Analytic Methods in Accident Research最新文献

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.

Variable speed limits are frequently used to improve traffic safety and harmonise traffic flow. This study investigates how, and to what extent, drivers reduce their speed upon passing a variable speed limit sign. We specifically consider the impact on braking behaviour due to the systematic inclusion of different social pressures exerted by surrounding traffic. This social pressure is the natural result of having two vehicle cohorts created by a change in the variable speed limit (the new speed limit being higher than the original). The cohort with the higher speed limit overtakes vehicles with the lower speed limit, instigating a specific passing rate on drivers in the lower speed cohort. A driving simulator study is employed to obtain individual driver data whilst being able to systematically change the social pressure applied. A sample comprising sixty-seven participants conducted multiple randomised drives, with varying passing rates from as low as 90 veh/h to as high as 360 veh/h. The speed reduction behaviour of the participants is modelled using a random parameter duration modelling approach. Both the panel nature of the data and unobserved heterogeneity are captured through a correlated grouped random parameters with heterogeneity-in-the-mean model. The random parameters are predicated on the different passing rate scenarios, allowing drivers to take shorter or longer to reduce their speeds compared to the reference passing rate. It is shown that the extent of social pressure impacts braking behaviour and therefore affects safety measures, which is a function of the magnitude of the speed limit change. In addition, an extensive decision tree analysis is conducted to understand differential braking behaviour. Results reveal that, on average, female drivers take a shorter time to reduce their speed under a high passing rate but longer in a low passing rate scenario compared to males. Similarly, young drivers are found to take longer to reduce their speeds in a high passing rate scenario compared to other age groups. Our main findings indicate that the within-cohort safety is lowest under low passing rates due to comparatively larger speed differences between drivers. Yet, under a high passing rate, we observe an increase in violation of the speed limit by the lower speed limit vehicles (but less within cohort speed differences). Whilst normally this would be an undesired effect across cohorts, this violation is argued to lead to increased safety due to the smaller discrepancy in speed.

Injury severity in police crash reports is usually recorded in three to five classes, including property damage, slight, moderate, serious, and fatal injuries. Among these classifications, serious injuries are commonly classified as cases where a road user is admitted to a hospital. In this classification, the length of hospital stay is not differentiated, whether one day or ten days, as long as the road user has been admitted to the hospital. As such, the inferences drawn from assuming that all serious injuries (1 if a road user is admitted to the hospital; 0 otherwise) are at the same severity level inherently suffer from aggregation bias and may not provide a thorough understanding of this severity category. This study proposes a hazard-based duration modelling approach to examine the severity of serious injury crashes measured in a continuous spectrum. Specifically, using the length of hospital stay as the measure of serious injuries, a random parameters hazard-based duration model with heterogeneity in means was applied to model serious injury crashes obtained by linking crash records in police and hospital databases. To address temporal instability, the injury records sources from Abu Dhabi, United Arab Emirates (UAE), between 2015 and 2019 were modelled separately for each year. The results showed that factors positively associated with more serious injury severity (prolonged length of hospital stay) are rural areas, high posted speed limits of 100–160 km/h, overturned crashes, speeding, impaired driving, involvements of a heavy vehicle, nighttime crashes, lack of restraint usage, and injuries to the head or lower extremities. In particular, speeding violations during nighttime are positively associated with more serious injuries. Furthermore, the means of the random parameters of head injury are positively influenced by speeding, lack of restraint usage, and motorcycle involvement through the heterogeneity-in-mean specification of the hazard-based duration model. The proposed modelling approach to model serious traffic injuries using a hazard-based duration model provides a comprehensive understanding of the factors associated with serious injuries.

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.

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.

The use of traffic conflict-based models to estimate crash risk and evaluate the safety of road locations is a popular direction for road safety analysis. However, a challenging issue of traffic conflict-based crash risk modeling is the selection of an appropriate sample size. Reliable conflict-based crash risk models typically require a large sample size which is always very difficult to collect. Further, when choosing a sample size, the bias-variance trade-off of model estimation is a constant concern. This study proposes an approach for identifying an adequate sample size for conflict-based crash risk estimation models. The appropriate sample size is determined by checking the model convergence and its goodness-of-fit. A quantitative approach for objectively testing the model goodness-of-fit is developed. Both the trace plots and the variation tendencies of Brooks-Gelman-Rubin statistics of parameter simulation chains are examined to inspect the model convergence. A graphical method is also used to check the model goodness of fit. If the model has not converged or fits poorly, then additional samples are required. The proposed method was applied to identify the adequate sample size for a Bayesian hierarchical extreme value theory (EVT) block maxima (BM) model using traffic conflict data from four signalized intersections in the city of Surrey, British Columbia. The indicator, modified time to collision (MTTC), was used to delineate traffic conflicts. A series of stationary and non-stationary Bayesian hierarchical BM models were developed using the cycle-level maximums of negated MTTC. The adequate sample sizes of stationary and non-stationary Bayesian hierarchical BM models were determined separately. Further, two methods of increasing the sample size (i.e., extending the observation period and combining data from different sites) were compared in terms of goodness-of-fit as well as crash estimate accuracy and precision. The results show that for both stationary and non-stationary models, the sample size used is adequate for model convergence and goodness-of-fit. Moreover, adding covariates to the stationary Bayesian hierarchical BM model does not affect the size of the required sample. Extending the observation period outperforms combining data from different sites in terms of goodness-of-fit as well as crash estimation accuracy and precision of non-stationary models. This is likely related to the existence of unmeasured factors that could impair model estimation and inference when merging data from several sites to augment the number of samples. Overall, the findings of this study can be applied to examine whether available data is adequate and the amount of additional data required for producing reliable statistical inference.

Several studies have shown that crash risk is a dynamic quantity that is frequently changing with considerable spatial and temporal variations. Recent advances in safety evaluation techniques such as using extreme value theory (EVT) models provided the opportunity to use traffic conflict data obtained from road user trajectories to estimate real time safety metrics. These metrics can aggregate crash risk along a certain route based on the duration of exposure to unsafe road conditions. This paper applies a Bayesian hierarchal extreme value theory model to trajectories obtained from a drone dataset from Athens, Greece, to develop a safest route algorithm capable of informing users about the safest route in an urban network in real time. The study area selected consists of a rectangular grid made up of 102 signalized and unsignalized intersections. The dynamic crash risk for each link in the network was obtained and used to identify the safest route between any origin–destination pair and the corresponding fastest route. The safest routes were then compared to the fastest routes and were found to be 22% safer on average, resulting in an 11% increased travel time. Moreover, the safest route was identical to the fastest route in 23% of the origin–destination pairs analyzed and had an average similarity of 54% in terms of links. Recognizing the trade-off between safety and mobility, a multi-objective routing methodology was proposed which balances travel time and crash risk using a weighted preference for safety. This work has considerable potential for improving the safety of all road users and may also be used for fleet routing applications as part of multi-objective routing systems.