Freeway Truck Traffic Safety in Wyoming: Crash Characteristics and Prediction Models

2019 ◽  
Vol 2673 (10) ◽  
pp. 333-342 ◽  
Author(s):  
Muhammad Tahmidul Haq ◽  
Milan Zlatkovic ◽  
Khaled Ksaibati
Keyword(s):  
Traffic Safety ◽  
Negative Binomial ◽  
Prediction Models ◽  
Weather Conditions ◽  
Significant Variable ◽  
Truck Traffic ◽  
Crash Data ◽  
Horizontal Curves ◽  
Rate Analysis ◽  
Road Geometry

The State of Wyoming experiences a high percentage of truck traffic along all its highways, especially Interstate 80 (I-80). The increased interactions between trucks and other vehicles have raised many operational and safety concerns. This paper presents a safety analysis and a development of safety performance functions (SPFs) along I-80, with a focus on truck crashes. Nine years of historical crash data in Wyoming (2008–2016) were used to observe the involvement of light, medium, and heavy trucks in crashes. Analysis of the major contributory factors showed that 54% of the total truck-related crashes occurred during icy road conditions and about 46% during snowy weather conditions, and approximately 45% involved driving too fast and driving in improper lane. The analysis also included segments with horizontal curves and vertical grades and their impacts on truck crashes. The crash rate analysis showed higher truck crash rate compared with total crash rate considering equal vehicle miles traveled as exposure. A zero-inflated negative binomial model was applied to develop Wyoming-specific SPFs for various truck crash types. The effects of traffic, road geometry characteristics, and weather parameters influencing different truck-related crashes were quantified from these models. Downgrades and steep upgrade sections were found to increase truck-related crashes. The number of rainy days per year was found to be a significant variable affecting truck-related crashes. On the other hand, the presence of climbing lanes has significant safety benefits.

scholarly journals Predicting Wet-Road Crashes Using the Finite-Mixture Zero-Truncated Negative Binomial Model

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Ying Chen ◽  
Zhongxiang Huang
Keyword(s):  
Traffic Safety ◽  
Negative Binomial ◽  
Safety Data ◽  
Method Comparison ◽  
Weather Conditions ◽  
Finite Mixture ◽  
Crash Risk ◽  
Crash Data ◽  
Road Crashes ◽  
Inclement Weather

Inclement weather affects traffic safety in various ways. Crashes on rainy days not only cause fatalities and injuries but also significantly increase travel time. Accurately predicting crash risk under inclement weather conditions is helpful and informative to both roadway agencies and roadway users. Safety researchers have proposed various analytic methods to predict crashes. However, most of them require complete roadway inventory, traffic, and crash data. Data incompleteness is a challenge in many developing countries. It is common that safety researchers only have access to data on sites where a crash has occurred (i.e., zero-truncated data). The conventional crash models are not applicable to zero-truncated safety data. This paper proposes a finite-mixture zero-truncated negative binomial (FMZTNB) model structure. The model is applied to three-year wet-road crash data on 395 divided roadway segments (total 586 km), and the parameters are estimated using the Markov chain Monte Carlo (MCMC) method. Comparison indicates that the proposed FMZTNB model has better fitting performance and is more accurate in predicting the number of wet-road crashes. The model is capable of capturing the heterogeneity within the sample crash data. In addition, lane width showed mixed effects in different components on wet-road crashes, which are not observed in conventional modeling approaches. Practitioners are encouraged to consider the finite-mixture zero-truncated modeling approach when complete safety dataset is not available.

scholarly journals EVALUATING THE EFFECTS OF ROAD GEOMETRY, ENVIRONMENT, AND TRAFFIC VOLUME ON ROLLOVER CRASHES

Transport ◽  
2016 ◽  
Vol 31 (2) ◽  
pp. 221-232 ◽  
Author(s):  
Mehdi Hosseinpour ◽  
Ahmad Shukri Yahaya ◽  
Ahmad Farhan Sadullah ◽  
Noriszura Ismail ◽  
Seyed Mohammad Reza Ghadiri
Keyword(s):  
Negative Binomial ◽  
Prediction Models ◽  
Warning Signs ◽  
Motor Vehicles ◽  
Count Data Models ◽  
Vehicle Traffic ◽  
Crash Data ◽  
Road Geometry ◽  
Safety Countermeasures ◽  
Horizontal Curvature

There are a number of factors that cause motor vehicles to rollover. However, the impacts of roadway characteristics on rollover crashes have rarely been addressed in the literature. This study aims to apply a set of crash prediction models in order to estimate the number of rollovers as a function of road geometry, the environment, and traffic conditions. To this end, seven count-data models, including Poisson (PM), negative binomial (NB), heterogeneous negative binomial (HTNB), zero-inflated Poisson (ZIP), zero-inflated negative binomial (ZINB), hurdle Poisson (HP), and hurdle negative binomial (HNB) models, were developed and compared using crash data collected on 448 segments of Malaysian federal roads. The results showed that the HTNB was the best-fit model among the others to model the frequency of rollovers. The variables Light-Vehicle Traffic (LVT), horizontal curvature, access points, speed limit, and centreline median were positively associated with the crash frequency, while UnPaved Shoulder Width (UPSW) and Heavy-Vehicle Traffic (HVT) were found to have the opposite effect. The findings of this study suggest that rollovers could potentially be reduced by developing road safety countermeasures, such as access management of driveways, straightening sharp horizontal curves, widening shoulder width, better design of centreline medians, and posting lower speed limits and warning signs in areas with higher rollover tendency.

scholarly journals Speed Models for Motorcycle Riders for Two-lane Rural Roads

2021 ◽  
Vol 17 ◽  
pp. 595-603
Author(s):  
Panagiotis Lemonakis ◽  
George Botzoris ◽  
Athanasios Galanis ◽  
Nikolaos Eliou
Keyword(s):  
Prediction Models ◽  
Flow Speed ◽  
Operating Speed ◽  
Rural Roads ◽  
The Road ◽  
Horizontal Curves ◽  
Road Geometry ◽  
Speed Prediction ◽  
The Subject ◽  
On The Road

The development of operating speed models has been the subject of numerous research studies in the past. Most of them present models that aim to predict free-flow speed in conjunction with the road geometry at the curved road sections considering various geometric parameters e.g., radius, length, preceding tangent, deflection angle. The developed models seldomly take into account the operating speed profiles of motorcycle riders and hence no significant efforts have been put so far to associate the geometric characteristics of a road segment with the speed behavior of motorcycle riders. The dominance of 4-wheel vehicles on the road network led the researchers to focus explicitly on the development of speed prediction models for passenger cars, vans, pickups, and trucks. However, although the motorcycle fleet represents only a small proportion of the total traffic volume motorcycle riders are over-represented in traffic accidents especially those that occur on horizontal curves. Since operating speed has been thoroughly documented as the most significant precipitating factor of vehicular accidents, the study of motorcycle rider's speed behavior approaching horizontal curves is of paramount importance. The subject of the present paper is the development of speed prediction models for motorcycle riders traveling on two-lane rural roads. The model was the result of the execution of field measurements under naturalistic conditions with the use of an instrumented motorcycle conducted by experienced motorcycle riders under different lighting conditions. The implemented methodology to determine the most efficient model evaluates a series of road geometry parameters through a comprehensive literature review excluding those with an insignificant impact to the magnitude of the operating speeds in order to establish simple and handy models.

Evaluating Safety Effectiveness of Truck Climbing Lanes using Cross-Sectional Analysis and Propensity Score Models

2019 ◽  
Vol 2673 (7) ◽  
pp. 662-672 ◽  
Author(s):  
Muhammad Tahmidul Haq ◽  
Milan Zlatkovic ◽  
Khaled Ksaibati
Keyword(s):  
Propensity Score ◽  
Traffic Safety ◽  
Propensity Scores ◽  
Negative Binomial ◽  
Panel Count Data ◽  
Cross Sectional ◽  
Crash Data ◽  
Cross Sectional Analysis ◽  
Safety Effectiveness ◽  
Sectional Analysis

The State of Wyoming is characterized by heavy truck traffic flow, especially along Interstate 80 (I-80). A large portion of I-80 in Wyoming goes through mountainous and rolling terrain, resulting in significant vertical grades. About 9% of I-80 in each direction is within vertical grades of more than 3%, with certain sections reaching grades of close to 7%. Currently, there are 14 miles of climbing lanes in both directions. This study investigates the effects of climbing lanes on traffic safety using sections of I-80 in Wyoming. Cross-sectional analysis and propensity score methods were applied to evaluate the safety effectiveness and calibrate the Crash Modification Factor (CMF) and Relative Risk (RR) for climbing lanes. Data were collected from different sources and Wyoming-specific safety performance functions were developed using crash data from 2008 to 2016 for total crashes and truck-related crashes. All the segments were selected from I-80 in Wyoming with climbing lanes as treatment sites, and segments with similar geometrical characteristics without climbing lanes as comparison sites. Aggregated data were used to develop Negative Binomial and Zero-Inflated Negative Binomial models for performing cross-sectional analysis as they were found to fit better for the crash data. On the other hand, panel count data were used to conduct a propensity scores-potential outcomes framework. The CMFs and RR for climbing lanes from both analyses were found to be effective in reducing total and truck-related crashes. This is a first study that develops CMFs for climbing lanes in Wyoming.

Effects of Varying Dispersion Parameter of Poisson–Gamma Models on Estimation of Confidence Intervals of Crash Prediction Models

2008 ◽  
Vol 2061 (1) ◽  
pp. 46-54 ◽  
Author(s):  
Srinivas Reddy Geedipally ◽  
Dominique Lord
Keyword(s):  
Confidence Intervals ◽  
Negative Binomial ◽  
Prediction Models ◽  
Motor Vehicle ◽  
Dispersion Parameter ◽  
Transportation Safety ◽  
Dispersion Parameters ◽  
Crash Data ◽  
Varying Dispersion ◽  
Using Data

In estimating safety performance, the most common probabilistic structures of the popular statistical models used by transportation safety analysts for modeling motor vehicle crashes are the traditional Poisson and Poisson–gamma (or negative binomial) distributions. Because crash data often exhibit overdispersion, Poisson–gamma models are usually the preferred model. The dispersion parameter of Poisson–gamma models had been assumed to be fixed, but recent research in highway safety has shown that the parameter can potentially be dependent on the covari-ates, especially for flow-only models. Given that the dispersion parameter is a key variable for computing confidence intervals, there is reason to believe that a varying dispersion parameter could affect the computation of confidence intervals compared with confidence intervals produced from Poisson–gamma models with a fixed dispersion parameter. This study evaluates whether the varying dispersion parameter affects the computation of the confidence intervals for the gamma mean (m) and predicted response (y) on sites that have not been used for estimating the predictive model. To accomplish that objective, predictive models with fixed and varying dispersion parameters were estimated by using data collected in California at 537 three-leg rural unsignalized intersections. The study shows that models developed with a varying dispersion parameter greatly influence the confidence intervals of the gamma mean and predictive response. More specifically, models with a varying dispersion parameter usually produce smaller confidence intervals, and hence more precise estimates, than models with a fixed dispersion parameter, both for the gamma mean and for the predicted response. Therefore, it is recommended to develop models with a varying dispersion whenever possible, especially if they are used for screening purposes.

scholarly journals Study on the influence of skid resistance on traffic safety of highway with a high ratio of bridges and tunnels

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Yongjie Ding ◽  
Danni Li ◽  
Mingxuan Huang ◽  
Xuejuan Cao ◽  
Boming Tang
Keyword(s):  
Correlation Analysis ◽  
Traffic Safety ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Prediction Models ◽  
Back Propagation ◽  
Weather Conditions ◽  
High Ratio ◽  
Skid Resistance ◽  
The Relationship

ABSTRACT The safety of highways with a high ratio of bridges and tunnels is related to multiple factors, for example, the skid resistance of the pavement surface. In this study, the distribution of accidents under different conditions was calculated to investigate the relationship between the road skid resistance and the incidence of traffic accidents based on the traffic accident data of the Yuxiang highway. Statistical results show that weather conditions and road alignment may affect traffic accidents. The correlation analysis method was used to study the relationship between three factors and traffic accidents. The results show that road alignment, weather conditions and road skid resistance are related to the incidence of traffic accidents. The traffic accident prediction models were established based on back propagation neural network to verify the correlation analysis results. It is confirmed that road alignment, weather conditions and road skid resistance are the factors that affect traffic accidents.

A Comparative Analysis of Factors Affecting the Frequency and Severity of Freight-Involved and Non-Freight Crashes on a Major Freight Corridor Freeway

2018 ◽  
Vol 2672 (34) ◽  
pp. 49-62 ◽  
Author(s):  
Samuel G. Taylor ◽  
Brendan J. Russo ◽  
Emmanuel James
Keyword(s):  
Los Angeles ◽  
Negative Binomial ◽  
Prediction Models ◽  
Negative Impact ◽  
The United States ◽  
Environmental Data ◽  
Traffic Crashes ◽  
Factors Affecting ◽  
Crash Data ◽  
Severity Prediction

Traffic crashes cost society billions of dollars each year as a result of property damage, injuries, and fatalities. Additionally, traffic crashes have a negative impact on mobility, as they are a primary cause of non-recurring delay. With the Interstate 10 corridor between the ports of Los Angeles and Houston being one of the most vital links for goods movement across the United States, safety and mobility along this freeway, particularly for freight traffic, are of significant concern. This study, which utilized six years of crash data from the state of Arizona, explores factors affecting the frequency and severity of crashes along the Arizona portion of the I-10 corridor, with a particular focus on freight-related crashes. The safety performance along the I-10 is analyzed through the development of crash frequency and severity prediction models using integrated crash, roadway, traffic, and environmental data. Negative binomial and ordered logit models, with the incorporation of random parameters, were estimated to provide a detailed understanding of factors associated with freight-involved crashes and how they compare to non-freight crashes in terms of frequency and severity. The results showed that several roadway- crash-, vehicle-, and person-related variables were associated with the frequency and/or severity of crashes along the study corridor. These findings provide important insights which can be used to develop or plan countermeasures aimed at improving the safety and efficiency of freight travel, which may include new ITS technologies, and targeted educational and enforcement campaigns.

Assessment of the Effects of Volume Completeness and Spatial and Temporal Correlation on Hourly Freeway Crash Prediction Models

2020 ◽  
Vol 2674 (9) ◽  
pp. 1097-1109
Author(s):  
Nancy Dutta ◽  
Michael D. Fontaine
Keyword(s):  
Traffic Safety ◽  
Negative Binomial ◽  
Prediction Models ◽  
Data Availability ◽  
Volume Data ◽  
Cross Sectional ◽  
Mixed Effect ◽  
Crash Prediction ◽  
Hourly Data ◽  
Crash Prediction Models

Traditional traffic safety analyses of crash frequency usually use highly aggregated cross-sectional data and ignore the time-varying nature of some critical factors. This research used 7 years of hourly data from 110 rural four-lane segments and 80 urban six-lane segments to develop hourly level crash prediction models and contrasted them with traditional annual average daily traffic (AADT)-based models. To account for the overdispersion of data and unobserved heterogeneity, generalized linear mixed-effect models were contrasted with negative binomial models. The models used average hourly volume as a measure of exposure, and the quantity of volume data available for the sites ranged from continuous counts to locations where only a couple of weeks of data were available every other year (short counts). While developing disaggregated models, the difference in data availability from these sources can be a potential source of error, so evaluating the change in performance of prediction models with changes in volume data availability was examined. The results showed that the best models include a combination of average hourly volume, selected geometric variables, and speed related parameters. Hourly models that included speed parameters consistently outperformed AADT models. Further investigation revealed that the positive effect of using a more inclusive and larger dataset was larger than the effect of accounting for data correlation. This showed that using short count stations as a data source does not diminish the quality of the developed models, thus indicating that these methods could be applied broadly across agencies, even when volume data is relatively sparse.

Development of Macro-Level Crime and Collision Prediction Models to Support Data-Driven Approach to Crime and Traffic Safety (DDACTS)

2018 ◽  
Vol 2672 (33) ◽  
pp. 56-66 ◽  
Author(s):  
Emmanuel A. Takyi ◽  
Seun Daniel Oluwajana ◽  
Peter Y. Park
Keyword(s):  
Law Enforcement ◽  
Traffic Safety ◽  
Empirical Bayes ◽  
Negative Binomial ◽  
Prediction Models ◽  
Data Driven ◽  
Macro Level ◽  
Goodness Of Fit Test ◽  
Violent Crimes ◽  
Fatal Injury

The number of violent crimes and fatal-injury collisions concerns many jurisdictions. Traditional enforcement tactics are often reactive, relying on historical crime and collision data to select locations for law enforcement. Advanced law enforcement tactics take a proactive approach. Such tactics include Data-Driven Approaches to Crime and Traffic Safety (DDACTS), which uses predicted numbers of crimes and collisions to identify locations for law enforcement. This DDACTS study was conducted in Regina, Saskatchewan, Canada. The research developed macro-level prediction models to predict violent crimes and collisions in each traffic analysis zone (TAZ) in Regina. The zonal nature of the analysis is important for overcoming confidentiality and privacy issues associated with violent crimes and fatal-injury collisions. Fifty-four input variables were used to describe each TAZ’s crimes, collisions, socio-demographic, road inventory, traffic, and land use characteristics. The analysis used negative binomial regression coupled with the empirical Bayes method (a popular approach in transportation, but relatively new to crime mapping) to develop two statistical models that predict the long-term mean value for the number of violent crimes/collisions per zone. Cumulative residual plots were used as the main goodness-of-fit test. The findings are summarized on a map showing the top ten hotzones for violent crimes, the top ten hotzones for fatal-injury collisions, and the zones where the crime and collisions zones overlap. The overlapping zones are the DDACTS zones. By focusing law enforcement in the DDACTS zones, it may be possible to reduce violent crimes and fatal-injury collisions simultaneously and use limited resources more cost effectively.

Impact of traffic Enforcement on Traffic Safety

2017 ◽  
Vol 19 (4) ◽  
pp. 238-246 ◽  
Author(s):  
Mohammad Mahdi Rezapour Mashhadi ◽  
Promothes Saha ◽  
Khaled Ksaibati
Keyword(s):  
Traffic Safety ◽  
Negative Binomial ◽  
Motor Vehicle ◽  
Seat Belt ◽  
Traffic Enforcement ◽  
Horizontal Curves ◽  
Binomial Modeling ◽  
The Cost ◽  
Number Of Citations ◽  
The Impact

Motor vehicle crashes (MVCs) have a huge cost to society in terms of death, injury and property damage. The cost of fatal MVCs alone is estimated at US $44 billion per year. Among many confounding factors, traffic citations as an element that may reduce MVC frequency are not well understood, and most research carried out to date has evaluated the effects of the total number of citations on the number of MVCs. However, certain types of citations may be more likely to reduce the number of MVCs, whereas other types are not very effective. This research was set out to examine the impact of different types of traffic citations on MVCs on two hazardous main US highways in Wyoming US-30 and US-26. A negative binomial modeling technique was implemented by exploiting 4 years of crash and citations data to identify the causal impacts of traffic citations on crash frequency by incorporating traffic and geometric features. The modeling results showed that higher numbers of speeding and seat belt citations reduce the number of crashes significantly. These findings are the results of law enforcement efforts along the highways. Traffic count and the number of horizontal curves were found to significantly increase the number of MVCs.

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