Analytic Methods in Accident Research最新文献

Speeding behaviors can be classified into different patterns according to both speeding-range and speeding-distance. Among the speeding patterns, some are more frequently observed in specific traffic scenarios, implying that the likelihood of speeding behaviors may vary across the speeding patterns due to the inconsistent impact of temporal, road, environmental, and other traffic factors. Additionally, the trigger of speeding is a complex process so the researchers may not have access to all the critical information associated with the speeding behaviors. This issue may bring about not only independent heterogeneity but also multi-dimensional heterogeneities that are mutually correlated when modeling speeding behaviors by patterns. However, the joint solution to the above challenges is rarely seen in past studies. To fill the knowledge gaps, this study uses taxi GPS trajectories to extract speeding behaviors and classify them into four patterns. The speeder’s percent of speeding distance for each speeding pattern is respectively measured to represent the likelihood of speeding behaviors by patterns. Afterwards, we compare the data-fitting between the models combined with different beta-gamma mixture distributions and a multivariate Gaussian error in modeling the four patterns of speeding likelihood. The combination with the best fitness is used to incorporate jointly correlated random parameters. The results show that the model with beta-gamma-gamma-gamma mixed distributions performs better than other combinations. The model with jointly correlated random parameters outperforms models with other random parameters. The factor analysis reveals that percent of driving at night, percent of driving on roads with a low-speed limit (≤30 km/h), average delays in junctions along the trips, and hourly income have consistent effects on the likelihood of speeding behaviors in all patterns, while the effects of the remaining factors are inconsistent across the speeding patterns. Furthermore, the heterogeneity unveiled by the model parameters is discussed. The study highlights the necessity of considering mixed distributions and multi-dimensional heterogeneities in modeling speeding likelihood by different patterns.

Count regression models have been applied to model expected crash frequency at individual roadway locations. Random parameters have been increasingly integrated into these models to account for unobserved heterogeneity. However, the introduction of random parameters might also mask issues in the model specification, leading to inaccurate relationships and model interpretation. Two of these specification-related issues are: (1) not considering the appropriate functional form of explanatory variables; and, (2) ignoring the best set of significant explanatory variables. To better examine the need for careful model specification, this study uses synthetic data to demonstrate that the consideration of random parameters does not address the two model specification issues identified. The results from the simulation study illustrate that (a) model specification issues cannot be circumvented by random parameters alone and (b) random parameter models including the exhaustive set of explanatory variables available offer significant model improvements.

The adverse road surface condition has been identified as an important factor resulting in serious casualties and property losses in traffic accidents, and there is a tremendous need to uncover the interaction mechanism between deteriorating road surfaces and vehicle impact locations on the driver injury severity at a disaggregate level. In this paper, three groups of random parameters logit models with heterogeneity in means (and variances) are developed to investigate the unobserved heterogeneity and temporal stability of the determinants affecting driver injury severity outcomes across different damage locations among single-vehicle crashes that occurred under adverse weather conditions. A three-year crash dataset gathered from January 1, 2015, to December 31, 2017, in Ohio is utilized. Three crash injury severity categories including no injury, minor injury, and severe injury are identified as outcome variables, while crash characteristics, driver characteristics, temporal characteristics, vehicle characteristics, roadway characteristics, and environment characteristics are regarded as potential predictors influencing driver injury severities. Additionally, likelihood ratio tests and marginal effects are used to assess the temporal instability and impact location non-transferability of the explanatory variables. The results indicate an overall temporal and locational instability of model estimates while several determinants are identified to have consistent effects on injury severity outcomes such as animal-involved collisions, old drivers, safety restraint usage, female drivers, physically impaired drivers, and vehicles with insurance. This study also quantifies and characterizes the net effect of year-to-year and location-to-location shifts through probability differences between out-of-sample predictions and within-sample observations. Varying magnitudes and inconsistent directions of distribution characteristics (mean, skewness, kurtosis, and prediction accuracy) in the probability differences across different impact locations over time are captured. Moreover, this study indicates that the non-transferability of collision locations has a greater impact on the prediction accuracy than the temporal instability. The findings could potentially serve as a reference for transportation administrators to formulate effective safety strategies to better protect drivers from adverse-road-related crashes.

Real-time prediction of crash risk is an effective method for enhancing traffic safety, but it is not fully explored in freeway tunnels. A two-stage deep learning modeling framework comprising a preliminary exploration stage and a prediction and analysis stage is proposed for real-time crash risk prediction in freeway tunnels. A random parameters logit model with heterogeneity in means and variances is used in the preliminary exploration stage to investigate the unobserved heterogeneity and influence mechanism of precursors on real-time crash risk. In the prediction and analysis stage, a random deep and cross network model considering feature interactions and unobserved heterogeneities is developed to predict and analyze real-time crash risk, which is interpreted by the shapley additive explanations approach. The multi-source fusion dataset, collected from the Caltrans performance measurement system and the weather information website, is used to validate the proposed framework for exploring real-time crash risk in freeway tunnels. Results reveal that: (1) the random parameters logit model with heterogeneity in means and variances outperforms the traditional logit model in terms of the model fitting, providing a reference for deep learning modeling that may be able to improve model performance by addressing heterogeneity; (2) the important crash precursors such as the average difference in speed between detectors of tunnel entrance and exit are discovered based on the marginal effect analysis of the random parameters logit model with heterogeneity in means and variances; (3) the random deep and cross network model yields the best prediction performance compared to its counterparts (some other data-driven models), demonstrating the superior performance of deep learning models for real-time risk prediction tasks. It also indicates that considering feature interaction and heterogeneity in deep learning modeling can improve prediction performance; and (4) the important precursors found in the random deep and cross network model using the shapley additive explanations approach are close to those discovered in the statistical model, indicating that the proposed deep learning model can capture the similar effects of precursors as the statistical models, and the precursor interactions and heterogeneities also can be observed by the shapley additive explanations approach.

Using traffic conflict-based extreme value theory (EVT) models to quantify real-time crash-risk of road facilities is a promising direction for developing proactive traffic safety management strategies. Existing EVT real-time crash-risk analysis studies have only focused on using block maxima models. This study proposes a dynamic Bayesian hierarchical peak over threshold modeling approach to estimate real-time crash-risk based on traffic conflicts. The proposed approach combines quantile regression, dynamic updating approach, Bayesian hierarchical structure, and the peak over threshold method to generate time-varying generalized Pareto distributions to derive real-time crash-risk measures (i.e., crash probability and return level). The derived real-time crash-risk measures are applied to estimate cycle-level crash-risk at three signalized intersections in Surrey, British Columbia. Five approaches are used to dynamically update the model parameters, including time trend model, generalized autoregressive conditional heteroskedasticity process approach, as well as the first-order, second-order, and third-order dynamic linear models. For comparison, static models are also developed. All the developed models are compared in terms of statistical fit and predictive performance. Based on the best fitted dynamic model, cycle-level crash probability and return level are calculated to measure signalized intersection safety at cycle level. The results show that dynamic models considerably outperform static models in terms of statistical fit and predictive performance. Further, the third-order dynamic model has the best performance, which is probably due to that the model incorporates two linear trends to respectively describe the variation of the coefficients as well as its change to better account for the variation in the effect of time-varying covariates. However, it should be noted that the third-order dynamic model development needs more computation time than other dynamic models, which may limit the application of the model.

This study presents a new safety measure derived from field theory. It evaluates the risk arising from the various concurrent conflicts within a platoon that can occur on high-speed highway driving situations, such as car-following, yielding, and lane changing. We defined the risk field as a finite scalar field produced by traveling vehicles on the road, and we defined the conflict field as the overlapping risk field between any vehicles in proximity on the roadway. The study used a probabilistic trajectory prediction model to construct risk fields and an approximation method to reduce the computational time for real-time applications. To demonstrate the applicability of the proposed new measure, we applied it to real-world trajectory data (NGSIM data from US Highway 101). We compared the results with three traditional conflict-based safety measures: post-encroachment time (PET), modified time-to-collision (MTTC), and deceleration rate to avoid a crash (DRAC). The new measure produced seamless and continuous risk estimations even during time windows when the other measures could not estimate the risk between vehicles. This is a major advantage over traditional measures. The study also developed visual displays of the estimated conflict fields to provide safety analysts with an intuitive and fast understanding of the results of the safety assessments made using the conflict field measure. We conclude that the proposed new safety measure provides a robust, reliable, and improved assessment of the risk involved in expected future mixed-traffic environments that involve both human-driven vehicles and automated vehicles in the future.

With the recent advancements in computer vision and artificial intelligence, traffic conflicts occurring at an intersection and associated traffic characteristics can be obtained at the granular level of a signal cycle in real-time. This capability enables the estimation of the real-time crash risk using sophisticated modelling techniques, e.g., extreme value theory. However, these models are inherently incapable of forecasting the crash risk of future time periods based on the temporal dependency of crash risks. This study proposes a unified framework of extreme value theory and autoregressive integrated moving average models for forecasting crash risks at signalised intersections. At the first level of this framework, a non-stationary generalised extreme value model has been developed to estimate the real-time rear-end crash risk at the signal cycle level using the video data collected from three signalised intersections in Queensland, Australia. To capture the time-varying effect of different traffic conditions on conflict extremes, traffic flow, speed, shockwave area, and platoon ratio covariates are incorporated into the generalised extreme value model. The signal cycle-level crash risks obtained from the first level form a univariate time series, which is modelled using two variants of autoregressive integrated moving average model to forecast the crash risk of future signal cycles. Results reveal that the autoregressive integrated moving average model with exogenous variables outperforms the model without exogenous variables and can forecast the crash risk for the next 30–35 min with reasonable accuracy. Similarly, results also demonstrate that different crash risk patterns within a typical day are accurately predicted. The proposed framework helps identify the spatiotemporal windows where safety gradually deteriorates over time, thus enabling proactive safety assessment.

Due to visibility limitations and other factors, the injuries sustained by bicyclists in nighttime vehicle-bicycle crashes tend to be more severe than daytime crashes. This paper seeks to provide insights into this day/night injury severity phenomenon by studying how day/night bicyclist injury severities have changed in crashes that occurred before, during, and after the COVID-19 lock downs. Using data from vehicle-bicycle crashes in the state of Florida over a three-year period (from 2019 to 2021 inclusive), separate yearly models of bicyclist-injury severities (with possible outcomes of severe injury, minor injury, and no visible injury) were estimated using a random parameters logit approach with possible heterogeneity in the means and variances of random parameters. Likelihood ratio tests were conducted to examine the overall stability of model estimates across the studied years as well as day/night differences, and a comparison of partially constrained and unconstrained temporal modeling approaches was undertaken. A wide range of variables potentially affecting resulting bicyclist injury severities in vehicle/bicycle crashes was considered including bicyclist and vehicle driver information, vehicle features, roadways and environmental conditions, temporal characteristics, and roadway features. The findings show statistically significant injury-severity differences between daytime and nighttime before, during and after the COVID-19 pandemic. Out-of-sample simulation results suggest that improving the visibility of bicyclist through mandated reflectivity, improved roadway illumination, undertaking public awareness campaigns relating to nighttime bicyclist safety, and vulnerable road user detection sensors in vehicles can all contribute to substantially improving nighttime bicyclist safety.

Run-off-road crashes are one of the most significant causes of road deaths worldwide. Given such significant safety concerns, a number of earlier studies examined the critical factors contributing towards run-off-road crash severity outcomes, mostly by using the information compiled in the official crash database. However, the official crash databases are less likely to have detailed information on driver behavior (errors/expectations) and roadway environment (roadway geometry and roadside attributes). This study aims to investigate the effects of design consistency measures on run-off-road crash severity mechanisms by applying a random parameters hierarchical ordered Probit model. This study contributes towards existing safety literature by demonstrating a complementary approach to capturing the effects of driver behavior and heterogeneity in roadway environment on run-off-road crash severity outcome through the composite measures of design consistency indices and cosmopolite measures of roadside hazard type variables. Specifically, 17 different functional forms of design consistency indices are developed to capture the behavioral factors from the road-geometric changes in developing run-off-road crash severity models. Further, in examining the effect of different types of the roadside environment on run-off-road crash severity outcomes, seven roadside hazard type variables are generated as a composite function of roadside object type and clear zone (lateral distance to roadside object). The empirical analysis of this study involves a two-step modelling approach - in the first step, the decision tree algorithm is applied to identify the higher-order interaction among independent variables, and in the second step, crash severity models are developed by employing several econometric approaches. The hybrid models are estimated by employing four econometric frameworks, which include Ordered Probit, Hierarchical Ordered Probit, Random Parameters Ordered Probit, and Random parameters Hierarchical Ordered Probit models. The run-off-road crash severity models are estimated by using crash data collected from the State of Queensland, Australia, for the years 2015 through 2019. Overall, this study reveals the importance of considering the interaction of drivers' behavior, road geometry, and roadside attributes along with other independent variables in developing run-off-road crash severity models.

Transportation safety studies have been mostly focused on using crash data that are rare events. Alternatively, conflict estimation can be used to assess safety. This has been proven as a proactive design methodology that does not rely on crashes and requires shorter observation. Traditionally, the safety studies involving both these reactive and proactive methods were based on aggregated data that does not take individual vehicle dynamics into consideration. This paper addresses this research gap by proposing a novel real-time conflict prediction methodology that uses previous instance trajectory data of individual vehicles to understand whether there can be potential conflict in the near future. A long-short term memory (LSTM) model is developed that can apprehend a conflict situation 9 s in the future. Data from connected vehicles have been used. The proposed model returned a recall of 81% with a false alarm rate of 28%. The predictive model has the potential to be implemented on vehicle dashboards to warn drivers of a conflict. The authors have also used SHAP (SHapley Additive exPlanation) to interpret the results from the LSTM model. It was deduced that acceleration above 0.3 m/s², deceleration within −1.5 m/s² to −0.25 m/s², and speed of more than 40kph were responsible for inducing a conflict.