Improving roadside design policies for safety enhancement using hazard-based duration modeling

Roque, Carlos; Jalayer, Mohammad

doi:10.1016/j.aap.2018.08.008

Cited by 24 publications

(18 citation statements)

References 29 publications

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“…For examples, researchers have traditionally used duration models to analyse activity and travel patterns (Juan & Xianyu, 2010;Joly 2006;Bhat, 1996;Mannering, et al, 1994) and vehicle ownership (De Jong, 1996;Gilbert, 1992). More recently, duration models have also been applied to better understand accident data, including modelling of traffic incident duration (Tavassoli Hojati, et al, 2013), crossing behaviour of cyclists at intersections (Yang, et al, 2015), driver braking times (Fu, et al, 2016;Bella & Silvestri, 2016), vehicle distance travelled in run-off-road crashes (Roque & Jalayer 2018), and minimum gap time for lane changing (Ali, et al 2019). Other applications duration models to understand the policy adoption process for municipal car ownership and use restrictions among Chinese cities.…”

Section: Impacts Of Local Context On Policy Adoptionmentioning

confidence: 99%

What prompts the adoption of car restriction policies among Chinese cities

Wang

Moody

Zhao

2020

International Journal of Sustainable Transportation

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Facing rapid motorization, many Chinese municipalities are implementing policies that restrict car ownership or use. However, there is significant variation in terms of which cities adopt these policies and when. This research systematically investigates what factors prompt local governments in China to adopt these car restriction policies. We collect a database of car restriction policies as well as economic, demographic, land use, and transportation indicators for 287 Chinese municipalities from 2001 to 2014. We adopt a mixed methods approach that combines a qualitative investigation of stated objectives and legislative precedent within policy documents with a quantitative duration model of policy adoption. We find that the adoption of comprehensive car ownership and use restriction policies across Chinese cities primarily responds to local air pollution and secondarily to car ownership and congestion. Policy adoption additionally responds to local subway line constructions. Local economic power and population size do not effectively explain policy adoption. Idiosyncratic effects at provincial or city levels are important, although the underlying mechanisms by which these network effects manifest remain unclear. Broadly, our findings suggest that problem solving and network effects both contribute to the adoption of car restriction policies across China's cities and that the legal policy documents reliably illustrate the motivations of these policies.

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Section: Impacts Of Local Context On Policy Adoptionmentioning

confidence: 99%

What prompts the adoption of car restriction policies among Chinese cities

Wang

Moody

Zhao

2020

International Journal of Sustainable Transportation

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“…Roque and Jalayer developed a hazard-based duration model to understand the distance traveled by errant vehicles in ROR crashes and their associated factors. Based on annual ROR crashes data, the extent severity was related to roadway and roadside geometric design features, including lane width and clear zones [24].…”

Section: Literature Reviewmentioning

confidence: 99%

“…In addition, some of the safety risk factors for rural roadsides were not included in previous studies, which may not provide a complete picture of rural roadside safety risk. Thus, the relationship obtained in previous studies between the factors and safety risk is possibly also biased [15,16,17,18,19,20,21,22,23,24].…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Safety Risk of Rural Roadsides Using a Bayesian Network Method

Tang

Zhu

Guo

et al. 2019

IJERPH

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Evaluating the safety risk of rural roadsides is critical for achieving reasonable allocation of a limited budget and avoiding excessive installation of safety facilities. To assess the safety risk of rural roadsides when the crash data are unavailable or missing, this study proposed a Bayesian Network (BN) method that uses the experts’ judgments on the conditional probability of different safety risk factors to evaluate the safety risk of rural roadsides. Eight factors were considered, including seven factors identified in the literature and a new factor named access point density. To validate the effectiveness of the proposed method, a case study was conducted using 19.42 km long road networks in the rural area of Nantong, China. By comparing the results of the proposed method and run-off-road (ROR) crash data from 2015–2016 in the study area, the road segments with higher safety risk levels identified by the proposed method were found to be statistically significantly correlated with higher crash severity based on the crash data. In addition, by comparing the respective results evaluated by eight factors and seven factors (a new factor removed), we also found that access point density significantly contributed to the safety risk of rural roadsides. These results show that the proposed method can be considered as a low-cost solution to evaluating the safety risk of rural roadsides with relatively high accuracy, especially for areas with large rural road networks and incomplete ROR crash data due to budget limitation, human errors, negligence, or inconsistent crash recordings.

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“…In terms of the severity of accidents involving roadside trees, Daniello and Gabler [5] investigated 3,600 collisions between motor vehicles and roadside fixed objects in the US from 2004 to 2008 and believed that the death risk in collisions with roadside fixed objects was considerably higher than that in collisions with the ground, with the death rate of collisions with a guardrail and the death rate of collisions with trees being 7 times and 15 times, respectively, that of collisions with the ground. By collecting roadside accident data, Roque and Jalayer [6] revealed that the severity of collisions between vehicles and trees was twice that of collisions with other obstacles. Holdridge and Shankar et al [7] employed the multinested logit model to confirm that planting thick and strong trees on the roadside tended to increase the probability of fatal roadside accidents.…”

Section: Introductionmentioning

confidence: 99%

Severity assessment of accidents involving roadside trees based on occupant injury analysis

Cheng

Pei

et al. 2020

PLoS ONE

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The aims of this study were to achieve a quantitative assessment of the severity of accidents involving roadside trees on highways and to propose corresponding safety measures to reduce accident losses. This paper used the acceleration severity index (ASI), head injury criteria (HIC) and chest resultant acceleration (CRA) as indicators of occupant injuries and horizontal radii, vehicle departure speeds, tree diameters and roadside tree spacing as research variables to carry out bias collision tests between cars, trucks and trees by constructing a vehicle rigid body system and an occupant multibody system in PC-crash 10.0® simulation software. A total of 2,256 data points were collected. For straight and curved segments of highways, the occupant injury evaluation models of cars were fitted based on the CRA, and occupant injury evaluation models of trucks and cars were fitted based on the ASI. According to the Fisher optimal segmentation method, reasonable classification standards of severities of accidents involving roadside trees and the corresponding ASI and CRA thresholds were determined, and severity assessment methods for accidents involving roadside trees based on the CRA and ASI were provided. Additionally, a new index by which to evaluate the accuracy of the accident severity classification and the degree of misclassification was built and applied for the validity verification of the proposed severity assessment methods. A proportion of trucks was introduced to further improve the ASI evaluation model. For the same simulation conditions, the results show that driver chest injuries are more serious than driver head injuries and that the average ASI of cars is greater than that of trucks. The CRA and ASI have a positive linear correlation with the departure speed and a logarithmic correlation with the roadside tree diameters. The larger the spacing of roadside trees is and the smaller the horizontal radius is, the smaller the chance that a vehicle will experience a second collision and the lower the risk of occupant injury. In method validation, the evaluation results from two proposed severity assessment methods based on the CRA and ASI are consistent, and the degrees of misclassification are 4.65% and 4.26%, respectively, which verifies the accuracy of the methods proposed in this paper and confirms

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Improving roadside design policies for safety enhancement using hazard-based duration modeling

Cited by 24 publications

References 29 publications

What prompts the adoption of car restriction policies among Chinese cities

What prompts the adoption of car restriction policies among Chinese cities

Evaluating the Safety Risk of Rural Roadsides Using a Bayesian Network Method

Severity assessment of accidents involving roadside trees based on occupant injury analysis

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