Analytic Methods in Accident Research最新文献

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.

This study explores the differences between day and night pedestrian-injury severities in vehicle–pedestrian crashes over a five-year period using data from the state of Kansas. Separate statistical models (random parameters logit models with possible heterogeneity in the means and variances of the random parameters) were estimated for day and night crashes to examine different pedestrian injury severity outcomes (no visible injury, moderate injury, and severe injury). Likelihood ratio tests were conducted to explore the temporal stability of the model estimations over different times of day and years. Many variables affecting injury severities were considered in model estimation including time and location of accidents, in addition to information on environmental, roadway, crash, vehicle, driver, and pedestrian characteristics. The findings indicate that the factors affecting pedestrian injury severities did change over time but that there is a clear day-night difference in the resulting injury severities of pedestrians, with nighttime crashes consistently resulting in more severe injuries overtime. This suggests policies and technologies that seek to essentially replicate daytime conditions (improved illumination, infrared pedestrian detection in vehicles, etc.) in nighttime conditions could have considerable safety benefits. Using the estimated random parameters models, extensive out-of-sample prediction simulations are used to provide estimates of the potential benefits of such nighttime mitigation policies and technologies, as well as how daytime/nighttime pedestrian injury severity probabilities have been changing over time.

Examining crash risk in the highly anticipated connected environment is hindered by its novelty and the consequent scarcity of relevant data. This study proposes an Extreme Value Theory approach to examine and quantify mandatory lane-changing crash risk in the traditional and connected environments using traffic conflict techniques. The CARRS-Q advanced driving simulator was utilised to collect trajectory data of 78 participants performing mandatory lane-changing manoeuvres in three randomised driving conditions: baseline (without driving aids), connected environment with perfect communication, and connected environment with communication delay. Using the exceedance statistics theory (also known as a Peak Over Threshold approach corresponding to Generalised Pareto distribution), three separate models corresponding to each driving condition were developed. Driving-related factors obtained from the driving simulator data, such as speeds, spacings, lag gaps, and remaining distances, as well as driver demographics, were used as input variables to these models. Relative crash risk analysis and characteristics of the fitted Generalised Pareto distributions were employed as indicators of safety. The findings suggest that the connected environment significantly reduces mandatory lane-changing crash risk compared with the baseline condition, with the highest risk reduction observed in the perfect communication condition. While the crash risk of the communication delay condition is higher than that of the perfect communication condition, it is lower than the baseline condition. Furthermore, a comparison of the developed model to its counterpart (i.e., Block Maxima approach) showed the better performance of the adopted approach. The findings of this study provide insights into the positive impact of the connected environment on the safety of mandatory lane-changing manoeuvres as well as confirm the veracity of Peak Over Threshold models in estimating crash risk using traffic conflict data.

Alcohol consumption has been acknowledged as a critical determinant concerning the occurrence of vehicle crashes and their resulting injury severities. To investigate the weekly transferability and temporal stability of the contributors determining different injury severity levels in alcohol-impaired driving crashes, this paper employs two groups of random thresholds random parameters hierarchical ordered probit models. Three injury-severity categories are determined as outcome variables: no injury, minor injury and severe injury, while multiple factors are investigated as explanatory variables including driver characteristics, vehicle characteristics, roadway characteristics, environmental characteristics, crash characteristics and temporal characteristics. The weekly transferability and temporal stability of the models are examined through three groups of likelihood ratio tests. Marginal effects are also adopted to analyze the weekly transferability and temporal stability of the explanatory variables. Overall, the findings exhibit weekly variations and temporal instability while some indicators are also observed to be of relative weekly transferability including speeding, aggressive driving, proceeding, motorcycle, speed limit between 45 and 55 mph, curve, driveway, overturned, hit-fixed-object, vehicle age (5–9 years old). Besides, curve and passenger car indicators in weekday models present relative temporal stability. This paper can provide insights into preventing alcohol-impaired driving crashes and can potentially facilitate the development of the corresponding crash injury mitigation policies. More studies could be conducted integrating the advanced data-driven methods into the statistical models to simultaneously achieve inference and prediction.

Using data of multi-vehicle crashes with drivers under the influence of alcohol/drugs in North Carolina from 2008 to 2017, this paper explores time-of-day variations (daytime vs. nighttime) and temporal instabilities of factors affecting alcohol/drug-impaired crash injury severities during three crash cycle phases after the Great Recession. Random parameters logit models with heterogeneity in the means and variances are utilized to identify significant factors, explore unobserved heterogeneity, reveal correlations between factors, and suggest possible impacts of economic conditions on the factors. Different likelihood ratio tests indicate that the effects of factors vary significantly across time-of-day and economic-related cycle periods. Significant time-of-day variations imply more severe injury alcohol/drug involved crashes during the nighttime compared to the daytime. Meanwhile, temporal instabilities are also observed in marginal effects of several factors across three-cycle periods. Proficient and cautious elder drivers were safer than young drivers during the depression period. Also, both depressing and expanding periods could affect the involvement of alcohol/drugs for drivers. Shifts in alcohol/drug use behaviors underscore the importance of accounting time-of-day variations, temporal instabilities, and heterogeneity in the means and variances inherent in alcohol/drug-impaired crash factors after the Great Recession. The insights of this study should be valuable to improve specific enforcements, qualify punishments, organize targeted campaigns, and design other preventive activities for alcohol/drug-impaired crashes.

Relative to safety assessment using data from observed crashes, conflict-based road safety assessment can potentially provide additional insights into crash causation processes. Despite numerous review studies on this topic, the application context of conflict measures has been generally overlooked. This study conducts a systematic review of conflict-based safety measures with a specific focus on the context of their applications. This study employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA) guidelines of systematic review and meta-analysis to review conflict measures used for the safety assessment of intersections over the past ten years (2010–19). A total of 386 studies are systematically reviewed to identify conflict measures used for various contexts, including intersection types, traffic operating conditions, study types, and the purpose of the study.

The systematic review indicates that temporal proximity measures, specifically time-to-collision and post-encroachment time, are the most widely used conflict measures regardless of the application context. Other families of conflict measures such as spatial proximity, kinematic, mixed and combinations of measures have also been applied depending on the context. Using the extracted data from relevant studies, linear regression models were developed for time-to-collision and post-encroachment time thresholds at signalized intersections and time-to-collision thresholds at unsignalized intersections. The thresholds are found to be associated with traffic environment types, sources of conflict data and the application purpose of conflict measures. The findings of this study identify several critical gaps in the literature that can help guide future research directions in the conflict-based safety assessment of transport facilities. Critical gaps include the scarcity of validation studies for conflict measures, the lack of suitable techniques to estimate crash risk by severity types, the primary focus on signalized intersections (leaving studies of other facility types underrepresented), and the lack of suitable conflict measures for vulnerable road users.

This paper investigates the determinants of injury severities in single-bicycle and bicycle-motor vehicle crashes by estimating correlated random parameter ordered probit models with heterogeneity in the means. This modeling approach extends the frontier of the conventional random parameters by capturing the likely correlations among the random parameters and relaxing the fixed nature of the means for the mixing distributions of the random parameters. The empirical analysis was based on a publicly available database of police crash reports in the UK using information from crashes occurred on urban and rural carriageways of Scotland between 2010 and 2018. The model estimation results show that various crash, road, location, weather, and driver or cyclist characteristics affect the injury severities for both categories of crashes. The heterogeneity-in-the-means structure allowed the incorporation of a distinct layer of heterogeneity in the statistical analysis, as the means of the random parameters were found to vary as a function of crash or driver/cyclist characteristics. The correlation of the random parameters enabled the identification of complex interactive effects of the unobserved characteristics captured by road, location and environmental factors. Overall, the determinants of injury severities are found to vary between single-bicycle and bicycle-motor vehicle crashes, whereas a number of common determinants are associated with different effects in terms of magnitude and sign. The comparison of the proposed methodological framework with less sophisticated ordered probit models demonstrated its relative benefits in terms of statistical fit, explanatory power and forecasting accuracy as well as its potential to capture unobserved heterogeneity to a greater extent.