Influence of Multiple Freeway Design Features on Freight Traffic Safety

Park, Juneyoung; Abdel-Aty, Mohamed; Wang, Ling; Lee, Gunwoo; Hong, Jungyeol

doi:10.1155/2019/5739496

Cited by 6 publications

(3 citation statements)

References 39 publications

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“…As a result of the analysis, the driver's physical condition, improper passing, speeding, and safe distance violations were found. Park et al (2019) [22] developed multivariate adaptive regression splines (MARS) models to analyze all types and freight vehicle crashes. MARS models show better model fitness than the negative binomial (NB) models.…”

Section: Freight Vehicle Safetymentioning

confidence: 99%

Multilevel Mixed-Effects Models to Identify Contributing Factors on Freight Vehicle Crash Severity

Park

2022

Sustainability

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Freight vehicle crashes are more serious than regular vehicle crashes because they are likely to lead to major damage and injury once they occur; therefore, countermeasures are needed. The fatality rate from freight vehicle crashes is 1.5 times higher than that of all other accidents, and the death rate from expressway freight vehicle crashes continues to increase. In this study, the ten-freight-vehicle crash severity models (the ordered logit and probit model, the multinomial logit and probit model, mixed-effects logit and probit model, random-effects ordered logit and probit model, and multilevel mixed-effects ordered logit and probit model) are used to analyze the freight vehicle crash severity factors. The model was constructed using data collected from expressways over eight years, and 13 factors were derived to increase the severity of crashes and 7 factors to reduce the severity of crashes. As a result of comparing the 10 constructed models using AIC and BIC, the multilevel mixed-effects ordered probit model showed the best performance. It is expected that it can contribute to improving the safety of freight vehicles in the expressway section by utilizing factors related to the severity of crashes derived from this study.

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Section: Freight Vehicle Safetymentioning

confidence: 99%

Multilevel Mixed-Effects Models to Identify Contributing Factors on Freight Vehicle Crash Severity

Park

2022

Sustainability

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“…Under the premise of meeting reasonable service level, the one-way configuration of more than three highway lanes is referred to as a "multi-lane highway." With the growth of car ownership and road traffic demand, the number of new or expanded multi-lane highways will gradually increase [2]. Compared to ordinary highways, multi-lane highways have a higher traffic flow, more complex traffic conditions, and more factors affecting the occurrence of traffic accidents, so safety research in multi-lane highway situations is essential.…”

Section: Introductionmentioning

confidence: 99%

Research on the Safety of the Left Hard Shoulder in a Multi-Lane Highway Based on Safety Performance Function

Zhao

et al. 2022

Sustainability

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The left hard shoulder plays an important role in the event of an emergency on the inside of a multi-lane highway, but past studies have not been able to clarify the criteria for its installation or quantify the safety impact of its installation on the left side. In order to study the influence of the left hard shoulder on the safety of vehicles traveling on multi-lane highways, based on past studies that only studied the situation of four-lane highways, this paper firstly constructs a multi-lane highway simulation model under different numbers of lanes based on the VISSIM traffic simulation and uses Surrogate Safety Assessment Model (SSAM) to study the conflict characteristics of multi-lane highway vehicles under different numbers of lanes. Based on the above findings, this paper introduces the Safety Performance Function (SPF) to construct a multi-lane freeway accident prediction model, calibrates the model by adding the indexes affected by the left side hard shoulder to the basic prediction mode, and uses the historical accident data of the Badou-Shihu section of the Guangdong Northern Second Ring Highway as the basis to study the differences in accident rates of the investigated section before and after setting the left hard shoulder. The study showed that the average Time to Collision (TTC) increased by 57.2%, Maximum Deceleration (MaxD) increased by 19.2%, and Delta Speed (DeltaS) increased by 15.3% after setting hard shoulders on the left side of multi-lane freeways, and traffic conflicts on multi-lane freeways were significantly reduced, and safety was improved considerably. In addition, the rear-end conflict rate decreased by 0.17%, 0.75%, and 4.6% after setting hard shoulders on the left side of one-way three, four, and five lanes, respectively, indicating that hard shoulders on the left side are the most effective in improving the safety of one-way five-lane freeways. The accident prediction results show that within the reasonable setting range of the left hard shoulder width (0~4 m), the accident rate decreases by about 1.5% for every 0.5 m increase if only the influence of the left hard shoulder width is considered. Without considering other factors, increasing the width of the hard shoulder on the left side can reduce the number of accidents. This indicates a significant safety improvement for a one-way five-lane highway after setting the hard shoulder on the left side, and the conclusion is consistent with the simulation results. In this paper, based on past research, the research object is extended to one-way three-, four-, and five-lane highways. The findings of this paper can help the road authorities develop specifications for installing hard shoulders on the left side of multi-lane freeways and adopt strategies to improve the traffic safety level of multi-lane freeways. In addition, the models and methods used in this paper can also help build a framework for future intelligent networked vehicle avoidance systems and promote the development of intelligent networked technologies.

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“…They use a variety of traffic accident-related information (human, road geometry, environmental factors, etc.) to find causal relationships and to predict the frequency of traffic accidents [10][11][12][13]. They analyze the predicted information and indicate a traffic safety level.…”

Section: Introductionmentioning

confidence: 99%

Urban traffic accident risk prediction for knowledge-based mobile multimedia service

Park

Hong

2020

Pers Ubiquit Comput

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Traditional accident prediction models have been mostly designed with statistical analysis that finds and analyzes the causal relationships between traffic accidents and a variety of human, road geometry, and environmental factors. However, these statistical methods have limitations in that they are based on assumptions about data distribution and function type. Therefore, this study suggests an accident prediction model using deep learning. This newly suggested risk prediction model is for predicting risk by reflecting static features of the road, such its length and the speed limit on it, and dynamic features of the road, such as traffic volume when driving on it, and the altitude and azimuth of the sun. For this purpose, 4470 accident cases, collected over 5 months from August to December 2018 in Seoul-the most complex, high-traffic, and accident-prone city in Korea-were analyzed. As a result of testing the model using such data, it was found to have an accuracy of 75% and recall of 81%. Based on testing results for the suggested risk prediction model, a system was developed to guide not only accident-prone regions predicted using statistical data but to also guide a risk level for the road. This level of risk is estimated based upon each given situation, so it can change even for the same road. This guide system can be used to provide a level of risk for each road segment and region but also to improve roads with recommendations, such as installation of safety features. In addition, it could support a mobile system that provides a driver with the optimized driving path for safety.

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Influence of Multiple Freeway Design Features on Freight Traffic Safety

Cited by 6 publications

References 39 publications

Multilevel Mixed-Effects Models to Identify Contributing Factors on Freight Vehicle Crash Severity

Multilevel Mixed-Effects Models to Identify Contributing Factors on Freight Vehicle Crash Severity

Research on the Safety of the Left Hard Shoulder in a Multi-Lane Highway Based on Safety Performance Function

Urban traffic accident risk prediction for knowledge-based mobile multimedia service

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