Analytic Methods in Accident Research最新文献

Speeding is one of the major significant causes of high crash risk and the associated injury severity outcomes. To combat such significant safety concerns, a speed limit enforcement system has been adopted widely around the world. This study aims to present an econometric approach that estimates the casual effect of speed enforcement on safety while addressing the endogeneity issue by employing an instrumental variable approach within a maximum simulated likelihood framework. In our study, safety enforcement is represented as the number of speeding tickets issued from the speed camera systems, while safety profile is presented as two dimensions of interest, including total crash risk and crashes by injury severity levels. The proposed econometric model takes the form of a correlated panel random parameters model with speed enforcement endogeneity. In estimating the joint panel model, speed enforcement and crash severity components are modeled by employing Random Parameters Ordered Logit Fractional Split technique, while ‘ is modeled by employing Random Parameters Negative Binomial regression technique. In the current study context, the ‘operational duration of speed camera’ serves as the instrumental variable for controlling the endogeneity between speed enforcement and safety. Further, the analysis is augmented by a detailed policy scenario analysis. The empirical analysis is demonstrated by employing roadway segment-level crash data and speeding tickets data from Queensland, Australia, for the years 2010 through 2013. From the policy analysis, it is found that a stricter speed enforcement for serious level of speeding offenses is likely to have greater safety benefits in reducing crash severity levels. Moreover, a targeted increase in operation duration along with stricter citations for major speeding is likely to have significant safety gain. The outcome of the study will allow the decision-makers to identify a robust resource allocation and speed camera deployment plan.

Safety literature has traditionally developed independent model systems for macroscopic and microscopic level analysis. The current research effort contributes to the literature on crash frequency by building a bridge between these two divergent streams of crash frequency research. The study proposes an integrated micro–macro level model for crash frequency estimation. Specifically, the study develops an integrated model system that allows for the influence of independent variables at the microscopic level to be incorporated within the macroscopic propensity estimation. The empirical analysis is based on the data drawn from 300 traffic analysis zones, 1818 roadway segments, and 4184 intersections from the City of Orlando, Florida for the years 2018 and 2019. The study considers a host of exogenous variables including roadway and traffic factors, land-use, built environment, and sociodemographic characteristics for the model estimation. The proposed model system can also accommodate for hierarchical correlations such as correlation between all segments or intersections in a zone. The study findings highlight the presence of common spatial unobserved factors influencing crash frequency across segment level and intersection level as well as presence of significant parameter variability across both micro and macro level in the crash frequency. The empirical analysis is further augmented by employing several goodness of fit and predictive measures. The results clearly demonstrate the improved performance offered by the proposed integrated micro–macro model relative to the non-integrated macro model. The overall model fit measures and interpretations encourage the application of the proposed model for crash frequency analysis.

Sleepiness is a common human factor among truck drivers resulting from sleep loss or time of day and causing impairment in vigilance, attention, and driving performance. While driver sleepiness may be associated with increased risk on the road, sleepy drivers may drive more cautiously as a result of risk-compensating behaviour. This endogeneity has been overlooked in the previous driver behaviour studies and may provide new insight into the effects of sleepiness on driving performance. In addition, the Karolinska Sleepiness Scale (KSS) has been widely used to quantify sleepiness. However, the KSS is a subjective self-reported measure and is reliant on honest reporting and understanding of the scale. An alternative way of quantifying sleepiness is using drivers’ heart rate and correlating it with their sleepiness. While recent advances in data collection technologies have made it possible to collect heart rate data in real-time and in an unobtrusive way, their application in measuring sleepiness particularly among truck drivers has been unexplored.

This study aims to address these gaps and contribute to analytic methods in road safety research by collecting truck drivers’ heart rate data in real-time, measuring sleepiness from those data, and using it in an instrumental variable modelling framework to investigate its effect on driving performance. To this end, a driving simulator experiment was conducted in Belgium and heart rate data were collected for 35 truck drivers via sensors installed on the steering wheel of the simulator. Additional demographic data were collected using a questionnaire before the experiment. An instrumental variable model consisting of a discrete binary logit and a continuous generalized linear model with grouped random parameters and heterogeneity in their means was then developed to study the effects of driver sleepiness on headway. Results indicate that age, years of holding driver licence, road type, type of truck transport, and weekly distance travelled are significantly associated with sleepiness among the participants of this study. Sleepy driving is associated with reduced headway for 30.5% of the drivers and increased headway for the other 69.5%, and night-time shift is associated with such varied effects. These findings indicate that there may be group- or context-specific risk patterns which cannot be explicitly addressed by hours of service regulations and therefore, transport operators, driver trainers and fleet managers should identify and handle such context-specific high risk patterns in order to ensure safe operations.

Driver’s response to a pedestrian crossing requires braking, whereby both excess and inadequate braking is directly associated with crash risk. The highly anticipated connected environment aims to increase drivers’ situational awareness by providing advanced information and assisting them during critical driving tasks such as braking. Focussing on this crucial behaviour and combined with the promise of a connected environment, the objective of this study is to examine the braking behaviour of drivers in response to a pedestrian at a zebra crossing in a connected environment. Seventy-eight participants from diverse backgrounds performed this driving task in the CARRS-Q Advanced Driving Simulator in two randomised driving scenarios: a baseline scenario (without driving aids) and a connected environment (with driving aids) scenario. A Weibull accelerated failure time duration modelling approach is adopted to model the braking behaviour of drivers. In particular, this duration model is specified to capture the panel nature of the data and unobserved heterogeneity through correlated grouped random parameters with heterogeneity-in-the-means in the Bayesian framework. Results indicate that, for most drivers in the connected environment, it takes longer to reduce their speed with less speed variation and a larger safety margin. In addition, a decision tree analysis for the braking time suggests that for older drivers, when the distance to the zebra crossing is larger in the connected environment than that in the baseline scenario, braking time is likely to increase. The model also reveals that the braking time of female drivers is longer in the connected environment compared to that of male drivers. Overall, the connected environment is associated with increased braking time by providing advanced information, giving drivers additional time to smoothly reduce their speed in response to a pedestrian at a zebra crossing, and ultimately making the vehicle–pedestrian interaction safer.

The real-time conflict prediction model using traffic flow characteristics is much less studied than the crash-based model. This study aims at exploring the relationship between conflicts and traffic flow features with the consideration of heterogeneity and developing predictive models to identify conflict-prone conditions in a real-time manner. The high-resolution trajectory data from the HighD dataset is used as empirical data. A novel method with the virtual detector approach for traffic feature extraction and a two-step framework is proposed for the trajectory data analysis. The framework consists of an exploratory study by random parameter logit model with heterogeneity in means and variances and a comparative study on several machine learning methods, including eXtreme Gradient Boosting (Boosting), Random Forest (Bagging), Support Vector Machine (Single-classifier), and Multilayer-Perceptron (Deep neural network). Results indicate that (1) traffic flow characteristics have significant impacts on the probability of conflict occurrence; (2) the statistical model considering mean heterogeneity outperforms the counterpart and lane differences variables are found to significantly impact the means of random parameters for both lane variables and lane differences variables; (3) eXtreme Gradient Boosting trained on an under-sampled dataset turns out to be the best model with the highest AUC of 0.871 and precision of 0.867, showing that re-sampling techniques can significantly improve the model performance. The proposed model is found to be sensitive to the conflict threshold. Sensitivity analysis on feature selection further confirms that the conflict risk prediction should consider both subject lane features and lane difference features, which verifies the consistency with exploratory analysis based on the statistical model. The consistency between statistical models and machine learning methods improves the interpretability of results for the latter one.

The logit model and its variations have been used extensively in the field of traffic safety in general, and crash severity analysis in particular. Attempts were made to overcome the logit's shortcomings and limitations by generalizing its binary form to a more relaxed and unconstrained setting. Such attempts include the addition of shape parameters in order to add more flexibility to the probability distribution, while maintaining the straightforwardness provided in the logit-type models, with the least computational effort. A well-known form that provides an extra parameter to the base logit is the scobit model. In this study, we explore several generalizations of the binary scobit model by applying the same conventional methods associated with the generalized logit forms, principally to cover the multinomial nature of crash severity outcomes. Those are the multinomial and the ordinal forms. Furtherly, we utilize mixed distributions to provide crash-specific random parameters with heterogeneity in means and variances. Crash severity dataset taken from Guangdong province, China, was used to compare the different forms. The multinomial scobit models provided better results in terms of sample and out-of-sample fit, with the cost of some complexity in the heterogeneous forms. Other forms did not show a substantial or consistent advantage over their logit counterparts. All models exhibit temporal instability when applied to multiple time periods.

Many intersections belong to more than one zone, but most research has not considered the effects of multiple zones in intersection crash analysis. This issue is known as a boundary problem. Unobserved heterogeneity between zones can lead to model misspecification which can result in biased parameter estimates and poor model fitting performance. This study investigated the issue using five years of intersection crash data from the City of Regina, Saskatchewan, Canada. The study developed three multiple membership multilevel negative binomial models to reduce unobserved zonal-level heterogeneity. Each multiple membership multilevel model used a different weight strategy. When the fitting performance of the three multiple membership multilevel models was compared with two additional models, a traditional single level model and a conventional multilevel model, all three multiple membership multilevel models had a better fitting performance. Five individual-level and seven group-level variables were statistically significant (90% confidence level) in all the models with five of the individual-level and four of the group-level variables statistically significant at the 99% confidence level. The multiple membership multilevel models also helped to prevent the underestimation of group-level variance and type I statistical errors that tend to occur with single level models and conventional multilevel models. In particular, the three multiple membership multilevel models produced more accurate results for intersections with a large AADT. As intersections with a large AADT are known to have more crashes, multiple membership multilevel models are likely to be more useful than single level models and conventional multilevel models when selecting intersections for safety improvement. The study recommends the adoption of a multiple membership multilevel model to improve fitting performance and reduce the boundary problem for intersections affected by more than one zone.

Rear-end crashes have become a serious global issue, with increasing injuries and fatalities accounting for massive property loss. The purpose of this study is to investigate the variation in the influence of factors affecting injury severity in rear-end and non-rear-end crashes and the change in impact degree over time. Using the three-year crash data of the Beijing–Shanghai Expressway from 2017 to 2019, the heterogeneity and temporal stability of contributing factors affecting rear-end and non-rear-end crashes were investigated through a group of random parameter logit models with unobserved heterogeneity in means and variances. Then, the temporal stability and transferability of the models were evaluated using likelihood ratio tests. Moreover, the marginal effects were calculated to explore the temporal stability and potential heterogeneity of the contributing variables from year to year. Using four possible injury severity outcomes, namely, fatal injury, severe injury, minor injury, and no injury, a wide variety of possible factors significantly affecting injury severity outcomes including environmental, temporal, spatial, traffic, speed, geometric, and sight distance characteristics were analyzed. Considerable differences were observed in the rear-end and non-rear-end crashes, and the contributing factors indicated statistically significant temporal instability in both crashes over the three-year period. This study can be of value in promoting highway safety aimed at rear-end and non-rear-end crashes and developing suitable safety countermeasures.

Overturned crashes are associated with a disproportionate number of severe injuries and fatalities, while hit-fixed-object crashes are acknowledged as the most frequent single-vehicle crashes. To investigate the temporal stability and differences of contributing factors determining different injury severity levels in overturned and hit-fixed-object crashes on rural roads accompanied by speeding driving, this paper estimates two groups of correlated random parameters logit models with heterogeneity in the means (one group relating to overturned crashes and the other relating to hit-fixed-object crashes). Three injury-severity categories are determined as outcome variables: severe injury, minor injury and no injury, while multiple factors are investigated as explanatory variables including driver, vehicle, roadway, environmental, and crash characteristics. The overall temporal instability and non-transferability between overturned and hit-fixed-object crashes are captured through likelihood ratio tests. Marginal effects are adopted to further exhibit temporal variations of the explanatory variables. Despite the overall temporal instability, some variables still present relative temporal stability such as alcohol, truck, aggressive driving, vehicle age (>14 years old), and speed limit (<45 mph). This non-transferability between overturned and hit-fixed-object crashes provides insights into developing differentiated strategies targeted at mitigating and preventing different types of crashes. Besides, out-of-sample prediction is undertaken given the captured temporal instability and non-transferability of overturned and hit-fixed-object crash observations. More studies can be conducted to accommodate the spatial instability, under-reporting of severe-injury crashes, the trade-off between model predictive capability, inference capability, and selectivity bias.

With the increasing advancements in autonomous vehicle (AV) technologies, the forecasts of AV market shares seem to follow an ever-growing trend. This leads to the inherent need for proactive safety evaluations of AV impacts on other road users. To that end, this study proposes a modeling framework for the proactive assessment of pedestrian safety in AV environments. The proposed framework relies on the Extreme Value Theory (EVT), with the peak over threshold modeling technique, to develop an estimate of AV-pedestrian collisions using AV-pedestrian conflicts. The proposed framework was applied to two AV datasets, collected from three locations in the US and Singapore, using the operating AV fleets of two developers, Motional and Lyft. Both datasets included trajectory data for the subject AV, as well as LiDAR point clouds and annotated video data from AV cameras to capture the trajectories of surrounding road users. The datasets were processed to extract the AV-pedestrian conflicts along with the corresponding conflict indicators, mainly the post-encroachment time (PET) and time-to-collision (TTC). Relevant covariates were introduced to the proposed models to enhance their performance and prediction accuracy, including turning movements and conflict speeds. The results indicate an alarming risk to pedestrians when interacting with AVs, especially at the early stages of AV adoption. The expected number of collisions ranged from 4 to 5.5 per million vehicle kilometers travelled (VKT) of the AVs. With the addition of the covariates, the expected number of collisions went down to a range of 2.3–3.7 per million VKT, but with a narrower confidence interval of the resulting estimate and a better fit. The introduced approach shows promising prospects for the application of EVT methods to address AV safety impacts. It also invites future applications to address issues of concern for pedestrian safety in different conditions of urban traffic.