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.

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 Personal Exposure to BC, PM and NO2 in the Paris Region Measured by Portable Sensors Worn by Volunteers

Toxics ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 33
Author(s):  
Baptiste Languille ◽  
Valérie Gros ◽  
Bonnaire Nicolas ◽  
Cécile Honoré ◽  
Anne Kaufmann ◽  
...  
Keyword(s):  
Large Scale ◽  
Road Traffic ◽  
Personal Exposure ◽  
Portable Sensors ◽  
Promising Solution ◽  
Field Campaigns ◽  
Paris Region ◽  
The Impact ◽  
First Time ◽  
Indoor And Outdoor

Portable sensors have emerged as a promising solution for personal exposure (PE) measurement. For the first time in Île-de-France, PE to black carbon (BC), particulate matter (PM), and nitrogen dioxide (NO2) was quantified based on three field campaigns involving 37 volunteers from the general public wearing the sensors all day long for a week. This successful deployment demonstrated its ability to quantify PE on a large scale, in various environments (from dense urban to suburban, indoor and outdoor) and in all seasons. The impact of the visited environments was investigated. The proximity to road traffic (for BC and NO2), as well as cooking activities and tobacco smoke (for PM), made significant contributions to total exposure (up to 34%, 26%, and 44%, respectively), even though the time spent in these environments was short. Finally, even if ambient outdoor levels played a role in PE, the prominent impact of the different environments suggests that traditional ambient monitoring stations is not a proper surrogate for PE quantification.

scholarly journals What Is the Impact of Additional Tropical Observations on a Modern Data Assimilation System?

2019 ◽  
Vol 147 (7) ◽  
pp. 2433-2449
Author(s):  
Laura C. Slivinski ◽  
Gilbert P. Compo ◽  
Jeffrey S. Whitaker ◽  
Prashant D. Sardeshmukh ◽  
Jih-Wang A. Wang ◽  
...  
Keyword(s):  
El Niño ◽  
Large Scale ◽  
El Nino ◽  
Mean Square ◽  
Observational Network ◽  
In Situ Observations ◽  
The Mean ◽  
The Impact ◽  
Assimilation System

Abstract Given the network of satellite and aircraft observations around the globe, do additional in situ observations impact analyses within a global forecast system? Despite the dense observational network at many levels in the tropical troposphere, assimilating additional sounding observations taken in the eastern tropical Pacific Ocean during the 2016 El Niño Rapid Response (ENRR) locally improves wind, temperature, and humidity 6-h forecasts using a modern assimilation system. Fields from a 50-km reanalysis that assimilates all available observations, including those taken during the ENRR, are compared with those from an otherwise-identical reanalysis that denies all ENRR observations. These observations reveal a bias in the 200-hPa divergence of the assimilating model during a strong El Niño. While the existing observational network partially corrects this bias, the ENRR observations provide a stronger mean correction in the analysis. Significant improvements in the mean-square fit of the first-guess fields to the assimilated ENRR observations demonstrate that they are valuable within the existing network. The effects of the ENRR observations are pronounced in levels of the troposphere that are sparsely observed, particularly 500–800 hPa. Assimilating ENRR observations has mixed effects on the mean-square difference with nearby non-ENRR observations. Using a similar system but with a higher-resolution forecast model yields comparable results to the lower-resolution system. These findings imply a limited improvement in large-scale forecast variability from additional in situ observations, but significant improvements in local 6-h forecasts.

Large-scale fuel deposition patterns on northern Spanish shores following the ‘Prestige’ oil spill

2008 ◽  
Vol 88 (3) ◽  
pp. 463-468 ◽  
Author(s):  
José Luis Acuña ◽  
Araceli Puente ◽  
Ricardo Anadón ◽  
Consolación Fernández ◽  
María Luisa Vera ◽  
...  
Keyword(s):  
Large Scale ◽  
Northern Spain ◽  
The West ◽  
Deposition Patterns ◽  
Prestige Oil Spill ◽  
Cantabrian Coast ◽  
The Mean ◽  
The Impact ◽  
Oil Tanker ◽  
Fuel Deposition

Following the accident of the oil tanker ‘Prestige’, we surveyed the large scale fuel deposition patterns on the Cantabrian shore (northern Spain) covering three regions (from west to east): (i) Asturias, west of Cape Peñas (24 segments surveyed); (ii) Asturias, east of Cape Peñas (33 segments surveyed); and (iii) Cantabria (also east of Cape Peñas, 256 segments surveyed). Fuel arrived to the Cantabrian Coast as a single oil wave which was more intense to the east than to the west of Cape Peñas. The mean percentage of coast length affected was 25, 41 and 15% in western Asturias, eastern Asturias and Cantabria, respectively. However, less than 10% of the substrate was covered by fuel in oiled patches, thus the impact was moderate. We conclude that these patterns are consistent with fuel transport by the Iberian Poleward Current, a hydrographic feature typical of this region during winter.

Drivers of biases in the extratropical storm tracks in CMIP6

2020 ◽  
Author(s):  
Matthew Priestley ◽  
Duncan Ackerley ◽  
Jennifer Catto ◽  
Kevin Hodges ◽  
Ruth McDonald ◽  
...  
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Storm Track ◽  
Storm Tracks ◽  
Extratropical Cyclones ◽  
Ocean Coupling ◽  
Coupled Climate Models ◽  
The Mean ◽  
The Impact

<p>Extratropical cyclones are the leading driver of the day-to-day weather variability and wintertime losses for Europe. In the latest generation of coupled climate models, CMIP6, it is hoped that with improved modelling capabilities come improvements in the structure of the storm track and the associated cyclones. Using an objective cyclone identification and tracking algorithm the mean state of the storm tracks in the CMIP6 models is assessed as well as the representation of explosively deepening cyclones. Any developments and improvements since the previous generation of models in CMIP5 are discussed, with focus on the impact of model resolution on storm track representation. Furthermore, large-scale drivers of any biases are investigated, with particular focus on the role of atmosphere-ocean coupling via associated AMIP simulations and also the influence of large-scale dynamical and thermodynamical features.</p>

Reconstruction of GRACE Total Water Storage Through Automated Machine Learning

2020 ◽  
Author(s):  
Alex Sun ◽  
Bridget Scanlon ◽  
Himanshu Save ◽  
Ashraf Rateb
Keyword(s):  
Machine Learning ◽  
Spatial Variability ◽  
Large Scale ◽  
Water Storage ◽  
Total Water ◽  
New Paradigm ◽  
Satellite Mission ◽  
Automated Machine Learning ◽  
The Mean ◽  
The Impact

<p>The GRACE satellite mission and its follow-on, GRACE-FO, have provided unprecedented opportunities to quantify the impact of climate extremes and human activities on total water storage at large scales. The approximately one-year data gap between the two GRACE missions needs to be filled to maintain data continuity and maximize mission benefits. There is strong interest in using machine learning (ML) algorithms to reconstruct GRACE-like data to fill this gap. So far, most studies attempted to train and select a single ML algorithm to work for global basins. However, hydrometeorological predictors may exhibit strong spatial variability which, in turn, may affect the performance of ML models. Existing studies have already shown that no single algorithm consistently outperformed others over all global basins. In this study, we applied an automated machine learning (AutoML) workflow to perform GRACE data reconstruction. AutoML represents a new paradigm for optimal model structure selection, hyperparameter tuning, and model ensemble stacking, addressing some of the most challenging issues related to ML applications. We demonstrated the AutoML workflow over the conterminous U.S. (CONUS) using six types of ML algorithms and multiple groups of meteorological and climatic variables as predictors. Results indicate that the AutoML-assisted gap filling achieved satisfactory performance over the CONUS. For the testing period (2014/06–2017/06), the mean gridwise Nash-Sutcliffe efficiency is around 0.85, the mean correlation coefficient is around 0.95, and the mean normalized root-mean square error is about 0.09. Trained models maintain good performance when extrapolating to the mission gap and to GRACE-FO periods (after 2017/06). Results further suggest that no single algorithm provides the best predictive performance over the entire CONUS, stressing the importance of using an end-to-end workflow to train, optimize, and combine multiple machine learning models to deliver robust performance, especially when building large-scale hydrological prediction systems and when predictor importance exhibits strong spatial variability.</p>

On the Climate Impact of Surface Roughness Anomalies

2008 ◽  
Vol 65 (7) ◽  
pp. 2215-2234 ◽  
Author(s):  
Daniel B. Kirk-Davidoff ◽  
David W. Keith
Keyword(s):  
Surface Roughness ◽  
Large Scale ◽  
Climate Impact ◽  
Water Model ◽  
Dynamic Mechanisms ◽  
Community Atmosphere Model ◽  
Horizontal Length ◽  
Rossby Wave Response ◽  
The Mean ◽  
The Impact

Abstract Large-scale deployment of wind power may alter climate through alteration of surface roughness. Previous research using GCMs has shown large-scale impacts of surface roughness perturbations but failed to elucidate the dynamic mechanisms that drove the observed responses in surface temperature. Using the NCAR Community Atmosphere Model in both its standard and aquaplanet forms, the authors have explored the impact of isolated surface roughness anomalies on the model climate. A consistent Rossby wave response in the mean winds to roughness anomalies across a range of model implementations is found. This response generates appreciable wind, temperature, and cloudiness anomalies. The interrelationship of these responses is discussed, and it is shown that the magnitude of the responses scales with the horizontal length scale of the roughened region, as well as with the magnitude of the roughness anomaly. These results are further elucidated through comparison with results of a series of shallow-water model experiments.

Highway Traffic Incident Detection and Optimal Sensor Placement Layout

2013 ◽  
Vol 650 ◽  
pp. 460-464 ◽  
Author(s):  
Lan Bai ◽  
Qi Sheng Wu ◽  
Mei Yang ◽  
Lan Xin Wei ◽  
Bo Li ◽  
...  
Keyword(s):  
Road Traffic ◽  
Detection Algorithm ◽  
Simulation Software ◽  
Incident Management ◽  
Incident Detection ◽  
Highway Traffic ◽  
Traffic Incidents ◽  
Traffic Incident ◽  
Before And After ◽  
Traffic Incident Management

Traffic incident detection is critical to the core of the traffic incident management process. In order to study the highway traffic incident detection algorithm and the layout spacing of the fixed detector, under the assumptions of the linear traffic flow, to detect traffic incidents as the goal, using TransModeler traffic simulation software to simulate the highway traffic conditions from Xian to Hanzhong, getting the changes in the macroscopic traffic parameters before and after the traffic incident, and analysis of the data, finally puts forward the optimal layout of spacing of basic road traffic incident detection.

Modeling the impact of traffic incidents during hurricane evacuations using a large scale microsimulation

2018 ◽  
Vol 31 ◽  
pp. 1159-1165 ◽  
Author(s):  
R. Michael Robinson ◽  
Andrew J. Collins ◽  
Craig A. Jordan ◽  
Peter Foytik ◽  
Asad J. Khattak
Keyword(s):  
Large Scale ◽  
Traffic Incidents ◽  
The Impact

Effect of Lean method implementation in the histopathology section of an anatomical pathology laboratory

2007 ◽  
Vol 61 (11) ◽  
pp. 1193-1199 ◽  
Author(s):  
S S Raab ◽  
D M Grzybicki ◽  
J L Condel ◽  
W R Stewart ◽  
B D Turcsanyi ◽  
...  
Keyword(s):  
Quality Improvement ◽  
Large Scale ◽  
Turnaround Time ◽  
Full Time ◽  
Pathology Laboratory ◽  
Quality Improvement Process ◽  
The Mean ◽  
Work Units ◽  
The Impact ◽  
Productivity Ratio

Background:In the USA, the lack of processes standardisation in histopathology laboratories leads to less than optimal quality, errors, inefficiency and increased costs. The effectiveness of large-scale quality improvement initiatives has been evaluated rarely.Aim:To measure the effect of implementation of a Lean quality improvement process on the efficiency and quality of a histopathology laboratory section.Methods:A non-concurrent interventional cohort study from 1 January 2003 to 31 December 2006 was performed, and the Lean process was implemented on 1 January 2004. Also compared was the productivity of the Lean histopathology section to a sister histopathology section that did not implement Lean processes. Pre- and post-Lean specimen turnaround time and productivity ratios (work units/full time equivalents) were measured. For 200 Lean interventions, a 5-part Likert scale was used to assess the impact on error, success and complexity.Results:In the Lean laboratory, the mean monthly productivity ratio increased from 3439 to 4074 work units/full time equivalents (p<0.001) as the mean daily histopathology section specimen turnaround time decreased from 9.7 to 9.0 h (p = 0.01). The Lean histopathology section had a higher productivity ratio compared with a sister histopathology section (1598 work units/full time equivalents, p<0.001) that did not implement Lean processes. The mean impact, success and complexity of interventions were 2.4, 2.7 and 2.5, respectively. The mean number of specific error causes affected by individual interventions was 2.6.Conclusion:It is concluded that Lean process implementation improved efficiency and quality in the histopathology section.

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