From Few to Many: Using Copulas and Monte Carlo Simulation to Estimate Safety Consequences (original) (raw)
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Accident Analysis & Prevention, 2019
This study employs a copula-based multivariate temporal ordered probit model to simultaneously estimate the four common intersection crash consequence metrics-driver error, crash type, vehicle damage and injury severity-by accounting for potential correlations due to common observed and unobserved factors, while also accommodating the temporal instability of model estimates over time. To this end, a comprehensive literature review of relevant studies was conducted; four different copula model specifications including Frank, Clayton, Joe and Gumbel were estimated to identify the dominant factors contributing to each crash consequence indicator; the temporal effects on model estimates were investigated; the elasticity effects of the independent variables with regard to all four crash consequence indicators were measured to express the magnitude of the effects of an independent variable on the probability change for each level of four indicators; and specific countermeasures were recommended for each of the contributing factors to improve the intersection safety. The model goodness-of-fit illustrates that the Joe copula model with the parameterized copula parameters outperforms the other models, which verifies that the injury severity, crash type, vehicle damage and driver error are significantly correlated due to common observed and unobserved factors and, accounting for their correlations, can lead to more accurate model estimation results. The parameterization of the copula function indicates that their correlation varies among different crashes, including crashes that occurred at stop-controlled intersections, four-leg intersections and crashes which involved drivers younger than 25. The model coefficient estimates indicate that the driver's age, driving under the influence of drugs and alcohol, intersection geometry and control types, and adverse weather and light conditions are the most critical factors contributing to severe crash consequences. The coefficient estimates of four-leg intersections, yield and stop-controlled intersections and adverse weather conditions varied over time, which indicates that the model estimation of crash data may not be stable over time and should be accommodated in crash prediction analysis. In the end, relevant countermeasures corresponding to law enforcement and intersection infrastructure design are recommended to all of the contributing factors identified by the model. It is anticipated that this study can shed light on selecting valid statistical models for crash data analysis, identifying intersection safety issues, and helping develop effective countermeasures to improve intersection safety.
An Integrated Driver-Vehicle-Environment (I-DVE) model to assess crash risks
2000
A wide range of driver and vehicle models have been proposed by traffic psychologists, engineers and traffic simulation researchers to assess crash risks. However, existing approaches are often confined within a single discipline and lack concepts that formally express the complexity of interactions between the driver, vehicle and environment as well as the broader scope and the interdisciplinary nature of
Validating a driving simulator using surrogate safety measures
Accident Analysis & Prevention, 2008
Traffic crash statistics and previous research have shown an increased risk of traffic crashes at signalized intersections. How to diagnose safety problems and develop effective countermeasures to reduce crash rate at intersections is a key task for traffic engineers and researchers. This study aims at investigating whether the driving simulator can be used as a valid tool to assess traffic safety at signalized intersections. In support of the research objective, this simulator validity study was conducted from two perspectives, a traffic parameter (speed) and a safety parameter (crash history). A signalized intersection with as many important features (including roadway geometries, traffic control devices, intersection surroundings, and buildings) was replicated into a high-fidelity driving simulator. A driving simulator experiment with eight scenarios at the intersection were conducted to determine if the subjects' speed behavior and traffic risk patterns in the driving simulator were similar to what were found at the real intersection. The experiment results showed that speed data observed from the field and in the simulator experiment both follow normal distributions and have equal means for each intersection approach, which validated the driving simulator in absolute terms. Furthermore, this study used an innovative approach of using surrogate safety measures from the simulator to contrast with the crash analysis for the field data. The simulator experiment results indicated that compared to the right-turn lane with the low rear-end crash history record (2 crashes), subjects showed a series of more risky behaviors at the right-turn lane with the high rear-end crash history record (16 crashes), including higher deceleration rate (1.80 ± 1.20 m/s 2 versus 0.80 ± 0.65 m/s 2), higher non-stop right-turn rate on red (81.67% versus 57.63%), higher right-turn speed as stop line (18.38 ± 8.90 km/h versus 14.68 ± 6.04 km/h), shorter following distance (30.19 ± 13.43 m versus 35.58 ± 13.41 m), and higher rear-end probability (9/59 = 0.153 versus 2/60 = 0.033). Therefore, the relative validity of driving simulator was well established for the traffic safety studies at signalized intersections.
Models of Driver Acceleration Behavior Prior to Real-World Intersection Crashes
IEEE Transactions on Intelligent Transportation Systems, 2017
Drivers involved in intersection collisions are at high risk of serious or fatal injury. Intersection advanced driver assistance systems (I-ADAS) are emerging active safety systems designed to help drivers safely traverse intersections. The effectiveness of I-ADAS is expected to be greatly dependent on pre-crash vehicle acceleration during intersection traversals. The objective of this paper was to develop pre-crash acceleration models for non-turning drivers involved in straight crossing path crashes and left-turning drivers in left turn across path opposite direction and lateral direction crashes. This paper used 348 event data recorder pre-crash records taken from crashes investigated as part of the National Automotive Sampling System/Crashworthiness Data System. The acceleration models generated from this pre-crash data were evaluated using a leave-one-out cross-validation procedure. Previously developed non-crash models from the literature were compared with the pre-crash models. Our hypothesis was that drivers involved in crashes would accelerate more aggressively than the "typical" driving population. This result suggests that drivers in pre-crash scenarios tend to accelerate more aggressively than drivers in normal scenarios (p<0.001). This has important implications for the design of I-ADAS. Specifically, higher acceleration results in less available time for I-ADAS to detect and respond to an imminent collision.
Examining driver injury severity in two vehicle crashes – A copula based approach
Accident Analysis & Prevention, 2014
A most commonly identified exogenous factor that significantly affects traffic crash injury severity sustained is the collision type variable. Most studies consider collision type only as an explanatory variable in modeling injury. However, it is possible that each collision type has a fundamentally distinct effect on injury severity sustained in the crash. In this paper, we examine the hypothesis that collision type fundamentally alters the injury severity pattern under consideration. Towards this end, we propose a joint modeling framework to study collision type and injury severity sustained as two dimensions of the severity process. We employ a copula based joint framework that ties the collision type (represented as a multinomial logit model) and injury severity (represented as an ordered logit model) through a closed form flexible dependency structure to study the injury severity process. The proposed approach also accommodates the potential heterogeneity (across drivers) in the dependency structure. Further, the study incorporates collision type as a vehicle-level, as opposed to a crash-level variable as hitherto assumed in earlier research, while also examining the impact of a comprehensive set of exogenous factors on driver injury severity. The proposed modeling system is estimated using collision data from the province of Victoria, Australia for the years 2006 through 2010.
Copula-Based Joint Model of Injury Severity and Vehicle Damage in Two-Vehicle Crashes
Transportation Research Record: Journal of the Transportation Research Board, 2015
In the transportation safety field, in an effort to improve safety, statistical models are developed to identify factors that contribute to crashes as well as those that affect injury severity. This study contributes to the literature on severity analysis. Injury severity and vehicle damage are two important indicators of severity in crashes and are typically modeled independently. However, there are common observed and unobserved factors affecting the two crash indicators that lead to potential interrelationships. Failure to account for the interrelationships between the indicators may lead to biased coefficient estimates in crash severity prediction models. The focus of this study was to explore interrelationships between injury severity and vehicle damage and to also identify the nature of these correlations across different types of crashes. A copula-based methodology that could simultaneously model injury severity and vehicle damage while also accounting for the interrelationsh...
Comparing Simulated Road Safety Performance to observed Crash Frequency at Signalized Intersections
2011
Microscopic traffic simulation has been developed and applied over the past two decades with the main focus towards the design and operations of transportation systems. Recently, due to advancements in data collection techniques and microscopic algorithms, the potential of microscopic simulation as a tool for safety assessments has been under considerable debate. This type of approach may allow better knowledge regarding the chain of events preceding crash occurrences; therefore, leading to a more comprehensive methodology for safety studies when compared to traditional observational studies. This paper presented a validation effort between observed rear-end collisions and simulated traffic conflicts, as reflected by three Safety Performance Measures (SPM) namely: Time to Collision (TTC), Deceleration Rate to Avoid the Crash (DRAC) and Crash Potential Index (CPI). Three years of accident data (2007-2009) for two-hour peak (7:00AM - 9:00AM) and off-peak (9:00AM - 11:00AM) were compar...