Investigation of injury severities in single-vehicle crashes in North Carolina using mixed logit models

Roque, Carlos; Jalayer, Mohammad; Hasan, Ahmed Sajid

doi:10.1016/j.jsr.2021.02.013

Cited by 20 publications

(3 citation statements)

References 31 publications

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“…In 1974, UT began to be widely used in transportation, and McFadden [17] proposed the multinomial logit model (MNL), which further refned the theory of random utility maximization (RUM). Subsequently, the form of MNL models has been gradually expanded to more fexible nested logit (NL) models [18], mixed logit (ML) models [19], latent class logit models (LCM) [20], and so on. Currently, UT is often applied to travel route choice behavior in transportation travel behavior.…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling the Traveler’s Route Choice Behavior under Unexpected Accidents

Zhu

Shi

Cui

et al. 2023

Journal of Advanced Transportation

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To investigate the route choice behavior of travelers under unexpected accidents, this study designs four different scenarios of travelers’ route choice behavior experiments according to the severity of unexpected accidents. The bounded rationality characteristics of travelers, such as the time perception difference coefficient, psychological threshold effect coefficient, and scale effect, are introduced into the generalized random regret minimization (GRRM) model. An improved generalized random regret minimization (IGRRM) model is constructed based on travelers’ route choice behavior under unexpected accidents. The collected data of travelers’ route choice results under four different congestion-level scenarios are analyzed by the IGRRM model. The study finds that with the increase in congestion level, travelers are more willing to change the route; during the commute, travelers tend to choose the route with less travel time and angular cost; in the moderate and serious congestion scenarios, travelers no longer reject the detour route; attribute perception difference, scale effect, and psychological threshold effect; affect travelers’ route choice behavior. The IGRRM model based on the route choice behavior of travelers can more accurately characterize the route choice behavior of travelers under unexpected accidents, provide a basis for traffic flow distribution under unexpected accidents, and benefit traffic management departments to take traffic control and guidance measures to ease the traffic congestion caused by unexpected accidents.

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Section: Literature Reviewmentioning

confidence: 99%

Modeling the Traveler’s Route Choice Behavior under Unexpected Accidents

Zhu

Shi

Cui

et al. 2023

Journal of Advanced Transportation

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“…As seen in Table 1 , some earlier studies investigated the contributing factors to injury severity in single-vehicle crashes using aggregate crash data 7 – 15 . In contrast, other research studies analyzed single-vehicle crash-injury severities using disaggregated data; for example, crashes on divided/undivided urban road 16 , crashes on rural/urban roadways 17 , crashes involving unimpaired/alcohol-impaired/drug-impaired drivers 18 , crashes with one-/two-/three-occupants 19 , riders/drivers of the crashes 20 , crashes on 2-lane/4-lane roadway 21 , crashes with difference light/weather condition 22 , familiar/unfamiliar drivers of the crashes 23 , passenger car/SUV crashes 24 , crashes under different weather scenarios 25 , 26 , fixed-object/overturn crashes 27 , crashes on arterial/ secondary/branch roadway 28 , and crashes from different period (temporal instability) 4 , 29 – 34 . However, none of the aforementioned literature investigated single-vehicle crashes using disaggregated data concerning restrained and unrestrained drivers, while also accounting for their speeding violation behavior in the crashes.…”

Section: Literature Reviewmentioning

confidence: 99%

Temporal instability and differences in injury severity between restrained and unrestrained drivers in speeding-related crashes

Champahom

Wisutwattanasak

et al. 2023

Sci Rep

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Upon detecting a crash impact, the vehicle restraint system locks the driver in place. However, external factors such as speeding, crash mechanisms, roadway attributes, vehicle type, and the surrounding environment typically contribute to the driver being jostled within the vehicle. As a result, it is crucial to model unrestrained and restrained drivers separately to reveal the true impact of the restraint system and other factors on driver injury severities. This paper aims to explore the differences in factors affecting injury severity for seatbelt-restrained and unrestrained drivers involved in speeding-related crashes while accounting for temporal instability in the investigation. Utilizing crash data from Thailand between 2012 and 2017, mixed logit models with heterogeneity in means and variances were employed to account for multi-layered unobserved heterogeneity. For restrained drivers, the risk of fatal or severe crashes was positively associated with factors such as male drivers, alcohol influence, flush/barrier median roadways, sloped roadways, vans, running off the roadway without roadside guardrails, and nighttime on unlit or lit roads. For unrestrained drivers, the likelihood of fatal or severe injuries increased in crashes involving older drivers, alcohol influence, raised or depressed median roadways, four-lane roadways, passenger cars, running off the roadway without roadside guardrails, and crashes occurring in rainy conditions. The out-of-sample prediction simulation results are particularly significant, as they show the maximum safety benefits achievable solely by using a vehicle's seatbelt system. Likelihood ratio test and predictive comparison findings highlight the considerable combined impact of temporal instability and the non-transferability of restrained and unrestrained driver injury severities across the periods studied. This finding also demonstrates a potential reduction in severe and fatal injury rates by simply replicating restrained driver conditions. The findings should be of value to policymakers, decision-makers, and highway engineers when developing potential countermeasures to improve driver safety and reduce the frequency of severe and fatal speeding-related single-vehicle crashes.

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“…Such fact has driven the development of statistical techniques to identify the unobserved heterogeneity. To accommodate the discrete nature of crash severity (no injury, slight injury, serious injury, and fatality), various regression approaches-random parameters logit (RP-logit) model [38,39], random parameters probit model [40], random intercept logit model [41], latent class logit model [10], and finite mixture random parameters model [16,42]-have been widely recommended due to their high flexibility [43][44][45]. Alternatively, random parameters ordered logit model [46] and random parameters ordered probit model [47] were applied to handle the intuitive ordering of crash severity.…”

Section: Statistical Techniques For Unobserved Heterogeneity and Spatial Correlationmentioning

confidence: 99%

Modeling of Low Visibility-Related Rural Single-Vehicle Crashes Considering Unobserved Heterogeneity and Spatial Correlation

et al. 2021

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Accident analysis and prevention are helpful to ensure the sustainable development of transportation. The aim of this research was to investigate the factors associated with the severity of low-visibility-related rural single-vehicle crashes. Firstly, a latent class clustering model was implemented to partition the whole-dataset into a relatively homogeneous sub-dataset. Then, a spatial random parameters logit model was established for each dataset to capture unobserved heterogeneity and spatial correlation. Analysis was conducted based on the crash data (2014–2019) from 110 two-lane road segments. The results show that the proposed method is a superior crash severity modeling approach to accommodate the unobserved heterogeneity and spatial correlation. Three variables—seatbelt not used, motorcycle, and collision with fixed object—have a stable positive correlation with crash severity. Motorcycle leads to a 12.8%, 23.8%, and 12.6% increase in the risk of serious crashes in the whole-dataset, cluster 3, and cluster 4, respectively. In the whole-dataset, cluster 2, and cluster 3, the risk of serious crashes caused by seatbelt not used increased by 5.5%, 0.1%, and 30.6%, respectively, and caused by collision with fixed object increased by 33.2%, 1.2%, and 13.2%, respectively. The results can provide valuable information for engineers and policy makers to develop targeted measures.

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Investigation of injury severities in single-vehicle crashes in North Carolina using mixed logit models

Cited by 20 publications

References 31 publications

Modeling the Traveler’s Route Choice Behavior under Unexpected Accidents

Modeling the Traveler’s Route Choice Behavior under Unexpected Accidents

Temporal instability and differences in injury severity between restrained and unrestrained drivers in speeding-related crashes

Modeling of Low Visibility-Related Rural Single-Vehicle Crashes Considering Unobserved Heterogeneity and Spatial Correlation

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