A highway crash risk assessment method based on traffic safety state division

Sun, Dongye; Ai, Yunfei; Sun, Yan Quan; Zhao, Liping

doi:10.1371/journal.pone.0227609

Cited by 12 publications

(5 citation statements)

References 26 publications

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“…In recent years, the concept of active safety has gradually entered the vision of researchers. More and more studies [8][9][10] have found that trafc fow state has a strong correlation with the road trafc crash occurrence. For example, Lee et al used the trafc fow data within 5 minutes before the crash to study the infuence of trafc fow dynamic characteristics on the collision pattern of the crash [11].…”

Section: Introductionmentioning

confidence: 99%

Highway Traffic Crash Risk Prediction Method considering Temporal Correlation Characteristics

Zhao¹,

Li²,

Sun³

et al. 2023

Journal of Advanced Transportation

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Crash risk analysis and prediction are considered the premise of highway traffic safety control, which directly affects the accuracy and effectiveness of traffic safety decisions. A highway traffic crash risk prediction method considering temporal correlation characteristics is proposed in this research. Firstly, the case-control sample analysis method is used to extract 6 time series sample data composed of crash traffic flow data and corresponding non-crash traffic flow data for crash risk analysis and prediction. Secondly, the multiparameter fusion clustering analysis method is used to indicate that the sample data of different time series have different effects on the crash risk. Then, the random forest model is used to screen several traffic flow variables that affect the highway crash risk. Thereafter, the downstream mean speed (ASD1D2), the upstream mean occupancy (AOU1U2), and the speed difference (DSU1D1) on the nearest detector were determined as the explanatory variables of the crash risk prediction model. Finally, based on the three variables, the dynamic Bayesian network model for highway traffic crash risk prediction is proposed. The overall prediction accuracy of this model is 84.9%, the crash prediction accuracy is 60.8%, and the non-crash prediction accuracy is 92.3%. Also, the prediction results show that the dynamic Bayesian model has better prediction effect than the static Bayesian model for the same sample data.

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Section: Introductionmentioning

confidence: 99%

Highway Traffic Crash Risk Prediction Method considering Temporal Correlation Characteristics

Zhao¹,

Li²,

Sun³

et al. 2023

Journal of Advanced Transportation

Self Cite

View full text Add to dashboard Cite

show abstract

“…The success rate of traffic accident risk management depends on the achievement of each supporting performance system indicator (da Costa et al, 2016). Road safety management and control has the main task of assessing the risk of traffic accidents based on case studies in order to improve the control of the risk of high traffic accidents in traffic (Sun et al, 2020).…”

Section: Go Downmentioning

confidence: 99%

Implementation of Traffic Safety Management on Road Sections with Non-Standard Grades

Laksana

Sabarudin

Hakim

2022

EAJMR

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The majority of traffic accidents were caused by the negligence of drivers and road users, followed by vehicle factors, and road conditions as the following variable. The focus of this research is on traffic safety managementcaused by the level of incline and descent on the Sofi-Wayabula road, Morotai Regency. This quantitative descriptive research involved traffic management, maintenance, and patrol supervisors. The research instrument applied consisting observation sheets, interview, and documentation research sheets. The grade or road slope outside the standard requires the mitigation of traffic sign engineering. This study proposes it through the implementation of the Building Information Modeling or BIM method supported by the optimization of 3D animation of traffic signs, especially in North Maluku. The research findings indicated that a number of drivers were assisted by the implementation of 3D animation engineering of traffic signs at a number of accident-prone points along the 10.9 km on the Sofi-Wayabula road, Morotai Regency, North Maluku Province. The research targets agreed by the researcher were effective with traffic safety governance supported by renewable technology methods and engineering.

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“…However, if significant fatal-collision causative factors are identified, those factors could also be considered in the likelihood function. Therefore, evaluating the impact of other causative factors such as weather, roadway geometry, and traffic characteristics in classifying sites as R [34][35][36][37] and other likelihood functions that consider those factors will be the subjects of future study. The authors also plan to further evaluate the performance of the proposed method along rural highways or urban area where the characteristics of fatal collisions can differ from those of this study [38].…”

Section: Plos Onementioning

confidence: 99%

Application of naïve Bayesian approach in detecting reproducible fatal collision locations on freeway

et al. 2021

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Detecting high-collision-concentration locations based solely on collision frequency may produce different results compared to those considering the severities of the collisions. In particular, it can lead government agencies focusing sites with a high collision frequency while neglecting those with a lower collision frequency but a higher percentage of injury and fatal collisions. This study developed systematic ways of detecting reproducible fatal collision locations (R) using the naïve Bayes approach and a continuous risk profile (CRP) that estimates the true collision risk by filtering out random noise in the data. The posterior probability of fatal collisions being reproducible at a location is estimated by the relationship between the spatial distribution of fatal-collision locations (i.e., likelihood) and the CRP (i.e., prior probability). The proposed method can be used to detect sites with the highest proxy measure of the posterior probability (PMP) of observing R. An empirical evaluation using 5-year traffic collision data from six routes in California shows that detecting R based on the PMP outperform those based on the SPF-based approaches or random selection, regardless of various conditions and parameters of the proposed method. This method only requires traffic collision and annual traffic volume data to estimate PMP that prioritize sites being R and the PMPs can be compared across multiple routes. Therefore, it helps government agencies prioritizing sites of multiple routes where the number of fatal collisions can be reduced, thus help them to save lives with limited resources of data collection.

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A highway crash risk assessment method based on traffic safety state division

Cited by 12 publications

References 26 publications

Highway Traffic Crash Risk Prediction Method considering Temporal Correlation Characteristics

Highway Traffic Crash Risk Prediction Method considering Temporal Correlation Characteristics

Implementation of Traffic Safety Management on Road Sections with Non-Standard Grades

Application of naïve Bayesian approach in detecting reproducible fatal collision locations on freeway

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