scholarly journals The Effects of Traffic Composition on Freeway Crash Frequency by Injury Severity: A Bayesian Multivariate Spatial Modeling Approach

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Huiying Wen ◽  
Jiaren Sun ◽  
Qiang Zeng ◽  
Xuan Zhang ◽  
Quan Yuan
Keyword(s):  
Injury Severity ◽  
Spatial Effects ◽  
Crash Frequency ◽  
Safety Improvement ◽  
Proposed Model ◽  
Conditional Autoregressive ◽  
Heavy Truck ◽  
Traffic Composition ◽  
Adjacent Segments ◽  
Horizontal Curvature

This study sets out to investigate the effects of traffic composition on freeway crash frequency by injury severity. A crash dataset collected from Kaiyang Freeway, China, is adopted for the empirical analysis, where vehicles are divided into five categories and crashes are classified into no injury and injury levels. In consideration of correlated spatial effects between adjacent segments, a Bayesian multivariate conditional autoregressive model is proposed to link no-injury and injury crash frequencies to the risk factors, including the percentages of different vehicle categories, daily vehicle kilometers traveled (DVKT), and roadway geometry. The model estimation results show that, compared to Category 5 vehicles (e.g., heavy truck), larger percentages of Categories 1 (e.g., passenger car) and 3 (e.g., medium truck) vehicles would lead to less no-injury crashes and more injury crashes. DVKT, horizontal curvature, and vertical grade are also found to be associated with no-injury and/or injury crash frequencies. The significant heterogeneous and spatial effects for no-injury and injury crashes justify the applicability of the proposed model. The findings are helpful to understand the relationship between traffic composition and freeway safety and to provide suggestions for designing strategies of vehicle safety improvement.

scholarly journals Investigating Spatial Autocorrelation and Spillover Effects in Freeway Crash-Frequency Data

2019 ◽  
Vol 16 (2) ◽  
pp. 219 ◽  
Author(s):  
Huiying Wen ◽  
Xuan Zhang ◽  
Qiang Zeng ◽  
Jaeyoung Lee ◽  
Quan Yuan
Keyword(s):  
Spatial Autocorrelation ◽  
Hybrid Model ◽  
Traffic Safety ◽  
Spillover Effects ◽  
Spatial Effects ◽  
Frequency Data ◽  
Contributing Factors ◽  
Exogenous Variables ◽  
Crash Frequency ◽  
Adjacent Segments

This study attempts to investigate spatial autocorrelation and spillover effects in micro traffic safety analysis. To achieve the objective, a Poisson-based count regression with consideration of these spatial effects is proposed for modeling crash frequency on freeway segments. In the proposed hybrid model, the spatial autocorrelation and the spillover effects are formulated as the conditional autoregressive (CAR) prior and the exogenous variables of adjacent segments, respectively. The proposed model is demonstrated and compared to the models with only one kind of spatial effect, using one-year crash data collected from Kaiyang Freeway, China. The results of Bayesian estimation conducted in WinBUGS show that significant spatial autocorrelation and spillover effects simultaneously exist in the freeway crash-frequency data. The lower value of deviance information criterion (DIC) and more significant exogenous variables for the hybrid model compared to the other alternatives, indicate the strength of accounting for both spatial autocorrelation and spillover effects on improving model fit and identifying crash contributing factors. Moreover, the model results highlight the importance of daily vehicle kilometers traveled, and horizontal and vertical alignments of targeted segments and adjacent segments on freeway crash occurrences.

Traffic and speed characteristics on two-lane highways: field study

2003 ◽  
Vol 30 (6) ◽  
pp. 1042-1054 ◽  
Author(s):  
Yasser Hassan
Keyword(s):  
Light Condition ◽  
Operating Speed ◽  
Ambient Light ◽  
Horizontal Curve ◽  
Vehicle Class ◽  
Rural Highways ◽  
Horizontal Alignment ◽  
Heavy Truck ◽  
Traffic Composition ◽  
Rural Highway

Many models have been developed to evaluate the operating speeds on two-lane rural highways. However, provided information usually lacks details essential to assess their applicability at locations other than where they were developed. This paper presents a procedure to interpret raw data collected on three horizontal curve sites of different two-lane rural highway classes in Ontario. The speed observations were categorized into three vehicle classes (passenger car, light truck, and multi-axle heavy truck) and four light condition categories (day, night, and two transition periods). The minimum headway and percentile value to define the operating speed were examined, and a revision of the current practice deemed not warranted. The findings also indicated that operating speeds do not depend on the time or vehicle class. Finally, the horizontal alignment affects the operating speed, but the speeds of the two travel directions on a horizontal curve may differ even with little contribution of the vertical alignment.Key words: highway geometric design, operating speed, traffic composition, traffic counters, ambient light, acceleration, deceleration.

scholarly journals Factors Contributing to the Relationship between Driving Mileage and Crash Frequency of Older Drivers

2019 ◽  
Vol 11 (23) ◽  
pp. 6643 ◽  
Author(s):  
Lee ◽  
Guldmann ◽  
Choi
Keyword(s):  
Negative Binomial ◽  
Age Groups ◽  
Urban Environments ◽  
Vehicle Miles Traveled ◽  
Crash Frequency ◽  
Rural Environments ◽  
Lower Likelihood ◽  
Conditional Autoregressive ◽  
The Relationship ◽  
Binomial Models

As a characteristic of senior drivers aged 65 +, the low-mileage bias has been reported in previous studies. While it is thought to be a well-known phenomenon caused by aging, the characteristics of urban environments create more opportunities for crashes. This calls for investigating the low-mileage bias and scrutinizing whether it has the same impact on other age groups, such as young and middle-aged drivers. We use a crash database from the Ohio Department of Public Safety from 2006 to 2011 and adopt a macro approach using Negative Binomial models and Conditional Autoregressive (CAR) models to deal with a spatial autocorrelation issue. Aside from the low-mileage bias issue, we examine the association between the number of crashes and the built environment and socio-economic and demographic factors. We confirm that the number of crashes is associated with vehicle miles traveled, which suggests that more accumulated driving miles result in a lower likelihood of being involved in a crash. This implies that drivers in the low mileage group are involved in crashes more often, regardless of the driver’s age. The results also confirm that more complex urban environments have a higher number of crashes than rural environments.

Exploring a multilevel approach with spatial effects to model housing price in San José, Costa Rica

2021 ◽  
pp. 239980832110411
Author(s):  
Eduardo Pérez-Molina
Keyword(s):  
Costa Rica ◽  
Multilevel Models ◽  
Housing Prices ◽  
Housing Price ◽  
Autoregressive Models ◽  
Metropolitan Region ◽  
Spatial Effects ◽  
San Jose ◽  
Conditional Autoregressive ◽  
Conditional Autoregressive Models

A multilevel model of the housing market for San José Metropolitan Region (Costa Rica) was developed, including spatial effects. The model is used to explore two main questions: the extent to which contextual (of the surroundings) and compositional (of the property itself) effects explain variation of housing prices and how does the relation between price and key covariates change with the introduction of multilevel effects. Hierarchical relations (lower level units nested into higher level) were modeled by specifying multilevel models with random intercepts and a conditional autoregressive term to include spatial effects from neighboring units at the higher level (districts). The random intercepts and conditional autoregressive models presented the best fit to the data. Variation at the higher level accounted for 16% of variance in the random intercepts model and 28% in the conditional autoregressive model. The sign and magnitude of regression coefficients proved remarkably stable across model specifications. Travel time to the city center, which presented a non-linear relation to price, was found to be the most important determinant. Multilevel and conditional autoregressive models constituted important improvements in modeling housing price, despite most of the variation still occurring at the lower level, by improving the overall model fit. They were capable of representing the regional structure and of reducing sampling bias in the data. However, the conditional autoregressive specification only represented a limited advance over the random intercepts formulation.

Predicting motorcycle crash injury severity using weather data and alternative Bayesian multivariate crash frequency models

2017 ◽  
Vol 108 ◽  
pp. 172-180 ◽  
Author(s):  
Wen Cheng ◽  
Gurdiljot Singh Gill ◽  
Taha Sakrani ◽  
Mohan Dasu ◽  
Jiao Zhou
Keyword(s):  
Injury Severity ◽  
Weather Data ◽  
Crash Frequency ◽  
Crash Injury Severity

scholarly journals Spatial regression methods to evaluate beekeeping production in the state of Rio de Janeiro

2013 ◽  
Vol 65 (2) ◽  
pp. 553-558
Author(s):  
W.S. Tassinari ◽  
M.C. Lorenzon ◽  
E.L. Peixoto
Keyword(s):  
Rio De Janeiro ◽  
High Performance ◽  
Spatial Regression ◽  
The State ◽  
Spatial Effects ◽  
Regression Methods ◽  
Honey Production ◽  
Production Factors ◽  
Proposed Model ◽  
Wild Vegetation

Brazilian beekeeping has been developed from the africanization of the honeybees and its high performance launches Brazil as one of the world´s largest honey producer. The Southeastern region has an expressive position in this market (45%), but the state of Rio de Janeiro is the smallest producer, despite presenting large areas of wild vegetation for honey production. In order to analyze the honey productivity in the state of Rio de Janeiro, this research used classic and spatial regression approaches. The data used in this study comprised the responses regarding beekeeping from 1418 beekeepers distributed throughout 72 counties of this state. The best statistical fit was a semiparametric spatial model. The proposed model could be used to estimate the annual honey yield per hive in regions and to detect production factors more related to beekeeping. Honey productivity was associated with the number of hives, wild swarm collection and losses in the apiaries. This paper highlights that the beekeeping sector needs support and help to elucidate the problems plaguing beekeepers, and the inclusion of spatial effects in the regression models is a useful tool in geographical data.

Modified Mixed Generalized Ordered Response Model to Handle Misclassification in Injury Severity Data

2018 ◽  
Vol 2672 (30) ◽  
pp. 53-63 ◽  
Author(s):  
Lacramioara Balan ◽  
Rajesh Paleti
Keyword(s):  
Injury Severity ◽  
Random Variable ◽  
Parameter Estimates ◽  
Discrete Random Variable ◽  
Response Models ◽  
Ordinal Response ◽  
Proposed Model ◽  
Misclassification Errors ◽  
Misclassification Rates ◽  
Ordered Response

Traditional crash databases that record police-reported injury severity data are prone to misclassification errors. Ignoring these errors in discrete ordered response models used for analyzing injury severity can lead to biased and inconsistent parameter estimates. In this study, a mixed generalized ordered response (MGOR) model that quantifies misclassification rates in the injury severity variable and adjusts the bias in parameter estimates associated with misclassification was developed. The proposed model does this by considering the observed injury severity outcome as a realization from a discrete random variable that depends on true latent injury severity that is unobservable to the analyst. The model was used to analyze misclassification rates in police-reported injury severity in the 2014 General Estimates System (GES) data. The model found that only 68.23% and 62.75% of possible and non-incapacitating injuries were correctly recorded in the GES data. Moreover, comparative analysis with the MGOR model that ignores misclassification not only has lower data fit but also considerable bias in both the parameter and elasticity estimates. The model developed in this study can be used to analyze misclassification errors in ordinal response variables in other empirical contexts.

Bayesian Semi-Parametric Realized Conditional Autoregressive Expectile Models for Tail Risk Forecasting

2020 ◽  
Author(s):  
Richard Gerlach ◽  
Chao Wang
Keyword(s):  
Value At Risk ◽  
Model Parameters ◽  
Model Framework ◽  
Measurement Equation ◽  
Asset Return ◽  
Proposed Model ◽  
Asymmetric Least Squares ◽  
Least Squares Optimization ◽  
Conditional Autoregressive ◽  
Improved Accuracy

Abstract A new model framework called Realized Conditional Autoregressive Expectile is proposed, whereby a measurement equation is added to the conventional Conditional Autoregressive Expectile model. A realized measure acts as the dependent variable in the measurement equation, capturing the contemporaneous dependence between it and the latent conditional expectile; it also drives the expectile dynamics. The usual grid search and asymmetric least squares optimization, to estimate the expectile level and parameters, suffers from convergence issues leading to inefficient estimation. This article develops an alternative random walk Metropolis stochastic target search method, incorporating an adaptive Markov Chain Monte Carlo sampler, which leads to improved accuracy in estimation of the expectile level and model parameters. The sampling properties of this method are assessed via a simulation study. In a forecast study applied to several market indices and asset return series, one-day-ahead Value-at-Risk and Expected Shortfall forecasting results favor the proposed model class.

Investigation of Heavy Truck Restraint System Effectiveness Through Finite Element Computer Simulations in Frontal Crashes

2016 ◽  
Author(s):  
Nathan Schulz ◽  
Chiara Silvestri Dobrovolny ◽  
Abhinav Mohanakrishnan
Keyword(s):  
Finite Element ◽  
Injury Severity ◽  
Seat Belt ◽  
Restraint System ◽  
Rigid Barrier ◽  
Heavy Trucks ◽  
Ring Location ◽  
Heavy Truck ◽  
Frontal Crashes ◽  
Occupant Kinematics

Computer finite element simulations play an important role in reducing the cost and time taken for prediction of a crash scenario. While interior crash protection has received adequate attention for automobiles, very little is known for commercial vehicle such as heavy trucks. The understanding of injury types for heavy trucks occupants in relation to different crash scenarios would help mitigation of the injury severity. Finite element computer models of the heavy truck cabin structure, interior cabin components, anthropomorphic test device (ATD) (also called dummy) and passive restraint systems were developed and assembled to simulate head-on crash of a heavy truck into a rigid barrier. The researchers developed a computer simulation parametric evaluation with respect to specific seat belt restraint system parameters for a speed impact of 56.3 km/h (35 mph). Restraint parameter variations within this research study are seat belt load limiting characteristics, inclusion of seat belt pretensioner, and variation of seat belt D-ring location. Additionally an airbag was included to investigate another restraint system. For each simulated impact characteristic and restraint system variation, the occupant kinematics were observed and occupant risks were assessed. Within the approximations and assumptions included in this study, the results presented in this paper should be considered as preliminary guidance on the effectiveness of the use of seat belt as occupant injury mitigation system.

A Novel Technique to Identify Hot Zones for Active Commuters’ Crashes

2018 ◽  
Vol 2672 (38) ◽  
pp. 266-276 ◽  
Author(s):  
Ahmed Osama ◽  
Tarek Sayed ◽  
Emanuele Sacchi
Keyword(s):  
Mahalanobis Distance ◽  
Empirical Bayes ◽  
Negative Binomial ◽  
Active Transportation ◽  
Spatial Effects ◽  
Distance Method ◽  
Crash Data ◽  
Safety Improvement ◽  
Pedestrian Crashes ◽  
Psi Method

This paper presents an approach to identify and rank accident-prone (hot) zones for active transportation modes. The approach aims to extend the well-known empirical Bayes (EB) potential for safety improvement (PSI) method to cases where multiple crash modes are modeled jointly (multivariate modeling). In this study, crash modeling was pursued with a multivariate model, incorporating spatial effects, using the full Bayes (FB) technique. Cyclist and pedestrian crash data for the City of Vancouver (British Columbia, Canada) were analyzed for 134 traffic analysis zones (TAZs) to detect active transportation hot zones. The hot zones identification (HZID) process was based on the estimation of the Mahalanobis distance, which can be considered an extension to the PSI method in the context of multivariate analysis. In addition, a negative binomial model was developed for cyclist and pedestrian crashes, where the EB PSI for each mode crash was quantified. The cyclist and pedestrian PSIs were combined to detect active transportation hot zones. Overall, the Mahalanobis distance method is found to outperform the PSI method in terms of consistency of results; and discrepancy is observed between the hot zones identified using both approaches.

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