Analysis of the Different Duration Stages of Accidents with Hazard-Based Model

Li, Ruimin; Guo, Min; Lu, Huapu

doi:10.1007/s13177-015-0115-6

Cited by 8 publications

(4 citation statements)

References 22 publications

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“…Different statistical methods and machine learning methods have been applied in traffic incident duration prediction, including tree-based method (Lin et al , 2016; Weng et al , 2015), Bayesian classifier (Cong et al , 2018; Ozbay and Noyan, 2006; Zou et al , 2021), hazard-based method (Haule et al , 2019; Li et al , 2017; Li et al , 2015) and ANN (Lee et al , 2017). Among these methods, the accelerated failure time (AFT) model is the most widely used hazard-based method (Li et al , 2017; Li et al , 2015). It assumes that the factors related to the incident will accelerate or decelerate the incident duration; thus, it is easily interpreted (Kay and Kinnersley, 2002).…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic prediction of traffic incident duration on urban expressways: a deep learning approach based on LSTM and MLP

Zhu¹,

et al. 2021

JICV

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Purpose Efficient traffic incident management is needed to alleviate the negative impact of traffic incidents. Accurate and reliable estimation of traffic incident duration is of great importance for traffic incident management. Previous studies have proposed models for traffic incident duration prediction; however, most of these studies focus on the total duration and could not update prediction results in real-time. From a traveler’s perspective, the relevant factor is the residual duration of the impact of the traffic incident. Besides, few (if any) studies have used dynamic traffic flow parameters in the prediction models. This paper aims to propose a framework to fill these gaps. Design/methodology/approach This paper proposes a framework based on the multi-layer perception (MLP) and long short-term memory (LSTM) model. The proposed methodology integrates traffic incident-related factors and real-time traffic flow parameters to predict the residual traffic incident duration. To validate the effectiveness of the framework, traffic incident data and traffic flow data from Shanghai Zhonghuan Expressway are used for modeling training and testing. Findings Results show that the model with 30-min time window and taking both traffic volume and speed as inputs performed best. The area under the curve values exceed 0.85 and the prediction accuracies exceed 0.75. These indicators demonstrated that the model is appropriate for this study context. The model provides new insights into traffic incident duration prediction. Research limitations/implications The incident samples applied by this study might not be enough and the variables are not abundant. The number of injuries and casualties, more detailed description of the incident location and other variables are expected to be used to characterize the traffic incident comprehensively. The framework needs to be further validated through a sufficiently large number of variables and locations. Practical implications The framework can help reduce the impacts of incidents on the safety of efficiency of road traffic once implemented in intelligent transport system and traffic management systems in future practical applications. Originality/value This study uses two artificial neural network methods, MLP and LSTM, to establish a framework aiming at providing accurate and time-efficient information on traffic incident duration in the future for transportation operators and travelers. This study will contribute to the deployment of emergency management and urban traffic navigation planning.

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Section: Literature Reviewmentioning

confidence: 99%

Dynamic prediction of traffic incident duration on urban expressways: a deep learning approach based on LSTM and MLP

Zhu¹,

et al. 2021

JICV

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“…Chung et al 6 developed the log-logistic accelerated failure time (AFT) metric model for temporal stability. Additional studies that utilized hazard-based models for predicting different duration stages include Hojati et al 7 and Li et al 8 .Moreover, Pang et al 9 improved the hazard-based duration modeling using a random parameters with heterogeneity in means and variances approach.…”

Section: Introductionmentioning

confidence: 99%

Traffic accident duration prediction using text mining and ensemble learning on expressways

Chen

Tao

2022

Sci Rep

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Predicting traffic accident duration is necessary for ensuring traffic safety. Several attempts have been made to achieve high prediction accuracy, but researchers have not considered traffic accident text data in much detail. The limited text data of the first report on an incident describes the characteristics of an accident that are initially available. This paper uses text data fusing and ensemble learning algorithms to build a model to predict an accident’s duration, and a preprocessing scheme of accident duration text data is established. Next, the random forest (RF) algorithm is applied to select feature variables of text data related to the traffic incident duration. Last, a text feature vector is introduced to models such as decision tree, k nearest neighbor, support vector regression, random forest, Gradient Boosting Decision Tree, and Xtreme Gradient Boosting. Our results show that the improved RF model has good prediction accuracy with RMSE, MAPE and R2. From this, the textual factors important to determining the duration of the accident are identified. Further, we investigated that the cumulative importance of 60% is sufficient for traffic accident prediction using text data. These results provide insights into minimizing traffic congestion related to accidents and contribute to the input optimization in text prediction.

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“…Regression methods were widely used for incident duration prediction in previous studies, such as linear regression [12,13]. To overcome the simple linear assumption between incident clearance time and explanatory variables, researchers proposed the hazard-based duration models (HBDM) to predict the incident duration precisely and explore the influence of significant factors on the incident duration, such as the Cox Proportional Hazards (PH) model and the Accelerated Failure Time (AFT) model [14]. Nam and Mannering [5] Lee and Fazio [15] used a proportional hazard-based Coxregression model to analyze the effect of explanatory variables on response time and clearance time, respectively.…”

Section: Introductionmentioning

confidence: 99%

Application of the Bayesian Model Averaging in Analyzing Freeway Traffic Incident Clearance Time for Emergency Management

Zou

Lin

Yang

et al. 2021

Journal of Advanced Transportation

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Identifying the influential factors in incident duration is important for traffic management agency to mitigate the impact of traffic incidents on freeway operation. Previous studies have proposed a variety of approaches to determine the significant factors for traffic incident clearance time. These methods commonly select a single “true” model among a majority of alternative models based on some model selection criteria. However, the conventional methods generally neglect the uncertainty related to the choice of models. This paper proposes a Bayesian Model Averaging (BMA) model to account for model uncertainty by averaging all plausible models using posterior probability as the weight. The BMA model is used to analyze the 2,584 freeway incident records obtained from I-5 corridor in Seattle, WA, USA. The results show that the BMA approach has the capability of interpreting the causal relationship between explanatory variables and clearance time. In addition, the BMA approach can provide better prediction performance than the Cox proportional hazards model and the accelerated failure time models. Overall, the findings in this study can be useful for traffic emergency management agency to apply an alternative methodology for predicting traffic incident clearance time when model uncertainty is considered.

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Analysis of the Different Duration Stages of Accidents with Hazard-Based Model

Cited by 8 publications

References 22 publications

Dynamic prediction of traffic incident duration on urban expressways: a deep learning approach based on LSTM and MLP

Dynamic prediction of traffic incident duration on urban expressways: a deep learning approach based on LSTM and MLP

Traffic accident duration prediction using text mining and ensemble learning on expressways

Application of the Bayesian Model Averaging in Analyzing Freeway Traffic Incident Clearance Time for Emergency Management

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