Analytic Methods in Accident Research最新文献

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.

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.

Research has shown the detrimental effects of using mobile phones whilst driving, which are more prominent and concerning for young drivers, who are often less experienced and riskier. As such, this study investigates young drivers’ response times when they encounter a safety–critical event on a suburban road whilst using a mobile phone. To collect high-quality trajectory data, the CARRS-Q advanced driving simulator was used. Thirty-two licenced young drivers were exposed to the sudden braking of the lead vehicle in their lane in three driving conditions: baseline (no phone conversation), handheld, and hands-free. Unlike extant studies, this paper proposes a hybrid modelling framework for the response times of distracted drivers. This framework combines a decision tree model and a correlated grouped random parameters duration model with heterogeneity-in-means. While the decision tree model identifies a priori relationship among main effects, the random parameter model captures unobserved heterogeneity and correlation between random parameters. The modelling results reveal that mobile phone distraction impairs response time behaviour for the majority of drivers. However, some drivers tend to respond earlier whilst being distracted, suggesting that the perceived risk of mobile use might have led to an early response, indicating their risk compensation behaviour. Female drivers tend to respond earlier compared to male drivers, indicating their safer and risk-averse behaviour. Overall, mobile phone distraction appears to deteriorate response time behaviour and poses a significant safety concern to drivers and the overall traffic stream unless mitigated.

Traditionally, aggregate crash frequency by severity and disaggregate severity analysis have been conducted independently in the safety literature. The current research effort contributes to the safety literature by bridging the gap between these two different streams of research by using both aggregate and disaggregate level crash data simultaneously. To be specific, the study proposes a framework that integrates aggregate and disaggregate level models. The proposed framework allows for the influence of independent variables at the crash record level to be incorporated within the aggregate level propensity estimation. The empirical analysis is based on the crash data drawn from the city of Orlando, Florida for the year 2019. The disaggregate level analysis uses 20,204 crash records that contain crash specific variables, temporal characteristics, roadway, vehicle and driver factors, road environmental and weather information for each record. For aggregate level model analysis, the study aggregated the crash records by severity class over 300 traffic analysis zones. An exhaustive set of independent variables including roadway and traffic factors, land-use attributes, built environment, and sociodemographic characteristics are considered in this analysis. The empirical analysis is further augmented by employing several goodness of fit and predictive measures. A validation exercise is also performed using a holdout sample to highlight the superior performance of the proposed integrated model relative to the non-integrated crash count by severity model. The proposed model can also accommodate common unobserved spatial correlation among crash records within the same zone. The model results illustrate the benefits of developing an integrated model system for crash frequency and severity.

With proactive safety assessment gaining significant attention in the literature, the relationship between traffic conflicts (which form the underpinnings of proactive safety measures) and observed crashes remains a critical research need. Such a need will grow significantly with the ongoing introduction of connected and autonomous vehicles where software and hardware improvements are likely to be determined from observed traffic conflict data as opposed to data from accumulated crashes. Extreme value theory has been applied for over two decades to study the relationship between traffic conflicts and crashes. While several advancements have been made in extreme value theory models over time, the need to continually evaluate the strengths and weaknesses of these models remains, particularly considering their likely use in improving the safety–critical elements of connected and autonomous vehicles. This paper seeks to comprehensively review studies on extreme value theory applications in traffic conflict/crash contexts by providing an in-depth assessment of alternate modelling methodologies and associated issues. Critical research needs relating to the further development of extreme value theory models are identified and include identifying efficient techniques for sampling extremes, determining optimal sample size, assessing and selecting appropriate traffic conflict measures, incorporating covariates, accounting for unobserved heterogeneity, and addressing issues associated with real-time estimations.