Traffic Incident Duration Prediction using BERT Representation of Text

Predicting traffic incident duration is important for effective and real-time traffic incident management (TIM), which helps to minimize traffic congestion, environmental pollution, and secondary incident related to this incident. Traffic incident duration prediction methods often use more input variables to obtain better prediction results. However, the problems that available variables are limited at the beginning of an incident and how to select significant variables are ignored to some extent. In this paper, a novel prediction method named NCA-BOA-RF is proposed using the Neighborhood Components Analysis (NCA) and the Bayesian Optimization Algorithm (BOA)-optimized Random Forest (RF) model. Firstly, the NCA is applied to select feature variables for traffic incident duration. Then, RF model is trained based on the training set constructed using feature variables, and the BOA is employed to optimize the RF parameters. Finally, confusion matrix is introduced to measure the optimized RF model performance and compare with other methods. In addition, the performance is also tested in the absence of some feature variables. The results demonstrate that the proposed method not only has high accuracy, but also exhibits excellent reliability and robustness.

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Traffic Incident Duration Prediction Based on Support Vector Regression

ICCTP 2011 ◽

10.1061/41186(421)241 ◽

2011 ◽

Cited By ~ 12

Author(s):

Wei-wei Wu ◽

Shu-yan Chen ◽

Chang-jiang Zheng

Keyword(s):

Support Vector Regression ◽

Support Vector ◽

Traffic Incident ◽

Incident Duration ◽

Duration Prediction

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

Journal of Intelligent and Connected Vehicles ◽

10.1108/jicv-03-2021-0004 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Weiwei Zhu ◽

Jinglin Wu ◽

Ting Fu ◽

Junhua Wang ◽

Jie Zhang ◽

...

Keyword(s):

Real Time ◽

Traffic Flow ◽

Incident Management ◽

Intelligent Transport System ◽

Content Type ◽

Traffic Incident ◽

Flow Parameters ◽

Incident Duration ◽

Traffic Incident Management ◽

Duration Prediction

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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Traffic Incident Duration Prediction Based on K-Nearest Neighbor

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.253-255.1675 ◽

2012 ◽

Vol 253-255 ◽

pp. 1675-1681 ◽

Cited By ~ 3

Author(s):

Yuan Wen ◽

Shu Yan Chen ◽

Qin Yuan Xiong ◽

Ru Bi Han ◽

Shi Yu Chen

Keyword(s):

Nearest Neighbor ◽

Public Works ◽

Incident Management ◽

Bayesian Decision ◽

K Nearest Neighbor ◽

Data Set ◽

K Value ◽

Traffic Incident ◽

Incident Duration ◽

Duration Prediction

Prediction of incident duration is very important in Advanced Intelligent Traffic Incident Management and the accuracy of prediction can provide exact information for travellers. It is widely used in the area of ITS. In this paper, K-Nearest neighbor (KNN) is employed to predict the incident duration, which puts forward a new distance metric and weight determination method. This KNN model is created based on the incident data set collected by DVS-Center for Transport and Navigation, Ministry of Transport, Public Works and Management, the Netherlands. Moreover, a simulation based on Matlab is used for incident duration prediction and optimizing the best k value. Finally, an error analysis is made based on this simulation. As a result, this method (KNN) obtains high accuracy and has a better effect than Bayesian Decision Method-Based Tree Algorithm. So it can be effectively applied to intelligent traffic incident detection and clearance systems.

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