Development of Freeway Traffic Incident Clearance Time Prediction Model by Accident Level

Lee, Soong-bong; Han, Dong Hee; Lee, Young-Ihn

doi:10.7470/jkst.2015.33.5.497

Cited by 6 publications

(8 citation statements)

References 5 publications

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“…Other statistical models adopted in incident duration modeling may include linear regression and structural equation modeling (15)(16)(17)(18)(19)(20). These models do not rely on the concepts of hazard and survival functions and thus can only estimate the duration of incidents without the explanatory powers during the event episodes.…”

Section: Literature Reviewmentioning

confidence: 99%

From Traffic Analysis to Real-Time Management: A Hazard-Based Modeling for Incident Durations Extracted Through Traffic Detector Data Anomaly Detection

Pan

Hamdar

2023

Transportation Research Record: Journal of the Transportation R

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The time and location of occurrence, among other characteristics of incidents, are needed to distribute traveler information and dispatch emergency service for traffic congestion and safety mitigation purposes. However, the corresponding clearance/recovery time is an important, yet under-studied, subject of research. Existing studies on incident duration modeling mainly utilize duration data gathered and provided by authorities through the efforts of individual agents. This type of manual data gathering may suffer coverage and consistency issues. In addition, the explanatory variables considered in these studies are static, that is, observed at the incident formation stage and assumed to be consistent over the incident periods. This setup, however, overlooks traffic flow dynamics; time-varying traffic variables during the incident episodes affect incidents clearance and disruption characteristics. In line with the above limitations, the objective of this study is to utilize hazard-based modeling to explain incident durations mined from traffic detector data while factoring in traffic flow dynamics. The 2014–2016 Virginia statewide traffic detector data were utilized to extract incident durations by implementing a machine-learning-based incident detection algorithm followed by a semi-parametric proportional hazard function. Such a function accounts for the time-varying traffic-descriptive covariates for incident duration modeling. The resulting monotonically decreasing baseline hazard reveals the snowball effect and the inertia effect of disruptions induced by incidents, which leads to the advice that incidents should be detected and mitigated within a 30 min duration. The normalized flow, density, and speed measures, and their temporal differences are all shown to be significant covariates scaling up or down the hazard.

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Section: Literature Reviewmentioning

confidence: 99%

From Traffic Analysis to Real-Time Management: A Hazard-Based Modeling for Incident Durations Extracted Through Traffic Detector Data Anomaly Detection

Pan

Hamdar

2023

Transportation Research Record: Journal of the Transportation R

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“…Zhang et al 17 used accident causality theory to construct the causality model of construction accidents and identify the key accident causes and divided them into three levels according to the severity, providing suggestions for construction safety management and accident prevention. To help relevant personnel to conduct efficient highway operation management when accidents occur, Lee et al 42 developed a model for predicting the time required to recover from a traffic accident considering the accident level. Xu et al 16 explored the correlation between accident severity and heterogeneity due to unobserved factors by using a seemingly unrelated regression (SUR) model.…”

Section: Impact Of the Accident Levelmentioning

confidence: 99%

“…High-level accidents should be given more attention in safety management, but there is still a lack of research on considering accident levels in causal analysis. Related research [14][15][16]42,43 mostly focuses on traffic accident severity. Therefore, based on network theory, this paper describes studying the types of risk factors in different types of construction accidents from the perspective of considering the accident level.…”

Section: Impact Of the Accident Levelmentioning

confidence: 99%

Risk analysis of construction accidents with a weighted network model considering accident level

Chen

Lei

Wei

2022

Quality & Reliability Eng

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Construction is considered to be one of the most dangerous industries due to its wide variety of accidents and high risk. Although people have been using various means to improve safety levels, the frequent occurrence of accidents still plagues the development of this industry. Accurately grasping the characteristics and analyzing the complex relationships between the risks of different accident types are the keys to a comprehensive understanding and promotion of construction safety. Simultaneously, we need to note that the level and frequency of accidents affects the risk characteristics, but previous studies have not paid sufficient attention to these features. Using complex network theory, based on the risk factors and risk triggering relationship extracted from construction accidents and considering the accident level and the frequency of occurrences as their weights, several weighted risk networks are generated according to different accident types. Then, several network analysis metrics are introduced to reveal the characteristics of the risk network and identify the key risk factors. The analytical framework helps to pose targeted suggestions to reduce the occurrence of frequent major accidents.

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“…The model performance is better if the value of MAPE is smaller and R 2 is larger. [41][42][43][44] The traffic fatalities training curve based on SVM prediction model is shown in Figure 2. The black curve represents the actual output, while yellow one represents the predicted fitting output.…”

Section: Data Normalizationmentioning

confidence: 99%

Traffic fatalities prediction using support vector machine with hybrid particle swarm optimization

Wang

et al. 2017

Journal of Algorithms & Computational Technology

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Road traffic safety is essential, therefore in order to predict traffic fatalities effectively and promote the harmonious development of transportation, a traffic fatalities prediction model based on support vector machine is established in this paper. The selection of parameters greatly affects the prediction accuracy of support vector machine. Introducing particle swarm optimization can find the optimal parameters and improve the prediction accuracy of support vector machine by parameter optimization. However, standard particle swarm optimization is easy to trap into the local optimum, so that the best parameter solutions cannot be found. Therefore, the mutation operation of the genetic algorithm is introduced into particle swarm optimization, particle swarm with mutation optimization is generated. It expands the search space and makes parameter selection more accurate. This paper predicts fatalities of traffic accident using small samples and nonlinear data. The results show that compared with particle swarm with mutation optimization back propagation neural network prediction model, particle swarm optimization-support vector machine model, support vector machine, back propagation neural network, K Nearest Neighbor (K-NN), and Bayesian network, the prediction model of traffic fatalities based on particle swarm with mutation optimization-support vector machine has higher prediction precision and smaller errors. It is feasible and effective to use particle swarm with mutation optimization to optimize the parameters of support vector machine, and this model can predict the accident more accurately.

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Development of Freeway Traffic Incident Clearance Time Prediction Model by Accident Level

Cited by 6 publications

References 5 publications

From Traffic Analysis to Real-Time Management: A Hazard-Based Modeling for Incident Durations Extracted Through Traffic Detector Data Anomaly Detection

From Traffic Analysis to Real-Time Management: A Hazard-Based Modeling for Incident Durations Extracted Through Traffic Detector Data Anomaly Detection

Risk analysis of construction accidents with a weighted network model considering accident level

Traffic fatalities prediction using support vector machine with hybrid particle swarm optimization

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