Analytic Methods in Accident Research最新文献

A copula-based dependence approach accommodates various facets of dependence structures in building multivariate stochastic models. In existing studies, applications of copula for ordinal random variables are predominantly modeled by employing traditional ordered models (ordered logit/probit) while assuming the effects of parameters to remain the same across all observations. The methodological contributions of this study are grounded in addressing the abovementioned significant methodological gaps in the application of copula formulation by proposing a copula-based random parameters nominal-ordinal joint model construct of correlated random variables. Specifically, we propose and develop a random parameters binary logit-generalized ordered logit copula formulation while also complementing the proposed approach by accommodating the effects of unobserved heterogeneity in parameter estimates. To the best of the authors’ knowledge, this study is the first instance to incorporate generalized ordered formulation within copula in extant econometrics literature. Further, to obtain a direct effect of exogenous variables on dependence, we parameterize the copula dependence structure as a function of different covariates in six different copula structures including a wide range of dependency structures which represent radial symmetry and asymmetry, and asymptotic tail dependence. The empirical contributions of this study are grounded in the application of the proposed copula-based formulation by examining ‘active traveler (pedestrian and bicyclist) crash type’ and ‘active traveler injury severity outcomes’ as two dimensions of active travel injury severity mechanism. The model is estimated by using crash data for the years 2012 through 2018 from the state of Queensland, Australia, by employing a comprehensive set of exogenous variables. In addition, the analyses are further augmented by complementing the elasticity effects of exogenous variables.

In conventional safety analysis, traffic and crash data are often aggregated at the geographical units like census tracts, street blocks, and traffic analysis zones, which are often delineated by roads and other physical entities. A considerable proportion of crashes may occur at or near the boundary of geographical units. Such the crashes, also known as boundary crashes, can correlate with the explanatory variables of neighboring geographical units, regardless of the spatial proximity. This could then bias the parameter estimation of crash frequency model. In this study, a novel data-driven approach is developed for the allocation of boundary crashes. For example, crash severity and bicyclist characteristics are considered in the crash feature-based allocation. An illustrative case study based on built environment, population, traffic and bicycle crash data from 289 Lower Layer Super Output Areas (LSOAs) of London in the period 2017–2019 was conducted. Results indicate that high matching percentages of boundary crash allocation can be achieved. Furthermore, prediction performances, in terms of root mean square error (RMSE) and mean absolute error (MAE), of the crash frequency models based on the proposed crash feature-based allocation method is superior, compared to that based on conventional boundary crash allocation methods like half-and-half and iterative assignment approaches. Last but not least, more influencing factors that affect the bicycle crash frequency at macroscopic level can be identified. Findings should be indicative to the spatial safety analysis for different geographical configurations.

Crashes at highway-rail grade crossings often result in higher proportion of injury and fatality of the vehicle occupants as compared to other crash types, necessitating in-depth investigation to identify their causal factors. In this study, injury-severity outcomes from highway-rail grade crossing crashes are analyzed using crash data from Texas and California, which are the most vulnerable states in the United States, in terms of highway-rail grade crossing crash occurrences. The data are collected from the Federal Railroad Administration’s (FRA) Office of Safety Analysis, covering a period between 2012 and 2020. Such data often suffer from out-of-date or missing information due to cost and available resources limitations, which inevitably may lead to unobserved characteristics varying systematically across various aspects of the data. Unobserved heterogeneity is an important misspecification issue, that in turn introduces modeling bias. To address these limitations, the random parameters multinomial logit modeling framework with heterogeneity in the means and variances is employed for the econometric analysis in this paper, which effectively accounts for multilayered unobserved heterogeneity. Spatial instability of the factors affecting different injury-severity levels is investigated as well. The results indicate that the factors are not spatially stable across Texas and California, leading to the estimation of two separate state-specific models. The estimation results of the two state-specific models help identify several vehicle-, train-, vehicle driver-, weather- and crossing-specific factors affecting different injury severity outcomes. Moreover, the results also demonstrate the varying magnitude of the identified factors on injury-severity across the two states, indicating the presence of spatial instability. The findings of this study highlight the importance of accounting for unobserved heterogeneity and spatial instability to avert critical methodological issues and misleading inferences from the simple aggregation used in most econometric analysis of highway-rail grade crossing crashes.

A novel and effective approach to safety management requires evaluating the safety of locations over short time periods (e.g. minutes). Unlike traditional methods that are based on aggregate crash records over a few years, crash proneness in this approach reflects short-time durations and is related to dynamic traffic changes and dangerous driving events. This paper proposes a new approach to dynamically assess the crash proneness of traffic conditions within a very short time (e.g., signal cycle length) and to dynamically identify high-risk locations. Using a Bayesian hierarchal Extreme Value Theory (EVT) model, the short-term crash risk metrics, risk of crash (ROC), and return level (RL), are calculated using traffic conflict data. A short-term hazardous location identification and ranking framework is developed based on crash-risk threshold exceedances for every short-term analysis period. By further investigating the variation in short-term crash risk, longer-term hazardous location identification and ranking metrics such as the longer-term crash risk index (LTCRI) and the percent of time exceeding (PTE) were developed. Using these metrics, a framework is proposed by which hazardous intersections can be dynamically classified and ranked in both the short-term and the longer-term. This ranking may be dynamically updated as more data becomes available. The proposed framework was applied to a trajectory dataset consisting of 47 signalized intersections obtained from a UAV-based dataset. Conflicts were identified from vehicle trajectories and were used to compute the proposed short-term and longer-term metrics. The intersections within the network were then ranked based on the proposed framework. This study demonstrates the importance of investigating short-term fluctuations in crash risk that may otherwise be lost to averaging in longer-term analysis and proposes a simple and practical solution.

The current study contributes to safety literature by incorporating the influence of temporal factors (observed and unobserved) within a multivariate model system for medical professional generated body region specific injury severity score. For this purpose, we adopt a hybrid econometric modeling approach that accommodates for the unobserved factors using two mechanisms. First, we parameterize unobserved temporal factor variation through the customization of the variance by time cohort (heteroscedasticity). Second, the common unobserved factors affecting severity across various body regions is accommodated through traditional random parameter consideration process. The proposed model system is estimated using data drawn from the National Automotive Sampling System-Crashworthiness Data System (NASS-CDS) database for the time cohorts 2003, 2006, 2009, 2012, and 2015. For the current analysis, we consider 6-point Abbreviated Injury Scale (AIS) for eight body regions (head, face, neck, abdomen, thorax, spine, lower extremity, and upper extremity). The proposed model system offers interesting insights on body region severity evolution over time. The model estimation is augmented with post-estimation exercises including hold-out sample validation analysis, illustrative policy analysis and extensive elasticity effect computation.

This paper presents a negative binomial random parameter model with heterogeneity in means and variance to capture the effect of heterogeneous effect of ramp type, alignment, truck volume and interchange geometry and on freeway ramp crash frequency. Two years (2018–2019) of crash data on freeway ramps in Washington State were analyzed. Model estimation results show ramp type (directional, semi-directional and loop), alignment, and traffic characteristics significantly impact ramp crash frequency. The northwest loop ramp indicator has a random parameter. The minimum horizontal curve radius and the total number of vertical curves on the ramp appear to be statistically significant sources of heterogeneity in the mean of this parameter. Heterogeneity in the mean of the random effect is influenced by single truck percentage and the low AADT indicator (<=1,340 vehicles per day).

Heterogeneity in the variance of the northwest loop ramp random parameter appears to be associated with the southwest loop ramp indicator indicating unobserved effects due to same-side loop geometries.

Directional ramp indicators (on- and off-ramps) and interactions involving speed limit, AADT and horizontal curve radius are statistically significant (as fixed parameters) in their impact on ramp crash frequency.

Total centerline mile footprint of all ramps at the interchange is a continuous fixed parameter effect. Ramp-specific lengths (longer than 0.335 miles) also appear to be statistically significant. The findings in this study suggest that ramp and interchange design need to account for a holistic integration of spatial footprint, type of ramp and alignment factors, in addition to traffic flow variables.

Developing robust and reliable statistical models to estimate, analyze, and understand crash data is a key element in various highway safety evaluation tasks. Crash data have characteristics not found in other data, including but not limited to the excess number of zero responses. The Negative Binomial-Lindley (NB-L) model has been proposed as a method to analyze data with many zero observations. In addition, the differences in various temporal and spatial factors result in variations of model coefficients among different groups of observations. A grouped random parameters model is a strategy to account for such unobserved heterogeneity. In this paper, we proposed the derivations and applications of the grouped random parameters negative binomial-Lindley model (G-RPNB-L) to account for the unobserved heterogeneity in crash data with many zero observations. We first illustrated our proposed model by designing a simulation study. The simulation study showed the ability of the proposed model to correctly estimate the coefficients. Then, we used an empirical dataset in Maine to show the application of the proposed model. We showed that the impact of weather variables denoting “Days with precipitation greater than 1.0 in.”, and “Days with temperature less than 32°F” varies across Maine counties. We also compared the proposed model with the NB, NB-L, and grouped random-parameters NB (G-RPNB) models using different goodness-of-fit metrics. The proposed G-RPNB-L model showed a superior fit compared to the other models.

The rapid technological advancements in video analytics and the availability of big data have made traffic conflict techniques a viable tool for road safety assessments. They can potentially overcome many major limitations of conventional road safety practices that use crash-data analyses. However, the current traffic conflict techniques flag serious concerns regarding the context-dependence of the relationship between traffic conflicts and crashes, the lack of consideration of road user and vehicle heterogeneities in their formulation, and the exclusion of crash severity estimation from the analysis process. To overcome these limitations, this study proposes a novel application of the safety field theory to estimate crash risk and severity by modeling the safety-aware interactions of various road users in a road traffic environment. The safety field theory borrows from the Physics concept of electromagnetic fields to mathematically define the safety “buffers” that road users typically maintain around them while moving in traffic. Additionally, the model formulation explicitly accounts for exceptional circumstances (crashes and extreme conflicts) and integrates severity in the risk estimation framework to provide a holistic safety assessment framework. The proposed safety field theory application was tested by analyzing a total of 196 h of traffic movement videos collected from three signalized intersections in Brisbane, Australia and extracting the required road user trajectory information through artificial intelligence-based video analytics. Extreme value modeling of the tail distribution of the risk force generated by the interacting road user safety fields showed that it could predict the crash frequency and outcome severity more accurately than the prevalent traffic conflict indicators. Thus, the proposed approach provides a single, unified, and efficient method of accurately estimating crash risk and injury severities that can be adapted for various application contexts. The study results significantly improve the effectiveness of automated safety analysis for transport facilities and could elevate the safety prediction algorithms of real-time applications like adaptive signal control systems and Connected and Automated Vehicles.

In this study, a speed-route joint choice model considering traveler’s safety concerns is proposed to concurrently model traveler’s safety-oriented travel speed and route choice behavior. Specifically, the safe-speed choice behavior is modeled as a trade-off process between perceived traffic safety and efficiency using a disutility function. The safe-route choice behavior is described by the proposed Mean-excess Crash Risk Cost model, where the route safety is modeled as a random variable following a specific distribution, and traveler’s concerns about both reliability and unreliability aspects of safety variability are considered. The model is accommodative to account for the random nature and the traveler’s perception of traffic safety. Also, the travel time cost is considered, which is depicted as a parallel criterion of travel safety in the route choice model. Moreover, the heterogeneities of travelers’ safety concerns in both the choices of speed and route are considered in the proposed joint model. Then, the study formulated the equilibrium problem with the two behavior elements (speed and route) and two choice criteria (safety and time), based on the assumption that all travelers tend to maximize their disutility when choosing speed while minimizing their travel safety variability and travel time. To illustrate the model, Nguyen and Dupuis, Sioux falls, and Changsha arterial networks are conducted as numerical studies. The result demonstrates the model’s capability in depicting travelers’ trade-off between safety and time when selecting the optimal travel speed. Considering the impact of route safety unreliability makes the model sensible to describe travelers’ safety-concerned route choice behavior. The model is also flexible to account for travelers’ crash risk aversion heterogeneity.

Motorcyclists’ at-fault status is an important factor influencing crash injury severity in that intrinsically unsafe riders tend to be at fault and are the ones likely to be involved in severe crashes. However, this endogeneity issue and its influence on model estimations have seldom been investigated with regard to motorcyclist crash severity analysis. This study proposes a simultaneous model system to account for the endogenous effects of at-fault status in the motorcyclists’ injury severity analysis. Four Bayesian simultaneous models were developed using motorcyclist crash injury data from Queensland, Australia, from the year 2017 through 2018, including an independent binary and independent ordered Probit model, a simultaneous binary-ordered Probit model without recursive structure, a simultaneous binary-ordered Probit model with a recursive structure, and a simultaneous random-parameters binary-ordered Probit model with a recursive structure. The results of all simultaneous models indicate the existence of endogeneity associated with at-fault status in the injury outcome analysis. In particular, the endogenous relationship is detected by the significant cross-equation correlations in the simultaneous models. The model comparison by Deviance Information Criteria highlights the superiority of the simultaneous random-parameters model with a recursive structure. It was found that exogenous variables such as traffic sign-controlled measures, posted speed limits of 100–110 km/h, the presence of vertical grades, rider age 16–24 years, and unlicensed influenced injury severity indirectly through at-fault status, and ignoring these indirect influences could result in biased estimates. The presence of random parameters, such as collisions with heavy vehicles and riders over 59 years, highlights the importance of considering heterogeneity. The simultaneous random-parameters model with a recursive structure model revealed that the critical factors contributing to riders’ at-fault status included unlicensed riders and posted speed limits of 100–110 km/h, and the crucial factors influencing riders’ injury levels included head-on crashes, collisions with heavy vehicles, darkness-unlighted, and riders over 59 years old. The proposed model system demonstrates the importance of considering both endogeneity and heterogeneity for modeling the injury severity of motorcyclists.