Negative Binomial Regression ◽  
Access Point ◽  
Management Policy ◽  
Crash Risk ◽  
Access Management ◽  
Crash Data ◽  
Crash Rates

Access management strategies, such as the introduction of minimum access point spacing criteria and turning movement restrictions, have been shown to be important elements in optimizing the operational and safety performance of roadway segments. The relationship between safety and these types of access policies is a complex issue, and the impacts of such features on traffic crashes is critical to the development of appropriate access management strategies. The purpose of this study was to provide a quantitative evaluation of how crash risk on multilane and two-lane highways varies with respect to access spacing in support of the development of a revised access management policy. Data were obtained for approximately 1,247 and 5,795 mi of segments across multilane and two-lane highways, respectively. Crash data were obtained for a five-year period from 2012 to 2016 and a series of random effect negative binomial regression models were estimated for each facility to examine the association between crash frequency, access point spacing, and traffic volume. For both facility types, crashes were found to increase consistently as the average spacing of access points along road segments decreased. Crash rates were highest when consecutive accesses were within 150 ft of one another and the frequency of crashes decreased substantively as spacing was increased to 300 ft and, particularly, 600 ft. With spacing beyond 600 ft, crash rates continued to decrease, although these improvements were less pronounced than at the lower range of values. These findings were generally consistent on multilane and two-lane highways.

scholarly journals Road traffic accidents on Senai-Desaru expressway

2018 ◽  
Vol 250 ◽  
pp. 02002 ◽  
Author(s):  
Nordiana Mashros ◽  
SittiAsmah Hassan ◽  
Yaacob Haryati ◽  
Mohd Shahrir Amin Ahmad ◽  
Ismail Samat ◽  
...  
Keyword(s):  
Traffic Safety ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Contributing Factors ◽  
Property Damage ◽  
Crash Frequency ◽  
Crash Data ◽  
Run Off ◽  
Accident Data ◽  
Road Segments

Understanding and prioritising crash contributing factors is important for improving traffic safety on the expressway. This paper aims to identify the possible contributory factors that were based on findings obtained from crash data at Senai-Desaru Expressway (SDE), which is the main connector between the western and eastern parts of Johor, Malaysia. Using reported accident data, the mishaps that had occurred along the 77.2 km road were used to identify crash patterns and their possible related segment conditions. The Average Crash Frequency and Equivalent Property Damage Only Average Crash Frequency Methods had been used to identify and rank accident-prone road segments as well as to propose for appropriate simple and inexpensive countermeasures. The results show that the dominant crash type along the road stretches of SDE had consisted of run-off-road collision and property damage only crashes. All types of accidents were more likely to occur during daytime. Out of the 154 segments, the 4 most accident-prone road segments had been determined and analysed. The results obtained from the analyses suggest that accident types are necessary for identifying the possible causes of accidents and the appropriate strategies for countermeasures. Therefore, this accident analysis could be helpful to relevant authorities in reducing the number of road accidents and the level of accident severity along the SDE.

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