Analytic Methods in Accident Research最新文献

In recent years, US States have raised their maximum interstate speed limits from 70 mi/h to 75 mi/h, 80 mi/h and even 85 mi/h. However, understanding the effect that these higher speed limits have had on the frequency and severity of crashes using traditional before and after analyses has been difficult due to possible temporal shifts in driver behavior, and potential changes in vehicle safety technology and highway safety features. Using multi-year data from before and after higher speed limits were instituted on Kansas freeways, random parameters models of crash frequency and resulting injury severity were estimated. Regarding the frequency of crashes, the findings showed that the higher speed limits did not have a significant effect in the mean number of crashes on the 253 studied roadway segments. For injury severity, model-estimation results in one- and two-vehicle crashes show that the factors affecting driver-injury severities have changed before and after the speed limit increase, but changes were also observed in the years before the speed limit increases and the years after. However, using pre-speed-limit-increase model estimation results to predict post-speed-limit-increase injury-severity distributions it was found that the aggregate effect of the changing influences of explanatory variables on average injury severities was relatively small. While the injury-severity estimation results make it difficult to attribute any temporal shifts in parameter values to the increased speed limit, there was a significant increase in the probability of rollover crashes that suggests the higher speed limits may have had some contributory effect on injury severities in single-vehicle crashes.

This study explores temporal shifts in the effects of explanatory variables on the injury severity outcomes of crashes involving distracted driving. Using data from distracted driving crashes on Kansas State highways over a four-year period (from 2014 to 2017 inclusive), separate yearly models of driver-injury severities (with possible outcomes of severe injury, minor injury, and no injury) were estimated using two alternate modeling approaches to account for possible unobserved heterogeneity: a latent-class multinomial logit with class probability functions and a random parameters logit with possible heterogeneity in the means and variances of random parameters. Likelihood ratio tests were conducted to determine if model parameter estimates have shifted over time. A wide range of variables were found to statistically influence driver-injury severities and the findings show that were statistically significant temporal shifts in parameter estimates in both the random parameters and latent class modeling approaches. These shifts are likely the result of changes in driver behavior, improvements in vehicle and highway safety features, changes in communication technologies, and other temporally shifting trends. However, while out-of-sample simulations show that the two modeling approaches both indicate that distracted driving crashes have become less severe over time, the alternate approaches produced substantially different injury-severity predictions, suggesting the need for future research to explore how unobserved heterogeneity can best be modeled in temporal contexts.

The truck involvement could potentially increase the crash frequency and resulted injury outcomes and it is of great necessity to understand the similarities and differences in the mechanism of how determinants influence injury severities of truck-involved and non-truck-involved crashes and explore their spatiotemporal stability. Based on the crash data of Beijing-Shanghai Expressway and Changchun-Shenzhen Expressway over the three years (2017–2019), the heterogeneity and spatiotemporal stability of contributing factors affecting truck-involved and non-truck-involved crashes were investigated through random-parameter logit models with unobserved heterogeneity in means and variances. Three injury severity outcomes of severe injury, minor injury, and no injury were examined considering multiple factors including driver, vehicle, roadway, environmental, temporal, spatial, traffic and crash characteristics. Besides, the spatiotemporal stability was investigated based on the likelihood ratio tests. Marginal effects were also calculated to analyze the spatiotemporal stability and potential heterogeneity of the contributing variables from year to year. The findings exhibited remarkable differences between truck-involved and non-truck-involved crashes, and an overall spatiotemporal instability was observed in the current study while several indicators were also reported to show relative spatial or temporal stability such as length of the horizontal curve, AADT, early morning, cloudy weather. This paper provided some suggestions to prevent crashes for truck-involved and non-truck-involved crashes across different highways respectively and develop safety measures accordingly.

Real-time safety analysis and optimization using surrogate safety measures such as traffic conflicts and techniques such extreme value theory (EVT) models is an emerging research topic in the context of proactive traffic safety management. However, the predictive performance and temporal transferability of the existing real-time safety analysis EVT models are subject to the assumption of invariant model parameters, which do not account for the temporal variability and is not suitable for real-time traffic data analysis. This study proposes a Bayesian dynamic extreme value modeling approach for conflict-based real-time safety analysis which integrates a Bayesian dynamic linear model with the extreme value distribution. The proposed approach has several unique advantages as it: 1) allows the model parameters to be time-varying; 2) integrates the newer data with prior information to recursively update the model parameters and account for state-space changes and react to sudden trend changes; 3) accounts for temporal variability and non-stationarity in conflict extremes; and 4) quantitatively evaluates the real-time safety levels of a road facility. The proposed approach is applied for cycle-by-cycle safety analysis at four signalized intersections in the city of Surrey, British Columbia. Traffic conflicts are characterized by the modified time to collision indicator. Three traffic parameters (traffic volume, shock wave area, and platoon ratio) at the signal cycle level are considered as covariates to account for non-stationarity. Several Bayesian dynamic and static extreme value models are developed and two safety indices, namely risk of crash (RC) and return level (RL), are generated to quantitatively represent the cycle-level safety. The RC directly reflects whether a cycle is risky while the RL can evaluate the safety levels of individual cycles. The results show that the dynamic model can identify more crash-risk cycles with either a positive RC or a positive RL than the static model and is more capable of differentiating the safety levels for individual cycles in terms of RL. Overall, the dynamic model outperforms the static model in terms of the statistical fit and aggregate crash estimation accuracy.

The present paper introduces the time between the start of a snowfall and the occurrence of a motor vehicle accident as a novel measure for evaluating motor vehicle safety during snowfalls. Detailed information of accidents that occurred during snowfalls between 2017 and 2020 in the state of New York are used to explore the accelerating or delaying effect of different factors on the time between the start of a snowfall and the occurrence of an accident. To that end, the hazard-based duration modeling framework is employed, and to account for multiple layers of unobserved heterogeneity, a random parameters with heterogeneity in means and variances approach is introduced – for this first time, to the authors’ knowledge. The temporal stability of the factors across the study period is investigated through conducting a series of systematic likelihood ratio tests, and the factors are not found to be temporally stable across the study years. Hence, separate year-specific models are estimated. The results show that a number of factors affect the time between the start of a snowfall and the occurrence of a motor vehicle accident such as: visibility conditions; concrete road sections; road sections with high Pavement Condition Index (PCI); roads with more than 4 lanes in both directions; locations in close proximity to bus stations; the period during the cold winter months (specifically February); the amount of accumulated snow on the ground before snowfall; the presence of ramps; and long time intervals between snowfalls (especially for heavy snow conditions and adverse visibility conditions). The findings from this paper are anticipated to offer insights to winter maintenance teams, transportation system operators, and users regarding accident-prone periods and locations during snowfalls.

Random parameters logit models have become an increasingly popular method to investigate crash-injury severities in recent years. However, there remain potential elements of the approach that need clarification including out-of-sample prediction, the calculation of marginal effects, and temporal instability testing. In this study, four models are considered for comparison: a fixed parameters multinomial logit model; a random parameters logit model; a random parameters logit model with heterogeneity in means; and a random parameters logit model with heterogeneity in means and variances. A full simulation of random parameters is undertaken for out-of-sample injury-severity predictions, and the prediction accuracy of the estimated models was assessed. Results indicate, not surprisingly, that the random parameters logit model with heterogeneity in the means and variances outperformed other models in predictive performance. Following this, two alternative methods for computing marginal effects are considered: one using Monte Carlo simulation and the other using individual estimates of random parameters. The empirical results indicate that both methods produced defensible results since the full distributions of random parameters are considered. Finally, two testing alternatives for temporal instability are evaluated: a global test across all time periods being considered, and a pairwise time-period to time-period comparison. It is shown that the pairwise comparison can provide more detailed insights into possible temporal variability.

The current research contributes to the burgeoning literature on multivariate models by proposing a hybrid model framework that (a) incorporates unobserved heterogeneity in a parsimonious framework and (b) allows for additional flexibility to accommodate for observed/systematic heterogeneity. Specifically, we estimate a Latent Segmentation Panel Mixed Negative Binomial (LPMNB) model to study the zonal level crash counts across different crash types. Further, we undertake a comparison exercise of the proposed hybrid LPMNB model with a Panel Mixed Negative Binomial model (PMNB) that accommodates for unobserved heterogeneity via a simulation setting. The analysis is conducted using the zonal level crash records by different crash types from Central Florida region for the year 2016 considering a comprehensive set of exogenous variables. The comparison exercise is further augmented by computing several goodness of fit measures along with elasticity analysis and the results offered by the LPMNB model highlight the value of the proposed model. Further, to offer insights on model selection incorporating computational complexity dimension along with other important attributes, we conduct a trade-off analysis considering four different attributes: (a) model fit, (b) prediction, (c) inference power and (d) computational complexity; across six different model strictures including traditional crash frequency models and our proposed LPMNB model.

Two-vehicle crashes have been dominating all types of traffic accidents, wherein the vehicle drivers have been sustaining the highest risk of injury among all vehicle occupants. To understand the critical factors to the drivers’ injury severity of two-vehicle crashes, we employed the random parameters multinomial logit model as a data analyzing tool. To capture the unobserved heterogeneity and potential temporal instability, we combined two strategies: Bayesian random parameter logit and explicitly correlated outcomes. The random parameter logit models were validated with a nine-year large-scale dataset compiled by combining the Crash Report Sampling System (CRSS) and General Estimates Sampling (GES) databases. The results underscore the importance of explicit modeling of inter-outcome correlation, which captured the potential transition probability between adjacent levels of injury severity and improved the model’s predictability. Our model also highlighted substantial per-case and per-driver heterogeneity, which respectively explained 22.8% and 29.4% of the total variance (minor injury) and 25.4% and 24.9% of the variance (severe injury). We found that the female drivers, old ($⩾65$ years) drivers, unbuckled drivers, speeding drivers sustained a higher injury risk in their corresponding groups. Drivers in lighter and older vehicles suffer higher injury risks. Several other factors also considerably affect the injury severity outcomes, such as the road’s speed limit and variables that are proxies of traffic volume (intersection type, whether at the peak hours). Regarding Bayesian modeling, we observed that using weakly informative prior distribution has little effect on the parameter estimates. We also pointed to the directions to further improve the proposed modeling framework.

The increasing number of crashes occurring in work zones has received considerable attention in recent years. Previous studies have mainly focused on associations between work zone configurations and crash occurrence. Although identification of associational relations helps us understand how work zones co-exist with crashes, it does not provide interventional guidelines necessary to improve safety of work zone operations. In this paper, a causal inference model based on the potential outcome framework is proposed to rigorously infer the causal effects of work zone presence on crash risks under various work zone configurations, along with robustness tests. In developing such a causal model, three research gaps are identified and addressed: (1) potential confounding bias due to unobservable roadway characteristics; (2) potential bias caused by unobserved variables in multisource data; and (3) lack of actually observed traffic data and weather information at the exact time when a crash occurred and lack of large-scale high-granular data. The proposed methodology is applied to 5,006 work zones in Pennsylvania from 2015 to 2017, and the results are validated via a series of robustness tests. The results show that the causal effect of a work zone on crash occurrence is significantly positive, especially on roadways with high traffic volumes, on long-distance work zones, and work zones conducted during daytime. It appears that conducting work zones during nighttime with the current deployment strategies on Pennsylvania state roads does not necessarily increase crash risks, but a work zone significantly increases crash risks during day time.

Since the mid-2000s, Public–Private Partnerships (PPP) have been established in transportation infrastructure projects as an effective alternative to the traditional procurement process, such as design-bid-build where the design and construction are awarded separately and sequentially to private firms. PPP contracts ensure both greater participation of the private sector, as well as shared responsibility in project delivery. However, the interrelationship between various PPP approaches and the status of traffic safety during the project implementation has not been thoroughly explored to date. This paper seeks to provide new insights into the performance of different PPP contracting approaches by investigating them from the perspective of transportation safety. To that end, a statistical analysis is conducted in order to distinguish differences with respect to the characteristics of crashes that occurred during the contractual period of roadway projects. Using data from 645 PPP contracts that were executed across multiple States of the US between 1996 and 2011, count data models of crash frequencies are developed. To take into account the effect of unobserved factors on crash frequencies, correlated random parameter models with heterogeneity in the means are estimated. The results of the statistical analysis overall show that the determinants of crash frequencies and the magnitude of their impacts vary across PPP types. Contracts with higher cost, shorter duration, fewer lane-miles to be covered, more asset work activities, as well as contracts for roadways featuring better pavement and drainage conditions, low to medium AADT, and higher width of shoulder are more likely to observe fewer crashes. Additionally, several variables resulted in correlated random parameters (such as, contract size in lane-miles and truck percentage), with their distributional characteristics being affected by other exogenous factors (such as pavement characteristics), thus unveiling the heterogeneous patterns underpinning the safety performance of different PPP approaches.