Combined PCA and NB to Predict Traffic Incident Duration

2017 ◽  
pp. 1-11
Author(s):  
Yechun Lao ◽  
Shuyan Chen ◽  
Ningning Song
Keyword(s):  
Traffic Incident ◽  
Incident Duration

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.

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.

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 A Descriptive Analysis on the Impact of COVID-19 Lockdowns on Road Traffic Incidents in Sydney, Australia

2021 ◽  
Vol 18 (21) ◽  
pp. 11701
Author(s):  
Sai Chand ◽  
Ernest Yee ◽  
Abdulmajeed Alsultan ◽  
Vinayak V. Dixit
Keyword(s):  
Large Scale ◽  
Road Traffic ◽  
Transportation Network ◽  
Sustainable Transportation ◽  
Traffic Incidents ◽  
Traffic Incident ◽  
Incident Duration ◽  
The Mean ◽  
The Impact ◽  
Sydney Australia

COVID-19 has had tremendous effects worldwide, resulting in large-scale death and upheaval. An abundance of studies have shown that traffic patterns have changed worldwide as working from home has become dominant, with many facilities, restaurants and retail services being closed due to the lockdown orders. With regards to road safety, there have been several studies on the reduction in fatalities and crash frequencies and increase in crash severity during the lockdown period. However, no scientific evidence has been reported on the impact of COVID-19 lockdowns on traffic incident duration, a key metric for crash management. It is also unclear from the existing literature whether the impacts on traffic incidents are consistent across multiple lockdowns. This paper analyses the impact of two different COVID-19 lockdowns in Sydney, Australia, on traffic incident duration and frequency. During the first (31 March–28 April 2020) and second (26 June–31 August 2021) lockdowns, the number of incidents fell by 50% and 60%, respectively, in comparison to the same periods in 2018 and 2019. The proportion of incidents involving towing increased significantly during both lockdowns. The mean duration of crashes increased by 16% during the first lockdown, but the change was less significant during the subsequent lockdown. Crashes involving diversions, emergency services and towing saw an increase in the mean duration by 67%, 16%, and 47%, respectively, during the first lockdown. However, this was not reflected in the 2021 data, with only major crashes seeing a significant increase, i.e., by 58%. There was also a noticeable shift in the location of incidents, with more incidents recorded in suburban areas, away from the central business area. Our findings suggest drastic changes in incident characteristics, and these changes should be considered by policymakers in promoting a safer and more sustainable transportation network in the future.

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