Investigating the effect of modeling single-vehicle and multi-vehicle crashes separately on confidence intervals of Poisson-gamma models

Analyzing the transition from two-vehicle collisions to chain reaction crashes: A hybrid approach using random parameters logit model, interpretable machine learning, and clustering

Chain reaction crashes (CRC) begin with a two-vehicle collision and rapidly intensify as more vehicles get directly involved. CRCs result in more extensive damage compared to two-vehicle crashes and understanding the progression of a two-vehicle collision into a CRC can unveil preventive strategies that have received less attention. In this study, to align with recent research direction and overcome the limitations of econometric and machine learning (ML) modelling, a hybrid approach is adopted. Moreover, to tackle the existing challenges in crash analysis, addressing unobserved heterogeneity in ML, and exploring random parameter effects and interactions more precisely, a new approach is proposed. To achieve this, a hybrid random parameter logit model and interpretable ML, joint with prior latent class clustering is implemented. Notably, this is the first attempt at using a clustering with hybrid modeling. The significant risk factors, their critical values, distinct effects, and interactions are interpreted using both marginal effects and the SHAP (SHapley Additive exPlanations) method across clusters. This study utilizes crash, traffic, and geometric data from eleven suburban freeways in Iran collected over a 5-year period. The overall results indicate an increased risk of CRC in congested traffic, higher traffic variation, and on horizontal curves combined with longitudinal slopes. Some parameters exhibit distinct or fluctuating effects, which are discussed across different conditions or considering interactions. For instance, during nighttime, heightened congestion on 2-lane freeways, increased traffic variation in less congested conditions, and adverse weather combined with horizontal curves and slopes pose risks. During daytime, increased traffic variation within highly congested sections, higher proportion of heavy vehicle traffic in moderately congested sections, and two lanes in each direction coupled with curves, elevate the levels of risk. The results of this study provide a better understanding of risk factors impact across different conditions, which are usable for policy makers.

Investigating the severity of single-vehicle truck crashes under different crash types using mixed logit models

INTRODUCTION

Almost 90% of fatal road crashes occur in developing countries. Among these countries, Iran has a noticeable fatal crash rate of 21.47 deaths per 100,000 persons. Improving the safety of trucks is of particular importance in Iran where road freight is used to transport almost 90% of the commodities. Researchers have suggested dichotomizing crashes into single- and multi-vehicle categories and found that when this is performed vast differences can be identified between the mechanisms behind these categories of crashes, particularly when investigating truck crashes.

METHOD

This study investigated single-vehicle truck crashes in Khorasan Razavi province in Iran from 2013 to 2021. Likelihood ratio tests were employed to show that separate models are statistically valid for different crash types. Subsequently, three mixed logit crash-type models were developed to investigate 5,703 single-vehicle truck crashes.

RESULTS

Four significant variables were exclusive to collisions with an object (brake failure, ABS, primary roads, and rainy or snowy weather), five significant variables were associated with run-off-road crashes (driving a loaded truck, speed limit (>60 km/h), paved shoulders, driving uphill, and inability to control the truck), and three significant variables were associated with overturn crashes (overloaded truck, curved roads, and changing direction suddenly). In all crash types, both fastening the seatbelt and speeding were found to be significant factors.

CONCLUSION

The research highlights the need to analyze single-vehicle truck crashes using distinct crash type models and highlights the unique contributing factors of three common single-vehicle crash types.

PRACTICAL APPLICATIONS

The study presents recommendations for policy to address key crash risks for trucks in Iran, including education and training to improve driver experience, compliance with seat belt usage, enforcement of speeding, and vehicle technologies to monitor drivers.

Analyzing driver injury severity in two-vehicle rear-end crashes considering leading-following configurations based on passenger car and light truck involvement

Rear-end crash is a major type of traffic crashes leading to a large number of injuries and fatalities each year, and passenger cars and light trucks are two main vehicle types in rear-end crashes on US roadways. Passenger cars and light trucks are different in size, vehicle mass and driver's vision. It is necessary to investigate the driver injury outcome patterns in rear-end crashes between passenger cars and light trucks considering crash configurations regarding the leading and following vehicle types. This study employs latent class multinomial logit (MNL) model to examine the risk factors on driver injury severity along with heterogeneity in variable effects presented by the cluster pattern in two-vehicle rear-end crashes involving passenger cars and light trucks, considering four crash configuration types, i.e., a passenger car struck by a passenger car, a light truck struck by a light truck, a passenger car struck by a light truck, and a light truck struck by a passenger car as exploratory variables. A model with two latent classes, which indicates the heterogeneity in variable effects among all the observations, is found to best fit the 7-year crash dataset from Washington State. The pseudo-elasticities are calculated to quantify the marginal effects of the contributing factors. The risk factors curve and sloping road condition, driver without seatbelt, and driver age of 65 and above increase driver fatality and serious injury risk greatly, and these three factors contribute from different latent classes. The crash configuration of a passenger car struck by a light truck is found to be one of class characteristics factors, which indicates that the heterogeneity exists between these two vehicle types. This factor is also a risk factor of injury. Furthermore, the leading vehicle is found to be much more vulnerable and closely related to injury, especially when it is in the crash of a passenger car struck by a light truck. The latent classes discovered give theoretical evidence of how to appropriately select subset data for further model construction for practical interest of serious injury prevention. The risk factors and their influence on injury severity provide beneficial insights on developing relevant countermeasures and strategies for injury severity mitigation on rear-end crashes involving passenger cars and light trucks.

Effects of design consistency on run-off-road crashes: An application of a Random Parameters Negative Binomial Lindley model

Run-off-road crashes are one of the most common crash types, especially in rural roadway environments contributing significantly to fatalities and severe injuries. These crashes are complex and multi-dimensional events, and factors like road geometry, driver behaviour, traffic characteristics and roadside features contribute to their occurrence, separately or interactively. Sudden changes in road geometry, in particular, can influence driver behaviour, and therefore, in developing a micro-level crash risk model for run-off-road crashes, one of the challenges is incorporating the effects of driver behaviour (disaggregated information) that may arise from the variations in road geometry (aggregated information). This study aims to examine the interaction between road geometry and driver behaviour through a set of measures for design consistency on two-lane rural roads. Multiple data sources, including crash data for 2014-18, traffic data, probe speed data and roadway geometric data, for twenty-three highways in Queensland, Australia, have been fused for this study. Seventeen types of design consistency measures with regard to alignment consistency, operating speed consistency and driving dynamics are tested. A run-off-road crash risk model is estimated by employing the Random Parameters Negative Binomial Lindley regression framework, which accounts for excess zeros in the crash counts and captures the effects of unobserved heterogeneity in the parameter estimates. Results indicate that the geometric design consistency capturing the interaction between driver behaviour and operational factors better predicts run-off-road crashes along rural highways. In addition, roadside attributes like clear zone width, infrastructures, terrain, and roadway remoteness also contribute to run-off-road crashes. The findings of the study provide a comprehensive understanding of the influence of variations in roadway geometry on driver behaviour and run-off-road crashes along rural highways.

Investigating the Difference in Factors Contributing to the Likelihood of Motorcyclist Fatalities in Single Motorcycle and Multiple Vehicle Crashes

In order to better understand the factors affecting the likelihood of motorcyclists' fatal injuries, motorcycle-involved crashes were investigated based on the involvement of the following vehicles: single motorcycle (SM), multiple motorcycles (MM) and motorcycle versus vehicle (MV) crashes.

METHOD

Binary logit and mixed logit models that consider the heterogeneity of parameters were applied to identify the critical factors that increase the likelihood of motorcyclist fatality.

RESULTS

Mixed logit models were found to have better fitting performances. Factors that increase the likelihood of motorcyclist fatality include lanes separated by traffic islands, male motorcyclists, and riding with BAC values of less than the legally limited value. Collisions with trees or utility poles lead to the highest likelihood of fatality in SM crashes. The effects of curved roads, same-direction swipe crashes, youth, and unlicensed motorcyclists are only significant in the likelihood of fatality in SM crashes.

CONCLUSIONS

Motorcyclists tend to be killed if they collide with large engine-size motorcycles and vehicles, unlicensed motorcyclists, or drivers with speeding related or right-of-way violations with positive BAC values. Driving or riding should be prohibited for any amount of alcohol or for anyone with a positive BAC value. Law enforcement should focus on unlicensed, speeding motorcyclists and drivers, and those who violate the right of way or perform improper turns. Roadside objects and facilities should be checked for appropriate placement and be equipped with reflective devices or injury protection facilities.

Influence of Intersection Density on Risk Perception of Drivers in Rural Roadways: A Driving Simulator Study

With the aim of maintaining a decent level of accessibility, the presence of intersections, often in high numbers, is one of the typical features of rural roads. However, evidence from literature shows that increasing intersection density increases the risk of accidents. Accident analysis literature regarding intersection density primarily consists of accident prediction models which are a useful tool for measuring safety performance of roads, but the literature is lacking in terms of evaluation of driver behavior using direct measurements of driver performance. This study focuses on the influence of intersection density on the risk perception of drivers through experiments carried out with a driving simulator. A virtual driving environment of a rural roadway was constructed. The road consisted of segments featuring extra-urban and village driving environments with varying intersection density level. Participants were recruited to drive through this virtual driving environment. Various driver performance measures such as vehicle speed and brake and gas pedal usage were collected from the experiment and then processed for further analysis. Results indicate an increase in driver’s perceived risk when the intersection density increased, according with the findings from the accident prediction modeling literature. However, at the same time, this driving simulator study revealed some interesting insights about oscillating perceived risk among drivers in the case of mid-level intersection separation distances. Beyond the accident research domain, findings from this study can also be useful for engineers and transportation agencies associated with access management to make more informed decisions.

Factors affecting injury severity and the number of vehicles involved in a freeway traffic accident: investigating their heterogeneous effects by facility type using a latent class approach

The number of vehicles involved in a traffic accident can be representative of the severity of the accident and provide profound insight into the diverse factors affecting severity, which cannot be identified through the victim fatality rate. This paper presents an analysis and comparison between the effects of factors affecting injury severity and the number of involved vehicles. In this study, a latent class model was used to investigate the unobserved heterogeneity of the accident factors. Freeway facility types are latent factors that affect the heterogeneity of the effects of accident factors. The class mainly including accidents at the freeway mainline sections included more injury/fatal accidents and multiple-vehicle accidents and more significant accident factor estimation results than the other class including accidents at the tollgates or ramps. Among these factors, night-time, faults made by the driver, and heavy vehicle accidents were found to increase the accident severity. Investigating accident factors affecting both the injury severity and number of involved vehicles is important as the number of people who are injured or dead is likely to increase when multiple vehicles are involved in the accident.

Exploring relationships between microscopic kinetic parameters of tires under normal driving conditions, road characteristics and accident types

INTRODUCTION

Freeway accidents are a leading cause of death in China, which also triggers substantial economic loss and an emotional burden to society. However, the internal mechanism of how microscopic kinetic parameters of vehicles influenced by road characteristics determine the occurrence of different types of accidents has not been explicitly studied. This research aimed to explore the "link role" of tire microscopic kinetic parameters in road characteristic variables and traffic accidents to aid in facilitating the traffic design and management, and thus to prevent traffic accident.

METHOD

A mountain freeway in Zhejiang Province, China was used as the research object and the data used in this paper were obtained through a real-time vehicle experiment. Multiple estimation models, including the standard ordered logit (SOL) model, fixed parameters logit (FPL) model, and random parameters logit (RPL) model were established.

RESULTS

The findings show that road characteristics will affect the longitudinal kinetic characteristics of the vehicle and, consequently, map the level of risk of rear-end accidents. Driving compensation effects were also identified in this paper (i.e., the drivers tend to be more cautious in complicated driving circumstances). Another finding relating to the mountain freeway is that different tunnel characteristics (e.g., tunnel entrance and tunnel exit) have different effects on different types of traffic accidents. Practical Applications: The framework proposed in this article can provide new insight for researchers to enlarge the research subjects of both explanatory and outcome variables in accident analysis. Future research could be implemented to consider more driving conditions.

Investigation of factors contributing to injury severity in single vehicle motorcycle crashes in India

Single-vehicle crashes are of major concern in both developed as well as in Low Middle Income Countries due to the severity of injuries, particularly fatal accidents. In India, a significant proportion of crashes are single-vehicle crashes. The vehicles which are involved in accidents due to causes such as self skidding, hitting stationary objects, trees that are simply contributed by the drivers themselves are referred to as out-of-control single-vehicle crashes. The main objective of this study is to evaluate the risk factors associated with single-vehicle motorcycle collisions in Tamilnadu, India and identifies the unique characteristics and injury outcomes associated with these collisions. Crash dataset for the present study was prepared from the police-reported crashes for the past nine years that occurred within the state of Tamilnadu between 2009 and 2017. The identified contributory factors which influence injury severity include driver characteristics, crash-related factors, traffic-related factors, vehicle and environment-related factors. In this study, injury severity is classified into three categories, i.e. fatal, serious, and minor injuries. Since the outcome of the injury severity could be measured on an ordinal scale, a discrete ordered outcome model, an ordered logit model is applied. To summarise the results, thirteen of the studied factors are found to have a significant influence on the injury severity of drivers. Results show that the likelihood of fatal injuries increases in crashes where motorcyclists hit stationary fixed objects, hit trees, ran-off road, inclement weather conditions, urban areas. It is also found that winter season, north districts of Tamilnadu, single and two-lane roads, highways, village roads and, other district roads, daylight conditions, drivers who are younger and working-age group, overtaking from left, taking u-turn are associated with less likelihood of fatal crashes. To increase the overall safety of the roads, targeted countermeasures may be designed in light of injury severity of the drivers with respect to single-vehicle crashes also. This study provides useful insights for reducing injury severity in single-vehicle motorcycle crashes.

Estimation of safety performance functions for urban intersections using various functional forms of the negative binomial regression model and a generalized Poisson regression model

Intersections are established dangerous entities of a highway system due to the challenging and unsafe roadway environment they are characterized for drivers and other road users. In efforts to improve safety, an enormous interest has been shown in developing statistical models for intersection crash prediction and explanation. The selection of an adequate form of the statistical model is of great importance for the accurate estimation of crash frequency and the correct identification of crash contributing factors. Using a six-year crash data, road infrastructure and geometric design data, and traffic flow data of urban intersections, we applied three different functional forms of negative binomial models (i.e., NB-1, NB-2, NB-P) and a generalized Poisson (GP) model to develop safety performance functions (SPF) by crash severity for signalized and unsignalized intersections. This paper presents the relationships found between the explanatory variables and the expected crash frequency. It reports the comparison of different models for total, injury & fatal, and property damage only crashes in order to obtain ones with the maximum estimation accuracy. The comparison of models was based on the goodness of fit and the prediction performance measures. The fitted models showed that the traffic flow and several variables related to road infrastructure and geometric design significantly influence the intersection crash frequency. Further, the goodness of fit and the prediction performance measures revealed that the NB-P model outperformed other models in most crash severity levels for signalized intersections. For the unsignalized intersections, the GP model was the best performing model. When only the NB models were compared, the functional form NB-P performed better than the traditional NB-1 and, more specifically, the NB-2 models. In conclusion, our findings suggest a potential improvement in the estimation accuracy of the SPFs for urban intersections by applying the NB-P and GP models.

A Motorcyclist-Injury Severity Analysis: A Comparison of Single-, Two-, and Multi-Vehicle Crashes Using Latent Class Ordered Probit Model

Motorcycle crashes increasingly become a high proportion of the overall motorized vehicle fatalities. However, limited research has been conducted to compare the injury severity of single-, two- and multi-vehicle crashes involving a motorcycle. This study aims to investigate the effects of rider characteristics, road conditions, pre-crash situations, and crash features on motorcycle severities with respect to different numbers of vehicles involved. The crash data used was obtained through a comprehensive Motorcycle Crash Causation Study (MCCS) by the Federal Highway Administration. An anatomic injury severity indicator, the New Injury Severity Score (NISS), is utilized to calculate a total score as the sum of squared the abbreviated injury scale scores of each of the rider's three most severe injuries. A hybrid approach integrating Latent Class Clustering (LCC) and Ordered Probit (OP) models was used to uncover the unobserved heterogeneity and to explore the major factors which significantly affect the injury severities resulting from single-, two- and multi-vehicle crashes involving a motorcycle. The results show that the significant differences in severity exist between different numbers of vehicles involved. More importantly, they also indicate dividing motorcycle crashes into homogeneous classes before modelling helps to discover insightful information. Pre-speed of the motorcycle is found to be a main factor associated with serious and critical injuries in most types of crashes. Findings of the study provide specific and insightful countermeasures targeting at the contributing factors of motorcycle crashes.

Traffic volume and crashes and how crash and road characteristics affect their relationship - A meta-analysis

The present study has investigated the relationship between traffic volume and crash numbers by means of meta-analysis, based on 521 crash prediction models from 118 studies. The weighted pooled volume coefficient for all crashes and all levels of crash severity (excluding fatal crashes) is 0.875. The most important moderator variable is crash type. Pooled volume coefficients are systematically greater for multi vehicle crashes (1.210) than for single vehicle crashes (0.552). Regarding crash severity, the results indicate that volume coefficients are smaller for more fatal crashes (0.777 for all fatal crashes) than for injury crashes but no systematic differences were found between volume coefficients for injury and property-damage-only crashes. At higher levels of volume and on divided roads, volume coefficients tend to be greater than at lower levels of volume and on undivided roads. This is consistent with the finding that freeways on average have greater volume coefficients than other types of road and that two-lane roads are the road type with the smallest average volume coefficients. The results indicate that results from crash prediction models are likely to be more precise when crashes are disaggregated by crash type, crash severity, and road type. Disaggregating models by volume level and distinguishing between divided and undivided roads may also improve the precision of the results. The results indicate further that crash prediction models may be misleading if they are used to predict crash numbers on roads that differ from those that were used for model development with respect to composition of crash types, share of fatal or serious injury crashes, road types, and volume levels.

A comparison between Artificial Neural Network and Hybrid Intelligent Genetic Algorithm in predicting the severity of fixed object crashes among elderly drivers

Run-off-road (ROR) crashes have always been a major concern as this type of crash is usually associated with a considerable number of serious injury and fatal crashes. A substantial portion of ROR fatalities occur in collisions with fixed objects at the roadside. Thus, this study seeks to investigate the severity of ROR crashes where elderly drivers, aged 65 years or more, hit a fixed object. The reason why the present study investigates this issue among older drivers is that, comparing to younger drivers, this age group of drivers have different psychological and physical features. Because of these differences, they are more likely to get injured in ROR types of crashes. This paper applies two types of Artificial Intelligence (AI) techniques, including hybrid Intelligent Genetic Algorithm and Artificial Neural Network (ANN) using the crashe information of California in 2012 obtained from Highway Safety Information System (HSIS) database. Although the results showed that the developed ANN outperformed the hybrid Intelligent Genetic Algorithm, the hybrid approach was more capable of predicting high-severity crashes. This is rooted in the way the hybrid model was trained by taking advantage of the Genetic Algorithm (GA). The results also indicated that the light condition has been the most significant parameter in evaluating the level of severity associated with fixed object crashes among elderly drivers, which is followed by the existence of the right and left shoulders. Following these three contributing factors, cause of collision, Average Annual Daily Traffic (AADT), number of involved vehicles, age, road surface condition, and gender have been identified as the most important variables in the developed ANN, respectively. This helps to identify gaps and improve public safety towards improving the overall highway safety situation of older drivers.

Investigating the injury severity of single-vehicle truck crashes in a developing country

Trucking plays a vital role in economic development in every country, especially countries where it serves as the backbone of the economy. The fast growth of economy in Iran as a developing country has also been accompanied by an alarming situation in terms of fatalities in truck-involved crashes, among the drivers and passengers of the trucks as well as the other vehicles involved. Despite the sizable efforts to investigate the truck-involved crashes, very little is known about the safety of truck movements in developing countries, and about the single-truck crashes worldwide. Thus, this study aims to uncover significant factors associated with injury severities sustained by truck drivers in single-vehicle truck crashes in Iran. The explanatory factors tested in the models include the characteristics of drivers, vehicles, and roadways. A random threshold random parameters hierarchical ordered probit model is utilized to consider heterogeneity across observations. Several variables turned out to be significant in the model, including driver's education, advanced braking system deployment, presence of curves on roadways, and high speed-limit. Using those results, we propose safety countermeasures in three categories of 1) educational, 2) technological, and 3) road engineering to mitigate the severity of single-vehicle truck crashes.

Identification of differential risk hotspots for collision and vehicle type in a directed linear network

Traffic accidents can take place in very different ways and involve a substantially distinct number and types of vehicles. Thus, it is of interest to know which parts of a road structure present an overrepresentation of a specific type of traffic accident, specially for some typologies of collisions and vehicles that tend to trigger more severe consequences for the users being involved. In this study, a spatial approach is followed to estimate the risk that different types of collisions and vehicles present in the central area of Valencia (Spain), considering the accidents observed in this city during the period 2014-2017. A directed spatial linear network representing the non-pedestrian road structure of the area of interest was employed to guarantee an accurate analysis of the point pattern. A kernel density estimation technique was used to approximate the probability of risk along the network for each collision and vehicle type. A procedure based on these estimates and the sample size locally available within the network was designed and tested to determine a set of differential risk hotspots for each typology of accident considered. A Monte Carlo based simulation process was then defined to assess the statistical significance of each of the differential risk hotspots found, allowing the elaboration of rankings of importance and the possible rejection of the least significant ones.

Freeway single and multi-vehicle crash safety analysis: Influencing factors and hotspots

Single-vehicle (SV) and multi-vehicle (MV) crashes have been recognized as differing in spatial distribution and influencing factors, but little consideration has been given to these differences as related to hotspot identification. For the purpose of better hotspot identification, this study aims to analyze influencing factors of SV and MV crashes and to explore the consistency between SV and MV hotspots. Crash data, roadway geometric design features, and traffic characteristics were collected along the two directions of a 45-km freeway section in Shanghai, China. Univariate negative binomial conditional autoregressive (NB-CAR) and bivariate negative binomial spatial conditional autoregressive (BNB-CAR) models were developed to analyze the influencing factors and specifically address (1) site correlation between SV and MV crashes within the same freeway segment, and (2) spatial correlation among different freeway segments within the same direction. The modeling results showed substantial differences in the significant factors that influence SV and MV crashes, including both roadway geometric features and traffic operational factors. A non-negligible site correlation was found between SV and MV crashes. Taking into account the site correlation, the BNB-CAR model outperformed the NB-CAR model in terms of parameter estimation and model fitting. For hotspot identification, potential for safety improvement based on the empirical Bayes method was adopted to handle the crash fluctuation problem. Substantial inconsistency was found between SV and MV hotspots despite the site correlation: in the top ten hotspots, no hotspot was shared by the two crash types. This result highlights the importance of differentiating SV and MV crashes when identifying hotspots, providing insight into freeway safety analysis.

Incorporating behavioral variables into crash count prediction by severity: A multivariate multiple risk source approach

The frequency and severity of traffic crashes have commonly been used as indicators of crash risk on transport networks. Comprehensive modeling of crash risk should account for both frequency and injury severity-capturing both the extent and intensity of transport risk for designing effective safety improvement programs. Previous research has revealed that crashes are correlated across severity categories because of the combined influence of risk factors, observed or unobserved. Moreover, crashes are the outcomes of a multitude of factors related to roadway design, traffic operations, pavement conditions, driver behavior, human factors, and environmental characteristics, or in more general terms: factors reflect both engineering and non-engineering risk sources. Perhaps not surprisingly, engineering risk sources have dominated the list of variables in the mainstream modeling of crashes whereas non-engineering sources, in particular, behavioral factors, are crucially omitted. It is plausible to assume that crash contributing factors from the same risk source affect crashes in a similar manner, but their influences vary across different risk sources. Conventional crash frequency modeling hypothesizes that the total crash count at any roadway site is well-approximated by a single risk source to which several explanatory variables contribute collaboratively. The conventional formulation is not capable of accounting for variations between risk sources; therefore, is unable to discriminate distinct impacts between engineering variables and non-engineering variables. To address this shortcoming, this study contributes to the development of multivariate multiple risk source regression, a robust modeling technique to model crash frequency and severity simultaneously. The multivariate multiple risk source regression method applied in this study can effectively capture the correlation between severity levels of crash counts while identifyinging the varying effects of crash contributing factors originated from distinct sources. Using crashes on Wisconsin rural two-lane highways, two risk sources - engineering and behavioral - were employed to develop proposed models. The modeling results were compared with a single equation negative binomial (NB) model, and a univariate multiple risk source model. The results show that the multivariate multiple risk source model significantly outperforms the other models in terms of statistical fit across several measures. The study demonstrates a unique approach to explicitly incorporating behavioral factors into crash prediction models while taking crash severity into consideration. More importantly, the parameter estimates provide more insight into the distinct sources of crash risk, which can be used to further inform safety practitioners and guide roadway improvement programs.

Functional forms of the negative binomial models in safety performance functions for rural two-lane intersections

Safety Performance Functions (SPFs) play a prominent role in estimating intersection crashes, and identifying the sites with the highest potential for safety improvement. To maximize the crash prediction accuracy, this paper describes the application of different functional forms of the Negative Binomial (NB) models (i.e. NB-1, NB-2 and NB-P) in estimating safety performance functions by crash type for three types of rural two-lane intersections, including three-leg stop-controlled (3ST) intersections, four-leg stop-controlled (4ST) intersections and four-leg signalized (4SG) intersections. Crash types were aggregated into same-direction, opposite-direction, intersecting-direction and single-vehicle crashes. Major and minor road Annual Average Daily Traffic (AADT) were used as predictors in the SPF estimation. In addition, major and minor road AADT were also used as predictors in the estimation of the over-dispersion parameter of the NB models to account for the crash data heterogeneity. In the end, all NB models were compared based on both the model estimation goodness-of-fit and the prediction performance. The model goodness-of-fit indicates that the NB-P model outperforms the NB-1 and NB-2 models for most crash types and intersection types, by providing a flexible variance structure to the NB approaches. The parameterization of the over-dispersion factor verifies that the over-dispersion parameter of the NB models highly depends on how the variance structure is defined in the model, and the over-dispersion parameter is shown to vary among different intersections for each crash type and can be estimated using both the major and minor road AADT at rural two-lane intersections. The NB-P model is found to more effectively capture the variation of over-dispersion among intersections in NB models, which benefits the accommodation of data heterogeneity in intersection SPF development. The prediction performance comparison illustrates that the NB-P model slightly improves the crash prediction accuracy compared with the other two models, especially for the 3ST and 4SG intersections. In conclusion, the NB-P model with parameterized over-dispersion factor is recommended to provide more unbiased parameter estimates when estimating SPFs by crash type for rural two-lane intersections.

Injury severity analysis of commercially-licensed drivers in single-vehicle crashes: Accounting for unobserved heterogeneity and age group differences

This study analyzes the injury severity of commercially-licensed drivers involved in single-vehicle crashes. Considering the discrete ordinal nature of injury severity data, the ordered response modeling framework was adopted. The moderating effect of driver's age on all other factors was examined by segmenting the parameters by driver's age group. Additional effects of the different drivers' age groups are taken into consideration through interaction terms. Unobserved heterogeneity of the different covariates was investigated using the Mixed Generalized Ordered Response Probit (MGORP) model. The empirical analysis was conducted using four years of the Highway Safety Information System (HSIS) data that included 6247 commercially-licensed drivers involved in single-vehicle crashes in the state of Minnesota. The MGORP model elasticity effects indicate that key factors that increase the likelihood of severe crashes for commercially-licensed drivers across all age groups include: lack of seatbelt usage, collision with a fixed object, speeding, vehicle age of 11 years or more, wind, night time, weekday, and female drivers. Also, the effects of several covariates were found to vary across different age groups.

Multivariate poisson lognormal modeling of crashes by type and severity on rural two lane highways

In an effort to improve traffic safety, there has been considerable interest in estimating crash prediction models and identifying factors contributing to crashes. To account for crash frequency variations among crash types and severities, crash prediction models have been estimated by type and severity. The univariate crash count models have been used by researchers to estimate crashes by crash type or severity, in which the crash counts by type or severity are assumed to be independent of one another and modelled separately. When considering crash types and severities simultaneously, this may neglect the potential correlations between crash counts due to the presence of shared unobserved factors across crash types or severities for a specific roadway intersection or segment, and might lead to biased parameter estimation and reduce model accuracy. The focus on this study is to estimate crashes by both crash type and crash severity using the Integrated Nested Laplace Approximation (INLA) Multivariate Poisson Lognormal (MVPLN) model, and identify the different effects of contributing factors on different crash type and severity counts on rural two-lane highways. The INLA MVPLN model can simultaneously model crash counts by crash type and crash severity by accounting for the potential correlations among them and significantly decreases the computational time compared with a fully Bayesian fitting of the MVPLN model using Markov Chain Monte Carlo (MCMC) method. This paper describes estimation of MVPLN models for three-way stop controlled (3ST) intersections, four-way stop controlled (4ST) intersections, four-way signalized (4SG) intersections, and roadway segments on rural two-lane highways. Annual Average Daily traffic (AADT) and variables describing roadway conditions (including presence of lighting, presence of left-turn/right-turn lane, lane width and shoulder width) were used as predictors. A Univariate Poisson Lognormal (UPLN) was estimated by crash type and severity for each highway facility, and their prediction results are compared with the MVPLN model based on the Average Predicted Mean Absolute Error (APMAE) statistic. A UPLN model for total crashes was also estimated to compare the coefficients of contributing factors with the models that estimate crashes by crash type and severity. The model coefficient estimates show that the signs of coefficients for presence of left-turn lane, presence of right-turn lane, land width and speed limit are different across crash type or severity counts, which suggest that estimating crashes by crash type or severity might be more helpful in identifying crash contributing factors. The standard errors of covariates in the MVPLN model are slightly lower than the UPLN model when the covariates are statistically significant, and the crash counts by crash type and severity are significantly correlated. The model prediction comparisons illustrate that the MVPLN model outperforms the UPLN model in prediction accuracy. Therefore, when predicting crash counts by crash type and crash severity for rural two-lane highways, the MVPLN model should be considered to avoid estimation error and to account for the potential correlations among crash type counts and crash severity counts.

Truck crash severity in New York city: An investigation of the spatial and the time of day effects

This paper investigates the differences between single-vehicle and multi-vehicle truck crashes in New York City. The random parameter models take into account the time of day effect, the heterogeneous truck weight effect and other influencing factors such as crash characteristics, driver and vehicle characteristics, built environment factors and traffic volume attributes. Based on the results from the co-location quotient analysis, a spatial generalized ordered probit model is further developed to investigate the potential spatial dependency among single-vehicle truck crashes. The sample is drawn from the state maintained incident data, the publicly available Smart Location Data, and the BEST Practices Model (BPM) data from 2008 to 2012. The result shows that there exists a substantial difference between factors influencing single-vehicle and multi-vehicle truck crash severity. It also suggests that heterogeneity does exist in the truck weight, and it behaves differently in single-vehicle and multi-vehicle truck crashes. Furthermore, individual truck crashes are proved to be spatially dependent events for both single and multi-vehicle crashes. Last but not least, significant time of day effects were found for PM and night time slots, crashes that occurred in the afternoons and at nights were less severe in single-vehicle crashes, but more severe in multi-vehicle crashes.

Freight-train derailment rates for railroad safety and risk analysis

Derailments are the most common type of train accident in the United States. They cause damage to infrastructure, rolling stock and lading, disrupt service, and have the potential to cause casualties, and harm the environment. Train safety and risk analysis relies on accurate assessment of derailment likelihood. Derailment rate - the number of derailments normalized by traffic exposure - is a useful statistic to estimate the likelihood of a derailment. Despite its importance, derailment rate analysis using multiple factors has not been previously developed. In this paper, we present an analysis of derailment rates on Class I railroad mainlines based on data from the U.S. Federal Railroad Administration and the major freight railroads. The point estimator and confidence interval of train and car derailment rates are developed by FRA track class, method of operation and annual traffic density. The analysis shows that signaled track with higher FRA track class and higher traffic density is associated with a lower derailment rate. The new accident rates have important implications for safety and risk management decisions, such as the routing of hazardous materials.

Driver injury severity outcome analysis in rural interstate highway crashes: a two-level Bayesian logistic regression interpretation

There is a high potential of severe injury outcomes in traffic crashes on rural interstate highways due to the significant amount of high speed traffic on these corridors. Hierarchical Bayesian models are capable of incorporating between-crash variance and within-crash correlations into traffic crash data analysis and are increasingly utilized in traffic crash severity analysis. This paper applies a hierarchical Bayesian logistic model to examine the significant factors at crash and vehicle/driver levels and their heterogeneous impacts on driver injury severity in rural interstate highway crashes. Analysis results indicate that the majority of the total variance is induced by the between-crash variance, showing the appropriateness of the utilized hierarchical modeling approach. Three crash-level variables and six vehicle/driver-level variables are found significant in predicting driver injury severities: road curve, maximum vehicle damage in a crash, number of vehicles in a crash, wet road surface, vehicle type, driver age, driver gender, driver seatbelt use and driver alcohol or drug involvement. Among these variables, road curve, functional and disabled vehicle damage in crash, single-vehicle crashes, female drivers, senior drivers, motorcycles and driver alcohol or drug involvement tend to increase the odds of drivers being incapably injured or killed in rural interstate crashes, while wet road surface, male drivers and driver seatbelt use are more likely to decrease the probability of severe driver injuries. The developed methodology and estimation results provide insightful understanding of the internal mechanism of rural interstate crashes and beneficial references for developing effective countermeasures for rural interstate crash prevention.

Examining driver injury severity outcomes in rural non-interstate roadway crashes using a hierarchical ordered logit model

Rural non-interstate crashes induce a significant amount of severe injuries and fatalities. Examination of such injury patterns and the associated contributing factors is of practical importance. Taking into account the ordinal nature of injury severity levels and the hierarchical feature of crash data, this study employs a hierarchical ordered logit model to examine the significant factors in predicting driver injury severities in rural non-interstate crashes based on two-year New Mexico crash records. Bayesian inference is utilized in model estimation procedure and 95% Bayesian Credible Interval (BCI) is applied to testing variable significance. An ordinary ordered logit model omitting the between-crash variance effect is evaluated as well for model performance comparison. Results indicate that the model employed in this study outperforms ordinary ordered logit model in model fit and parameter estimation. Variables regarding crash features, environment conditions, and driver and vehicle characteristics are found to have significant influence on the predictions of driver injury severities in rural non-interstate crashes. Factors such as road segments far from intersection, wet road surface condition, collision with animals, heavy vehicle drivers, male drivers and driver seatbelt used tend to induce less severe driver injury outcomes than the factors such as multiple-vehicle crashes, severe vehicle damage in a crash, motorcyclists, females, senior drivers, driver with alcohol or drug impairment, and other major collision types. Research limitations regarding crash data and model assumptions are also discussed. Overall, this research provides reasonable results and insight in developing effective road safety measures for crash injury severity reduction and prevention.

An insight into the performance of road barriers - redistribution of barrier-relevant crashes

Unlike most of traffic safety treatments that prevent crashes, road barriers reduce the severity of crash outcomes by replacing crashes with a high risk of severe injury and fatality (such as median crossover head-on collisions or collisions with high-hazard objects) with less risky events (such as collisions with barriers). This "crash conversion" is actually more complex than one-to-one replacement and it has not been studied yet. The published work estimated the reduction of selected types of crashes (typically, median crossover collisions) or the overall effect of barriers on crash severity. The objective of this study was to study the probabilities of various types of crash events possible under various road and barrier scenarios. The estimated probabilities are conditional given that at least one vehicle left the travelled way and the resulted crash had been recorded. The results are meant to deliver a useful insight onto the conversion of crashes by barriers from more to less risky to help better understand the mechanism of crash severity reduction. Such knowledge should allow engineers more accurate estimation of barriers' benefits and help researchers evaluate barriers' performance to improve the barrier's design. Seven barrier-relevant crash events possible after a vehicle departs the road could be identified based on the existing crash data and their probabilities estimated given the presence and location of three types of barriers: median concrete barriers, median and roadside W-beam steel guardrails, and high-tension median cable barriers. A multinomial logit model with variable outcomes was estimated based on 2049 barrier-relevant crashes occurred between 2003 and 2012 on 1258 unidirectional travelled ways in Indiana. The developed model allows calculating the changes in the probabilities of the barrier-relevant crash events. The results of this study indicated that road departures lead to less frequent crossings of unprotected (no barriers) medians 50-80ft. wide than for narrower medians 30-50ft wide. This benefit decreased with an increase in rollovers inside the median. Although our data indicated no median crossover events when a median barrier was present, the risk of crossovers, although low, is still present and could manifest itself if the sample were larger. The presence of barriers near a travelled way was associated with a higher risk of redirecting errant vehicles back to the roadway where they could collide with other vehicles continuing on the road. As expected, cable barriers installed on the far-side edge of a median were associated with a lower probability of being hit by errant vehicles and of redirecting vehicles into traffic than the nearside cable barriers. On the other hand, the probability of off-road non-barrier crashes was higher because vehicles penetrating the median from the unprotected side were exposed to median ditches and similar obstacles. The roadside guardrails were confirmed to reduce the percentage of hazardous off-road crashes. The results of this study facilitate a more transparent evaluation of the safety effect of road barriers.

Bootstrap resampling approach to disaggregate analysis of road crashes in Hong Kong

Road safety affects health and development worldwide; thus, it is essential to examine the factors that influence crashes and injuries. As the relationships between crashes, crash severity, and possible risk factors can vary depending on the type of collision, we attempt to develop separate prediction models for different crash types (i.e., single- versus multi-vehicle crashes and slight injury versus killed and serious injury crashes). Taking advantage of the availability of crash and traffic data disaggregated by time and space, it is possible to identify the factors that may contribute to crash risks in Hong Kong, including traffic flow, road design, and weather conditions. To remove the effects of excess zeros on prediction performance in a highly disaggregated crash prediction model, a bootstrap resampling method is applied. The results indicate that more accurate and reliable parameter estimates, with reduced standard errors, can be obtained with the use of a bootstrap resampling method. Results revealed that factors including rainfall, geometric design, traffic control, and temporal variations all determined the crash risk and crash severity. This helps to shed light on the development of remedial engineering and traffic management and control measures.

Analysis of driver injury severity in single-vehicle crashes on rural and urban roadways

This study analyzes driver injury severities for single-vehicle crashes occurring in rural and urban areas using data collected in New Mexico from 2010 to 2011. Nested logit models and mixed logit models are developed in order to account for the correlation between severity categories (No injury, Possible injury, Visible injury, Incapacitating injury and fatality) and individual heterogeneity among drivers. Various factors, such as crash and environment characteristics, geometric features, and driver behavior are examined in this study. Nested logit model and mixed logit model reveal similar results in terms of identifying contributing factors for driver injury severities. In the analysis of urban crashes, only the nested logit model is presented since no random parameter is found in the mixed logit model. The results indicate that significant differences exist between factors contributing to driver injury severity in single-vehicle crashes in rural and urban areas. There are 5 variables found only significant in the rural model and six significant variables identified only in the urban crash model. These findings can help transportation agencies develop effective policies or appropriate strategies to reduce injury severity resulting from single-vehicle crashes.

An explanatory analysis of driver injury severity in rear-end crashes using a decision table/Naïve Bayes (DTNB) hybrid classifier

Rear-end crashes are a major type of traffic crashes in the U.S. Of practical necessity is a comprehensive examination of its mechanism that results in injuries and fatalities. Decision table (DT) and Naïve Bayes (NB) methods have both been used widely but separately for solving classification problems in multiple areas except for traffic safety research. Based on a two-year rear-end crash dataset, this paper applies a decision table/Naïve Bayes (DTNB) hybrid classifier to select the deterministic attributes and predict driver injury outcomes in rear-end crashes. The test results show that the hybrid classifier performs reasonably well, which was indicated by several performance evaluation measurements, such as accuracy, F-measure, ROC, and AUC. Fifteen significant attributes were found to be significant in predicting driver injury severities, including weather, lighting conditions, road geometry characteristics, driver behavior information, etc. The extracted decision rules demonstrate that heavy vehicle involvement, a comfortable traffic environment, inferior lighting conditions, two-lane rural roadways, vehicle disabled damage, and two-vehicle crashes would increase the likelihood of drivers sustaining fatal injuries. The research limitations on data size, data structure, and result presentation are also summarized. The applied methodology and estimation results provide insights for developing effective countermeasures to alleviate rear-end crash injury severities and improve traffic system safety performance.

Detecting unforgiving roadside contributors through the severity analysis of ran-off-road crashes

The objective of this paper is to study the contributors influencing ran-off-road (ROR) crash severities in a setting that has not been analysed in the literature, namely on freeways not designed according to the "forgiving roadside" concept. To accomplish the analysis, ROR crash data were collected on freeway road sections in Portugal and multinomial and mixed logit models were estimated using the driver injury and the most severely injured occupant as outcome variables. Our results are in line with previous findings reported in the literature on ROR crash severity in a number of distinct settings. Most importantly, this study shows the contribution of critical slopes and vehicle rollover towards fatal injuries and highlights the importance of introducing the "forgiving roadside" concept to mitigate ROR crash severity in Portuguese freeways. The study also indicates the importance of protecting errant vehicles particularly in horizontal curves, as these are linked with fatalities. Finally, the empirical findings from the developed models revealed problems in current Portuguese roadside design, especially with regards to criteria for forgiving slopes provision and warrants for safety barrier installation.

Mixed logit model-based driver injury severity investigations in single- and multi-vehicle crashes on rural two-lane highways

Crashes occurring on rural two-lane highways are more likely to result in severe driver incapacitating injuries and fatalities. In this study, mixed logit models are developed to analyze driver injury severities in single-vehicle (SV) and multi-vehicle (MV) crashes on rural two-lane highways in New Mexico from 2010 to 2011. A series of significant contributing factors in terms of driver behavior, weather conditions, environmental characteristics, roadway geometric features and traffic compositions, are identified and their impacts on injury severities are quantified for these two types of crashes, respectively. Elasticity analyses and transferability tests were conducted to better understand the models' specification and generality. The research findings indicate that there are significant differences in causal attributes determining driver injury severities between SV and MV crashes. For example, more severe driver injuries and fatalities can be observed in MV crashes when motorcycles or trucks are involved. Dark lighting conditions and dusty weather conditions are found to significantly increase MV crash injury severities. However, SV crashes demonstrate different characteristics influencing driver injury severities. For example, the probability of having severe injury outcomes is higher when vans are identified in SV crashes. Drivers' overtaking actions will significantly increase SV crash injury severities. Although some common attributes, such as alcohol impaired driving, are significant in both SV and MV crash severity models, their effects on different injury outcomes vary substantially. This study provides a better understanding of similarities and differences in significant contributing factors and their impacts on driver injury severities between SV and MV crashes on rural two-lane highways. It is also helpful to develop cost-effective solutions or appropriate injury prevention strategies for rural SV and MV crashes.

Investigating the relationship between run-off-the-road crash frequency and traffic flow through different functional forms

Crash prediction models play a major role in highway safety analysis. These models can be used for various purposes, such as predicting the number of road crashes or establishing relationships between these crashes and different covariates. However, the appropriate choice for the functional form of these models is generally not discussed in research literature on road safety. In case of run-off-the-road crashes, empirical evidence and logical considerations lead to conclusion that the relationship between expected frequency and traffic flow is not monotonously increasing.

Alcohol involvement and other risky driver behaviors: effects on crash initiation and crash severity

OBJECTIVE

Alcohol-involved drivers or those with blood alcohol concentrations greater than 0.00 percent have more frequent and more severe crashes than other drivers. Alcohol use, because it delays perception and response and impairs coordination, increases the risk of a crash. However, those using alcohol may take additional driving risks, which may also lead to crashes. This study was done to learn whether risks besides alcohol involvement contributed to crash initiation and whether crash severity increased with alcohol involvement or with those other risky behaviors.

METHODS

Data that represented nearly 1.4 million motor vehicle crashes were accessed from an NHTSA database. Analyses evaluated whether alcohol-involved driving was associated with other driving risks and whether driver alcohol involvement, alone or together with other risks, increased the likelihood of initiating a 2-vehicle crash or in the event of a crash or increased crash severity.

RESULTS

Alcohol-involved drivers were less likely to use seat belts, drove faster, and were more likely to be distracted than others. Those who initiated 2-vehicle crashes were more likely to be alcohol involved or to have taken other driving risks than others from the same crashes. Crash severity was significantly greater for alcohol-involved drivers than for other drivers, but severity increased further if additional risks were taken. Crashes involving only drivers who had not used alcohol were also sometimes severe, and that severity was associated with risky driving behaviors. When crashes involved 2 drivers, the behaviors of both affected crash severity.

CONCLUSIONS

Risky driving behaviors, including alcohol involvement, increased the risk of a crash. Crash severity tended to increase with any risky behavior and to increase further with multiple risky behaviors. Other risky behaviors were associated with both alcohol involvement and crashes. Therefore, if effects from those other risky behaviors were not accommodated for, those effects would confound apparent associations between alcohol involvement and crashes. Therefore, this study's use of multivariate models that accommodated for effects from those other behaviors provided a truer picture of alcohol's association with crashes than simpler models would have. Supplemental materials are available for this article. Go to the publisher's online edition of Traffic Injury Prevention to view the supplemental file.

Reprint of "Modeling two-vehicle crash severity by a bivariate generalized ordered probit approach"

This study simultaneously models crash severity of both parties in two-vehicle accidents at signalized intersections in Taipei City, Taiwan, using a novel bivariate generalized ordered probit (BGOP) model. Estimation results show that the BGOP model performs better than the conventional bivariate ordered probit (BOP) model in terms of goodness-of-fit indices and prediction accuracy and provides a better approach to identify the factors contributing to different severity levels. According to estimated parameters in latent propensity functions and elasticity effects, several key risk factors are identified-driver type (age>65), vehicle type (motorcycle), violation type (alcohol use), intersection type (three-leg and multiple-leg), collision type (rear ended), and lighting conditions (night and night without illumination). Corresponding countermeasures for these risk factors are proposed.

Comparing single vehicle and multivehicle fatal road crashes: a joint analysis of road conditions, time variables and driver characteristics

The difference between single vehicle crashes and multivehicle crashes was investigated in a collection of fatal crashes from six European countries. Variables with respect to road conditions, time variables, and participant characteristics were studied separately at first and then jointly in a logistic multiple regression model allowing to weigh different accounts of single vehicle as opposed to multivehicle crash occurrence. The most important variables to differentiate between single and multivehicle crashes were traffic flow, the presence of a junction and the presence of a physical division between carriageways. Heavy good vehicles and motorcycles were less likely to be involved in single vehicle crashes than cars. Moreover crashes of impaired drivers with more passengers were more likely to be single vehicle crashes than those of other drivers. Young drivers, rural roads, nights and weekends were all shown to have a higher proportion of single vehicle crashes but in the multivariate analysis these effects were demonstrated to be mediated by the road conditions named above.

Multi-level Bayesian analyses for single- and multi-vehicle freeway crashes

This study presents multi-level analyses for single- and multi-vehicle crashes on a mountainous freeway. Data from a 15-mile mountainous freeway section on I-70 were investigated. Both aggregate and disaggregate models for the two crash conditions were developed. Five years of crash data were used in the aggregate investigation, while the disaggregate models utilized one year of crash data along with real-time traffic and weather data. For the aggregate analyses, safety performance functions were developed for the purpose of revealing the contributing factors for each crash type. Two methodologies, a Bayesian bivariate Poisson-lognormal model and a Bayesian hierarchical Poisson model with correlated random effects, were estimated to simultaneously analyze the two crash conditions with consideration of possible correlations. Except for the factors related to geometric characteristics, two exposure parameters (annual average daily traffic and segment length) were included. Two different sets of significant explanatory and exposure variables were identified for the single-vehicle (SV) and multi-vehicle (MV) crashes. It was found that the Bayesian bivariate Poisson-lognormal model is superior to the Bayesian hierarchical Poisson model, the former with a substantially lower DIC and more significant variables. In addition to the aggregate analyses, microscopic real-time crash risk evaluation models were developed for the two crash conditions. Multi-level Bayesian logistic regression models were estimated with the random parameters accounting for seasonal variations, crash-unit-level diversity and segment-level random effects capturing unobserved heterogeneity caused by the geometric characteristics. The model results indicate that the effects of the selected variables on crash occurrence vary across seasons and crash units; and that geometric characteristic variables contribute to the segment variations: the more unobserved heterogeneity have been accounted, the better classification ability. Potential applications of the modeling results from both analysis approaches are discussed.

Modeling two-vehicle crash severity by a bivariate generalized ordered probit approach

This study simultaneously models crash severity of both parties in two-vehicle accidents at signalized intersections in Taipei City, Taiwan, using a novel bivariate generalized ordered probit (BGOP) model. Estimation results show that the BGOP model performs better than the conventional bivariate ordered probit (BOP) model in terms of goodness-of-fit indices and prediction accuracy and provides a better approach to identify the factors contributing to different severity levels. According to estimated parameters in latent propensity functions and elasticity effects, several key risk factors are identified-driver type (age>65), vehicle type (motorcycle), violation type (alcohol use), intersection type (three-leg and multiple-leg), collision type (rear ended), and lighting conditions (night and night without illumination). Corresponding countermeasures for these risk factors are proposed.

Bayesian random effect models incorporating real-time weather and traffic data to investigate mountainous freeway hazardous factors

Freeway crash occurrences are highly influenced by geometric characteristics, traffic status, weather conditions and drivers' behavior. For a mountainous freeway which suffers from adverse weather conditions, it is critical to incorporate real-time weather information and traffic data in the crash frequency study. In this paper, a Bayesian inference method was employed to model one year's crash data on I-70 in the state of Colorado. Real-time weather and traffic variables, along with geometric characteristics variables were evaluated in the models. Two scenarios were considered in this study, one seasonal and one crash type based case. For the methodology part, the Poisson model and two random effect models with a Bayesian inference method were employed and compared in this study. Deviance Information Criterion (DIC) was utilized as a comparison factor. The correlated random effect models outperformed the others. The results indicate that the weather condition variables, especially precipitation, play a key role in the crash occurrence models. The conclusions imply that different active traffic management strategies should be designed based on seasons, and single-vehicle crashes have different crash mechanism compared to multi-vehicle crashes.

A multivariate tobit analysis of highway accident-injury-severity rates

Relatively recent research has illustrated the potential that tobit regression has in studying factors that affect vehicle accident rates (accidents per distance traveled) on specific roadway segments. Tobit regression has been used because accident rates on specific roadway segments are continuous data that are left-censored at zero (they are censored because accidents may not be observed on all roadway segments during the period over which data are collected). This censoring may arise from a number of sources, one of which being the possibility that less severe crashes may be under-reported and thus may be less likely to appear in crash databases. Traditional tobit-regression analyses have dealt with the overall accident rate (all crashes regardless of injury severity), so the issue of censoring by the severity of crashes has not been addressed. However, a tobit-regression approach that considers accident rates by injury-severity level, such as the rate of no-injury, possible injury and injury accidents per distance traveled (as opposed to all accidents regardless of injury-severity), can potentially provide new insights, and address the possibility that censoring may vary by crash-injury severity. Using five-year data from highways in Washington State, this paper estimates a multivariate tobit model of accident-injury-severity rates that addresses the possibility of differential censoring across injury-severity levels, while also accounting for the possible contemporaneous error correlation resulting from commonly shared unobserved characteristics across roadway segments. The empirical results show that the multivariate tobit model outperforms its univariate counterpart, is practically equivalent to the multivariate negative binomial model, and has the potential to provide a fuller understanding of the factors determining accident-injury-severity rates on specific roadway segments.

Spatial and temporal visualisation techniques for crash analysis

Understanding the underlying structure of single vehicle crashes (SVCs) is essential for improving safety on the roads. Past research has found that SVCs tend to cluster both spatially and temporally. However, limited research has been conducted to investigate the interaction between the location of SVCs and the time they occur, especially at different levels of scales or spatial extents. This paper applied spatial, temporal and spatio-temporal techniques to investigate patterns of SVCs in Western Australia between 1999 and 2008, at different levels of scale. Spider graphs were adapted to identify temporal patterns of vehicle crashes at two different levels of scales: daily and weekly with respect to their causes. The spatial structures of vehicle crashes were analysed using Kernel Density Estimation analysis at three different scales: West Australia, Metropolitan area, and Perth Local Government Area (LGA). These are illustrated using spatial zooming theory. Comap was then used to demonstrate the spatio-temporal interaction effect on vehicle crashes. The results show significant differences in spatio-temporal patterns of SVCs for various crash causes. The techniques used here have the potential to help decision makers in developing effective road safety strategies.

Injury severities of truck drivers in single- and multi-vehicle accidents on rural highways

In adverse driving conditions, such as inclement weather and/or complex terrain, trucks are often involved in single-vehicle (SV) accidents in addition to multi-vehicle (MV) accidents. Ten-year accident data involving trucks on rural highway from the Highway Safety Information System (HSIS) is studied to investigate the difference in driver-injury severity between SV and MV accidents by using mixed logit models. Injury severity from SV and MV accidents involving trucks on rural highways is modeled separately and their respective critical risk factors such as driver, vehicle, temporal, roadway, environmental and accident characteristics are evaluated. It is found that there exists substantial difference between the impacts from a variety of variables on the driver-injury severity in MV and SV accidents. By conducting the injury severity study for MV and SV accidents involving trucks separately, some new or more comprehensive observations, which have not been covered in the existing studies can be made. Estimation findings indicate that the snow road surface and light traffic indicators will be better modeled as random parameters in SV and MV models respectively. As a result, the complex interactions of various variables and the nature of truck-driver injury are able to be disclosed in a better way. Based on the improved understanding on the injury severity of truck drivers from truck-involved accidents, it is expected that more rational and effective injury prevention strategy may be developed for truck drivers under different driving conditions in the future.