Sequential Prediction for Large-Scale Traffic Incident Duration: Application and Comparison of Survival Models

Li, Xiaobing; Li, Jun; Khattak, Asad J.; Nambisan, Shashi

doi:10.1177/0361198119899041

Cited by 20 publications

(6 citation statements)

References 20 publications

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“…Based on parametric survival modeling, a five-phase prediction method was proposed according to the time sequence of information available during emergency operation. The results showed that with the increase in information, the performance of the model will be improved according to the root mean square error and the average absolute percentage error [ 33 ]. In 2021, Zhu et al used the multi-layer perception (MLP) and LSTM model to integrate the relevant factors of traffic incident and real-time traffic flow parameters to predict the duration.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Duration of Traffic Incidents by Hybrid Deep Learning Based on Multi-Source Incomplete Data

Shang

Xie

2022

IJERPH

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Traffic accidents causing nonrecurrent congestion and road traffic injuries seriously affect public safety. It is helpful for traffic operation and management to predict the duration of traffic incidents. Most of the previous studies have been in a certain area with a single data source. This paper proposes a hybrid deep learning model based on multi-source incomplete data to predict the duration of countrywide traffic incidents in the U.S. The text data from the natural language description in the model were parsed by the latent Dirichlet allocation (LDA) topic model and input into the bidirectional long short-term memory (Bi-LSTM) and long short-term memory (LSTM) hybrid network together with sensor data for training. Compared with the four benchmark models and three state-of-the-art algorithms, the RMSE and MAE of the proposed method were the lowest. At the same time, the proposed model performed best for durations between 20 and 70 min. Finally, the data acquisition was defined as three phases, and a phased sequential prediction model was proposed under the condition of incomplete data. The results show that the model performance was better with the update of variables.

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Section: Introductionmentioning

confidence: 99%

Prediction of Duration of Traffic Incidents by Hybrid Deep Learning Based on Multi-Source Incomplete Data

Shang

Xie

2022

IJERPH

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“…H. Cong, et al [7], J. Tang, et al [8], and X. Li, et al [9] developed models to predict the duration of the incident. A. Khattak, et al, [10]…”

Section: Related Workmentioning

confidence: 99%

Predicting Incident Duration Based on Machine Learning Methods

2021

IJCCCE

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Traffic incidents dont only cause various levels of traffic congestion but often contribute to traffic accidents and secondary accidents, resulting in substantial loss of life, economy, and productivity loss in terms of injuries and deaths, increased travel times and delays, and excessive consumption of energy and air pollution. Therefore, it is essential to accurately estimate the duration of the incident to mitigate these effects. Traffic management center incident logs and traffic sensors data from Eastbound Interstate 70 (I-70) in Missouri, United States collected during the period from January 2015 to January 2017, with a total of 352 incident records were used to develop incident duration estimation models. This paper investigated different machine learning (ML) methods for traffic incidents duration prediction. The attempted ML techniques include Support Vector Machine (SVM), Random Forest (RF), and Neural Network Multi-Layer Perceptron (MLP). Root mean squared error (RMSE) and Mean absolute error (MAE) were used to evaluate the performance of these models. The results showed that the performance of the models was comparable with SVM models slightly outperforms the RF, and MLP models in terms of MAE index, where MAE was 14.23 min for the best-performing SVM models. Whereas, in terms of the RMSE index, RF models slightly outperformed the other two models given RMSE of 18.91 min for the best-performing RF model. Index Terms— Incident Duration, Neural Network Multi-Layer Perceptron, Random Forest, Support Vector Machine.

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“…For instance, Restricted Boltzmann Machines 10 , Bayesian Support Vector Regression (BSVR) 11 ,and the Extreme Gradient Boosting Machine Algorithm (XGBoost) 12 are evaluated on the prediction of traffic accident duration. Li et al 13 established a survival model to deal with the early stage's lack of relevant information about event disposal. Kuang et al 14 proposed a two-step model consisting of a Bayesian network and k-nearest neighbor and predicted the duration of accidents using the vehicle sensor data of Xiamen City in 2015.…”

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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Sequential Prediction for Large-Scale Traffic Incident Duration: Application and Comparison of Survival Models

Cited by 20 publications

References 20 publications

Prediction of Duration of Traffic Incidents by Hybrid Deep Learning Based on Multi-Source Incomplete Data

Prediction of Duration of Traffic Incidents by Hybrid Deep Learning Based on Multi-Source Incomplete Data

Predicting Incident Duration Based on Machine Learning Methods

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

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