scholarly journals A Hybrid Method for Traffic Incident Duration Prediction Using BOA-Optimized Random Forest Combined with Neighborhood Components Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Qiang Shang ◽  
Derong Tan ◽  
Song Gao ◽  
Linlin Feng
Keyword(s):  
Random Forest ◽  
Confusion Matrix ◽  
Prediction Method ◽  
Bayesian Optimization ◽  
Incident Management ◽  
Bayesian Optimization Algorithm ◽  
Traffic Incident ◽  
Incident Duration ◽  
Components Analysis ◽  
Duration Prediction

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.

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

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.

Traffic Incident Duration Prediction Based on K-Nearest Neighbor

2012 ◽  
Vol 253-255 ◽  
pp. 1675-1681 ◽  
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.

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

2020 ◽  
Vol 2674 (1) ◽  
pp. 79-93 ◽  
Author(s):  
Xiaobing Li ◽  
Jun Liu ◽  
Asad Khattak ◽  
Shashi Nambisan
Keyword(s):  
Large Scale ◽  
Early Stage ◽  
Prediction Method ◽  
Finite Mixture Model ◽  
Finite Mixture ◽  
Survival Models ◽  
Incident Management ◽  
Traffic Incident ◽  
Sequential Prediction ◽  
Incident Duration

A quick and accurate traffic incident duration prediction could greatly facilitate traffic incident management. However, at the very early stage of an incident, limited information is available for prediction. Information gathering for large-scale traffic incidents is a chronological process when a multi-agency response is required. At the early stage, information such as incident start time and roadway and weather conditions may be available, but information about response agencies and incident management solutions (e.g., lane closures) remains unknown. The objective of this study is to develop a sequential prediction method to handle the chronological process of incident information gathering. The method is based upon parametric survival modeling, which is often utilized to predict incident duration. This study took advantage of a unique incident database and identified over 600 large-scale incidents in the East Tennessee area from 2015 to 2016. A five-stage prediction method is proposed according to the chronological process by which information becomes available during incident operations. Using the data, this study compared three survival models: frailty model, multilevel mixed-effects model, and finite mixture model. Generally, with more information becoming available for modeling from the first to the last stage, the models’ performance improved according to the root mean square error and mean absolute percent error. The finite mixture model outperforms the other two models and its mean absolute percentage error is between 10% and 15%. Incident-associated factors at each stage are discussed and implications based on the study outcomes are also covered in the paper.

Traffic Incident Duration Prediction using BERT Representation of Text

2021 ◽  
Author(s):  
Prashansa Agrawal ◽  
Antony Franklin ◽  
Digvijay Pawar ◽  
Srijith PK
Keyword(s):  
Traffic Incident ◽  
Incident Duration ◽  
Duration Prediction

Traffic Incident Duration Estimation Based on a Dual-Learning Bayesian Network Model

2018 ◽  
Vol 2672 (45) ◽  
pp. 196-209 ◽  
Author(s):  
Haozhe Cong ◽  
Cong Chen ◽  
Pei-Sung Lin ◽  
Guohui Zhang ◽  
John Milton ◽  
...  
Keyword(s):  
Bayesian Network ◽  
Expert Knowledge ◽  
Transportation Systems ◽  
Practical Significance ◽  
Incident Management ◽  
Highway Traffic ◽  
Traffic Incidents ◽  
Traffic Incident ◽  
Incident Duration ◽  
Traffic Incident Management

Highway traffic incidents induce a significant loss of life, economy, and productivity through injuries and fatalities, extended travel time and delay, and excessive energy consumption and air pollution. Traffic emergency management during incident conditions is the core element of active traffic management, and it is of practical significance to accurately understand the duration time distribution for typical traffic incident types and the factors that influence incident duration. This study proposes a dual-learning Bayesian network (BN) model to estimate traffic incident duration and to examine the influence of heterogeneous factors on the length of duration based on expert knowledge of traffic incident management and highway incident data collected in Zhejiang Province, China. Fifteen variables related to three aspects of traffic incidents, including incident information, incident consequences, and rescue resources, were included in the analysis. The trained BN model achieves favorable performance in several areas, including classification accuracy, the receiver operating characteristic (ROC) curve, and the area under curve (AUC) value. A classification matrix, and significant variables and their heterogeneous influences are identified accordingly. The research findings from this study provide beneficial reference to the understanding of decision-making in traffic incident response and process, active traffic incident management, and intelligent transportation systems.

A Study on Freeway Traffic Incident Duration and Improvement Countermeasures

2013 ◽  
Vol 411-414 ◽  
pp. 2752-2757
Author(s):  
Jing Ru Gao
Keyword(s):  
Incident Management ◽  
Key Factors ◽  
Traffic Incidents ◽  
Freeway Traffic ◽  
Emergency Rescue ◽  
Traffic Incident ◽  
Incident Duration ◽  
Clearing Time ◽  
Discovery Time ◽  
Traffic Incident Management

Reducing the duration of freeway traffic incident plays a significant role in improving the efficiency of freeway transportation. The duration of freeway traffic incidents is composed of four stages: the discovery time, response time, clearing time and the recovery time. Through analyzing the key factors influencing different duration stations, this article studies how factors including incident severity, aid resource allocation and emergency rescue preplan influence the duration, and pointedly propose countermeasures to reduce freeway traffic incidents duration. The conclusion of this article provides reference for improving the efficiency in freeway traffic incident management. Key words: traffic incident; duration; factor analysis; improve countermeasures

scholarly journals Incident Duration Modeling Using Flexible Parametric Hazard-Based Models

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Ruimin Li ◽  
Pan Shang
Keyword(s):  
Failure Time ◽  
Economic Losses ◽  
Incident Management ◽  
Accelerated Failure Time ◽  
Traffic Incidents ◽  
Traffic Incident ◽  
Duration Time ◽  
Incident Duration ◽  
Traffic Incident Management ◽  
Log Normal

Assessing and prioritizing the duration time and effects of traffic incidents on major roads present significant challenges for road network managers. This study examines the effect of numerous factors associated with various types of incidents on their duration and proposes an incident duration prediction model. Several parametric accelerated failure time hazard-based models were examined, including Weibull, log-logistic, log-normal, and generalized gamma, as well as all models with gamma heterogeneity and flexible parametric hazard-based models with freedom ranging from one to ten, by analyzing a traffic incident dataset obtained from the Incident Reporting and Dispatching System in Beijing in 2008. Results show that different factors significantly affect different incident time phases, whose best distributions were diverse. Given the best hazard-based models of each incident time phase, the prediction result can be reasonable for most incidents. The results of this study can aid traffic incident management agencies not only in implementing strategies that would reduce incident duration, and thus reduce congestion, secondary incidents, and the associated human and economic losses, but also in effectively predicting incident duration time.

Sign in / Sign up

Export Citation Format

Share Document