traffic incident
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2021 ◽  
Vol 39 (39) ◽  
pp. 84-95
Author(s):  
Grzegorz Diemientiew

The purpose of the article is to present various adverse events that may occur in connection with the loading, unloading and transport of dangerous substances. The focus was on finding the reasons for their formation and an analysis of the quantity and structure of the goods transported was carried out. It also describes the possibility of using the geographical information system, with particular regard to the risk situation during the transport of such substances.


Author(s):  
Kaiqun Fu ◽  
Taoran Ji ◽  
Nathan Self ◽  
Zhiqian Chen ◽  
Chang-Tien Lu

2021 ◽  
Author(s):  
Hongke Xu ◽  
Qilin Yue ◽  
Shan Lin ◽  
Chaozhi Zhao ◽  
Tao Wu

2021 ◽  
Vol 11 (4) ◽  
pp. 5909-5927
Author(s):  
Marina Leite De Barros Baltar ◽  
Victor Hugo Souza De Abreu ◽  
Andrea Souza Santos

Traffic incidents (such as broken-down vehicles, accidents, flat tires and other) constitute an important concern in the urban context, impacting the sustainable development. Thus, currently, the proposition of efficient traffic incident management systems has been encouraged to re-establish road safety and restore the network's traffic capacity. Thus, this paper aims to investigate the main impacts of traffic incidents and elaborate a logical structure of actions that should be employed to improve their management. The results show that many impacts can be identified in the three spheres of sustainable development and improvement actions must accelerate responses to emergencies, invest in Intelligent Transportation System (ITS), develop urban planning with a focus on more roads secure and enforce existing laws and regulations.


2021 ◽  
pp. 545-556
Author(s):  
Tahri Manal Salima ◽  
Fekhar Achwaq ◽  
Benahmed Khelifa ◽  
Bourouis Amina

Author(s):  
Sai Chand ◽  
Ernest Yee ◽  
Abdulmajeed Alsultan ◽  
Vinayak V. Dixit

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.


Author(s):  
Prashansa Agrawal ◽  
Antony Franklin ◽  
Digvijay Pawar ◽  
Srijith PK

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Weiwei Zhu ◽  
Jinglin Wu ◽  
Ting Fu ◽  
Junhua Wang ◽  
Jie Zhang ◽  
...  

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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