Traffic Restrictions Research Articles

Method for prediction of ship traffic behaviour and encounter frequency

AbstractThe design of new rules on seaways, such as traffic restrictions, requires determining the degree of improvement in marine traffic safety beforehand by considering the occurrence of new hazardous factors. This study proposes a method to predict the future traffic behaviour and ship encounter frequency (EF) with the introduction of a new traffic rule. First, a sensitivity analysis is conducted to identify the factors affecting the EF. A method of predicting future traffic behaviour and EF is presented based on the analysis of changes in the traffic flow in an area with a temporal restriction. Results show that the method appropriately predicts the location and degree of the occurrence of encounters in the sea area. The proposed method contributes to the discussion of future traffic safety, when sailing in a specific area is restricted by new regulations, installations of new offshore wind farms and fishing reefs.

Assessing the effects of non-local traffic restriction policy on urban air quality

Traffic restriction policy (TRP) is common practice to alleviate urban congestion and air pollution. However, effects of TRP for non-local vehicles and elevated expressways on local and regional air quality are understudied. Using air pollutant data collected by ground-based national and roadside environmental monitoring stations and field measurements, comparative research design and regression discontinuity analysis were performed to investigate the local and regional variations in air pollutant concentrations with and without the TRP. Results indicate that traffic restriction significantly decreased the local and regional concentration levels of primary gaseous pollutants (e.g., NOX and CO), particularly under more stringent TRP during China International Import Expo, which also brought an ozone increase due to the reduced effect of NOX titration. However, the TRP only exhibited limited impacts on mitigating regional particulate pollution. These findings suggest that the air quality improvements due to the non-local TRP highly depend on the types of pollutants and the spatial locations.

Position Paper: Overview of Workplace Bullying in Higher Educational Organizations

Coronavirus disease (COVID-19) outbreak has severely affected the whole world. It has overshadowed all the developmental activities across different countries. The COVID-19 pandemic has negatively affected global economy and has threatened health security of people worldwide. This pandemic have affected environment dynamically. Decrease in economic activities, travelling, transport and traffic restrictions all over the world have led to decreased emissions of greenhouse gases and drop in air and water pollution observed universally. Also more than 40 % of downfall is observed in NO2 concentration in Asia and Europe as compared to 2019. Reduced pollution had a good effect on freshwater aquatic life. The reduction in worldwide aircraft flight directly impact on forecast and decreases 65% of weather prediction. Snow fall was observed even in a summer in India. This pandemic has had a long-term impact, affecting all aspects of human life and halting all developmental processes. Combating the COVID-19 pandemic is currently at the top of the global agenda. Quarantine measures opted for safety of the public have positively affected the environment. Although it has downturned the economic growth drastically, it has also contributed in lowering the pollution. If there is a silver lining to this horrible situation, it may be that it has given us a whiff of the air we will breathe in a low-carbon future.

To what extent the traffic restriction policies applied in Barcelona city can improve its air quality?

Barcelona city (Spain) is applying a series of traffic restriction measures that aim at renewing and reducing the amount of circulating vehicles to improve air quality. The measures include changes in the built environment to reduce private vehicle space in specific areas through the so-called “superblocks” and tactical urban planning actions, along with the implementation of a city-wide Low Emission Zone (LEZ) that restricts the entry of the most polluting vehicles to the city. Our study quantifies the impact of these measures in the greater area of Barcelona combining a coupled macroscopic traffic and pollutant emission model with a multi-scale air quality model. Our modelling system allows estimating the effect of different traffic restrictions upon traffic and the associated emissions and air quality levels at a very high resolution (20 m). The measures were evaluated both individually and collectively to assess both their relative and overall impact upon emissions and air quality. We show that in the absence of traffic demand reductions, the application of isolated measures that reduce private vehicle space, either through superblocks or tactical urban planning, have no overall emission impacts; only localized street-level NOx positive and negative changes (±17%) are found due to traffic re-routing and the generation of new bottlenecks. It is only when these measures are combined with optimistic fleet renewal as a result of the LEZ implementation and demand reductions, that relevant global emission reductions in NOx are obtained (−13% and −30%, respectively) with estimated NO2 reductions of −36% and −23% at the two traffic air quality monitoring stations. Despite the potential improvements, our simulations suggest that current measures are insufficient to comply with EU air quality standards and that further traffic restriction policies to reduce traffic demand are needed.

The Impact of Inter-City Traffic Restriction on COVID-19 Transmission from Spatial Econometric Perspective

The aim of this paper is to conduct a spatial correlation study of virus transmission in the Hubei province, China. The number of confirmed COVID-19 cases released by the National Health and Construction Commission, the traffic flow data provided by Baidu migration, and the current situation of Wuhan intercity traffic were collected. The Moran’s I test shows that there is a positive spatial correlation between the 17 cities in the Hubei province. The result of Moran’s I test also shows that four different policies to restrict inter-city traffic can be issued for the four types of cities. The ordinary least squares regression, spatial lag model, spatial error model, and spatial lag error model were built. Based on the analysis of the spatial lag error model, whose goodness of fit is the highest among the four models, it can be concluded that the speed of COVID-19 spread within a certain region is not only related to the current infection itself but also associated with the scale of the infection in the surrounding area. Thus, the spill-over effect of the COVID-19 is also presented. This paper bridges inter-city traffic and spatial economics, provides a theoretical contribution, and verifies the necessity of a lockdown from an empirical point of view.

MANAJEMEN TRAFIK MENGGUNAKAN HTB UNTUK MENINGKATKAN KUALITAS LAYANAN IP NETWORK

ABSTRACTIn IP-based networks with many users, a bandwidth usage regulation mechanism is needed to ensure service quality. Mikrotik RouterOS has a feature to run QoS known as queue. RouterOS uses the Hierarchichal Token Bucket (HTB) method as the main queue method. The results of the tests carried out show that HTB is able to provide traffic limitation facilities for each classification and guarantee or ensure the amount of bandwidth is less or equal to the predetermined amount. If there is unused bandwidth it can be used by a lower classification (borrowing mechanism).

A Two-Stage SEM–Artificial Neural Network Analysis of Mobile Commerce and Its Drivers

The COVID-19 health and economic crisis has affected all areas of social life globally, including the economy. The world economy has declined due to purchasing power for individuals who have been forced to stay at home and cannot perform work. These restrictions to prevent the spread of SARS-Cov-2 have led to an increase in electronic commerce and mobile commerce as tools for procuring goods and services. In this paper, we conducted a longitudinal analysis of mobile commerce as an essential electronic commerce component, establishing the main drivers of mobile commerce and the intensity of their influences. The research focuses on mobile commerce in the United States (U.S.). It covers the period 2010–2020, the last year of this period capturing the context of the COVID-19 pandemic and its impact on electronic commerce (e-commerce) and mobile commerce (m-commerce). In the macroeconomic analysis of competitiveness, we established the main drivers of m-commerce, using artificial neural networks and the mediation effects found between the variables that describe m-commerce, e-commerce, and total sales, using structural equation modeling. The research results indicate an increase in the share of e-commerce in total sales and a predominance of the m-commerce share in e-commerce on the background of traffic restrictions and social distance rules imposed due to the COVID-19 pandemic. Stakeholders in the m-commerce area should consider the following enhancing drivers: increasing internet speed, expanding 5G and Wi-Fi networks, and increasing accessibility and trust in mobile devices and applications.

The impacts of COVID-19 on older adults’ active transportation mode usage in Isfahan, Iran

AimsSince active mobility is essential for older adults’ health and well-being, especially in crises, this study aims to investigate and compare factors affecting active mode choice of older adults (AMCOA) in the pre- and post-COVID-19 outbreak and evaluate changes in their active mobility behavior and no research has been conducted this subject. MethodologyThe sample study of this cross-sectional study includes individuals over 60 years old residing in Isfahan, Iran. For interviews based on a structured questionnaire, which was drafted based on reviewing the literature, 453 participants were randomly selected in 15 municipality districts of Isfahan. A binary logistic regression model was used to analyze the data. Result and DiscussionThe results indicate that in the post-outbreak the average walking duration per week decreased from 59 to 29 min; while, the share of this mode has increased from 40% to 65%. Also, the share of bicycles and the average cycling duration per week increased from 9% to 18% and from 9 to 15 min, respectively. Moreover, trip frequency, bicycle ownership, quality of walking and cycling routes, intersections safety, neighborhood security and greenery, traffic calming, CBD accommodation, and public transportation accessibility have positive effects on AMCOA; while, trip distance and vehicle ownership affect negatively. The results reveal that older adults have turned to the bicycle for most of their long trips during the pandemic because it is not subjected to traffic restrictions. Besides, the findings show that increasing bicycle ownership and improving bike-sharing infrastructure make the bicycle a resilient alternative when public transportation and private vehicles are not efficient. ConclusionPolicymakers and urban planners should consider that an elderly-friendly neighborhood with mixed, dense, and accessible land uses and services, as well as safe and secure routes can increase older adults’ active mobility in the crises. AimsSince active mobility is essential for older adults’ health and well-being, especially in crises, this study aims to investigate and compare factors affecting active mode choice of older adults (AMCOA) before and during the first wave of COVID-19 outbreak and evaluate changes in their active mobility behavior and no research has been conducted this subject.

Developing a Traffic Model to Estimate Vehicle Emissions: An Application in Seoul, Korea

In this study, a traffic demand model was created based on a simulation network, and another model was built to calculate exhaust-gas emissions generated by vehicles based on the emission function. Subsequently, emissions for three scenarios were analyzed based on the traffic restriction policy according to the vehicle grading system implemented in Seoul. According to the results of the analysis, emission reduction under the vehicle restriction policy was the highest among passenger cars in the low-speed range, while the emissions of cargo trucks in the high-speed range were found to be high. The emissions showed a high ratio of carbon monoxide and nitrogen oxides, and high emissions were generated from liquefied petroleum gas and diesel vehicles. Furthermore, the effects of vehicle restriction policy were confirmed to reduce emissions from diesel and other vehicle types. Using the established model, we were able to confirm that the vehicle restriction policy contributed to the improvement of air quality. Furthermore, the diesel vehicle restriction policy also had an impact on reducing the emissions of vehicle types other than those using diesel.

Evaluating the effects of the COVID-19 outbreak on the older adults’ travel mode choices

Background and aimsTo increase the older adults' mobility during the COVID-19 outbreak, providing appropriate conditions for using different transportation modes and organizing transportation facilities for the older adults are essential. Hence, this study aims to evaluate and compare factors affecting the older adults’ travel mode choice to investigate transportation policies for increasing their independent mobility in the post-outbreak that has not been addressed in previous research. MethodologyThe population of this cross-sectional study consisted of Isfahan citizens aged 60 years and over. For interviews based on a structured questionnaire, 453 participants were randomly selected in 15 municipality districts of Isfahan. Multinomial logistic models were used to analyze the data. FindingsThe results show that despite the decrease in the average frequency of travels per week, the increase in the share of walking and cycling modes, making shopping and recreational travels on foot, and cycling can indicate the resilience of walking and cycling in critical situations. The results also reveal that active modes have effective roles in the older adults’ mobility in the post-outbreak because they are not subject to traffic restrictions like private vehicles and social constraints and crowd avoidance like public transportation. Discussion and conclusionPolicies such as the proper location of facilities, increasing density and mixing of land uses, landscaping, traffic reduction, increasing ownership of bicycle and tricycle (to eliminate the problems of falling in getting on and off the bicycles), driving training courses for the safe driving, and intersections safety improvement are essential to maintain the older adults' mobility after the outbreak. With more capacity and low occupancy, the public transportation system, high-quality accessibility, and safe routes will also attract the older adults’ travels in the crisis.

Changes in the concentration and composition of urban aerosols during the COVID-19 lockdown

This work investigates the impact of COVID-19 restrictive measures on the mass concentrations of PM1 and PM10, and their chemical components (water-soluble ions, organic and elemental carbon, and major and trace metals) at an urban site in the western Mediterranean. The evolution of gaseous pollutants (NOx, O3 and some volatile organic compounds) was also analyzed. The concentrations measured during the lockdown in 2020 were compared to those obtained during the same period over the preceding five years. The average decrease in the levels of NOx and traffic-related volatile organic compounds was higher than 50 %, while O3 concentrations did not exhibit significant variations during the study period. Our results show that temporal variations in PM1 and PM10 concentrations were strongly affected by the frequency of Saharan dust events. When these episodes were excluded from the analysis period, a 35 % decrease in PM1 and PM10 levels was observed. Traffic restrictions during the lockdown led to important reductions in the concentrations of elemental carbon and metals derived from road dust (e.g. Ca and Fe) and break wear (e.g. Cu). Regarding secondary inorganic aerosols, nitrate showed the largest reductions as a consequence of the drop in local emissions of NOx.

Internet of vehicles and autonomous systems with AI for medical things.

The current world faces a considerable traffic rate on roads due to the increase in various types of vehicles. It caused emergency vehicles to delay and increasing the patients' health risk factor. Internet of vehicle-based artificial neural network (IoV-ANN) has been proposed for effective health autonomous system in medical things. The proposed IoV-ANN provides a secure network to monitor and track the vehicle's motion using the global positioning system. It consists of an autonomous system which is enabled with an artificial neural network (ANN). ANN model has three layers. First layers collect the data using IoV sensors. Second or hidden layers process the sensor data, predict the road's traffic condition and reroute the emergency vehicle to an exact route. IoV-ANN helps to reduce road congestion in this article to enhance the timely functioning of an emergency vehicle. ANN categorizes the congestion networks of traffic. Traffic restrictions such as changing the queue gap in the road signals and the alternative roads are altered automatically due to congestion. It allows the government to develop ideas for alternate routes to enhance traffic control. The output layer gives commands to the driver autonomously. The simulation analysis of the proposed method proved that the system could work independently. The IoV-ANN achieves the highest performance rate of (97.89%), with a reduced error rate (9.12%) traffic congestion rate (10.31%), travel period (32 s), vehicle detection rate (93.61%), classification accuracy (95.02%), MAPE (8.4%), throughput rate (93.50%) when compared to other popular methods.

Risk assessment of paternal depression in relation to partner delivery during COVID-19 pandemic in Wuhan, China

BackgroundThe COVID-19 pandemic and relevant prevention and control measures may affect the mental health and induce depressive symptoms in fathers with concurrent partner delivery exposure. This study aims to investigate the prevalence of depression in fathers with simultaneous exposure to COVID-19 pandemic and the effects of family functions on paternal perinatal depression (PPD) risk.MethodsA cross-sectional study was conducted among the perinatal fathers recruited in a large tertiary hospital in Wuhan across the whole pandemic period from 31 December 2019 to 11 April 2020. Edinburgh Postnatal Depression Scale (EPDS) and APGAR family function scale were used to evaluate PPD and family function, respectively. Chi-square test and multivariable-adjusted logistic regression model were applied for data analysis.ResultsAmong the 1187 participants, the prevalence of PPD was 13.82% throughout the COVID-19 pandemic. Compared with that in the time period before the announcement of human-to-human transmission on 19 January 2020, the depression risk was significantly lower during the traffic restriction (OR = 0.54, 95% CI: 0.34, 0.86) and public transportation reopening periods (OR = 0.29, 95% CI: 0.14, 0.59). Poor/fair family functions was associated with elevated depression risk (OR = 2.93, 95% CI: 1.90, 4.52). Individuals reporting a low family income and smoking had high depression risks.ConclusionsA declined risk of PPD was observed over the traffic restriction period of the COVID-19 pandemic. An improved family function may help alleviate the risk of PPD during the pandemic. Health authorities are recommended to formulate targeted prevention and control strategies to handle PPD.

Health and related economic benefits associated with reduction in air pollution during COVID-19 outbreak in 367 cities in China

Due to the COVID-19 outbreak, the Chinese government implemented nationwide traffic restrictions and self-quarantine measures from January 23 to April 8 (in Wuhan), 2020. We estimated how these measures impacted ambient air pollution and the subsequent consequences on health and the health-related economy in 367 Chinese cities. A random forests modeling was used to predict the business-as-usual air pollution concentrations in 2020, after adjusting for the impact of long-term trend and weather conditions. We calculated changes in mortality attributable to reductions in air pollution in early 2020 and health-related economic benefits based on the value of statistical life (VSL). Compared with the business-as-usual scenario, we estimated 1239 (95% CI: 844–1578) PM2.5-related deaths were avoided, as were 2777 (95% CI: 1565–3995) PM10-related deaths, 1587 (95% CI: 98–3104) CO-related deaths, 4711 (95% CI: 3649–5781) NO2-related deaths, 215 (95% CI: 116–314) O3-related deaths, and 1088 (95% CI: 774–1421) SO2-related deaths. Based on the reduction in deaths, economic benefits for in PM2.5, PM10, CO, NO2, O3, and SO2 were 1.22, 2.60, 1.36, 4.05, 0.20, and 0.95 billion USD, respectively. Our findings demonstrate the substantial benefits in human health and health-related costs due to improved urban air quality during the COVID lockdown period in China in early 2020.

The effects of home confinement on pediatric fractures during the COVID 19 outbreak

ObjectiveTo control the transmission of coronavirus disease 2019 (COVID-19), the Chinese government encouraged people to stay at home. This study aimed to evaluate the effects of home confinement on the occurrence of fractures among children.Study designWe retrospectively reviewed children admitted to Children’s Hospital of Chongqing Medical University, for traumatic injury from January 24 to March 10, 2020, and the same time period in 2017, 2018 and 2019. At the same time, children with fracture were screened out and the date for the past 4 years was compared in terms of etiology, location of fracture, sex and age to evaluate the effects of home confinement on the epidemiology of pediatric fractures during the COVID-19 outbreak.ResultsThere were 6066 fractures in5,346 patients in 2017–2019, and 1034 fractures in 862 patients in 2020; the number of patients in all years reached a peak at the age of 2 to 4 years. The patients were slightly younger in 2020 than in 2017–2019 (t = 9.953, 95% CI: 0.846–1.262), and the proportion of boys in 2017–2019 is higher than in 2020 (X2 = 6.944, P = 0.008). Home confinement and traffic restriction resulted in a reduction in traffic accidents-associated fractures among children (X2 = 16.399, P < 0.001).ConclusionHome confinement lead to the significant reduction in the number of pediatric fractures, especially in male children, but the number of patients under 4 years old was still considerable, and the proportion of younger patients even increased. Therefore, the perspective of children should not be relaxed during home isolation.

The impact of super-spreader cities, highways, and intensive care availability in the early stages of the COVID-19 epidemic in Brazil

Although international airports served as main entry points for SARS-CoV-2, the factors driving the uneven geographic spread of COVID-19 cases and deaths in Brazil remain mostly unknown. Here we show that three major factors influenced the early macro-geographical dynamics of COVID-19 in Brazil. Mathematical modeling revealed that the “super-spreading city” of São Paulo initially accounted for more than 85% of the case spread in the entire country. By adding only 16 other spreading cities, we accounted for 98–99% of the cases reported during the first 3 months of the pandemic in Brazil. Moreover, 26 federal highways accounted for about 30% of SARS-CoV-2’s case spread. As cases increased in the Brazilian interior, the distribution of COVID-19 deaths began to correlate with the allocation of the country’s intensive care units (ICUs), which is heavily weighted towards state capitals. Thus, severely ill patients living in the countryside had to be transported to state capitals to access ICU beds, creating a “boomerang effect” that contributed to skew the distribution of COVID-19 deaths. Therefore, if (i) a lockdown had been imposed earlier on in spreader-capitals, (ii) mandatory road traffic restrictions had been enforced, and (iii) a more equitable geographic distribution of ICU beds existed, the impact of COVID-19 in Brazil would be significantly lower.

Comparing quantile regression methods for probabilistic forecasting of NO2 pollution levels

High concentration episodes for NO2 are increasingly dealt with by authorities through traffic restrictions which are activated when air quality deteriorates beyond certain thresholds. Foreseeing the probability that pollutant concentrations reach those thresholds becomes thus a necessity. Probabilistic forecasting, as oposed to point-forecasting, is a family of techniques that allow for the prediction of the expected distribution function instead of a single future value. In the case of NO2, it allows for the calculation of future chances of exceeding thresholds and to detect pollution peaks. However, there is a lack of comparative studies for probabilistic models in the field of air pollution. In this work, we thoroughly compared 10 state of the art quantile regression models, using them to predict the distribution of NO2 concentrations in a urban location for a set of forecasting horizons (up to 60 hours into the future). Instead of using directly the quantiles, we derived from them the parameters of a predicted distribution, rendering this method semi-parametric. Amongst the models tested, quantile gradient boosted trees show the best performance, yielding the best results for both expected point value and full distribution. However, we found the simpler quantile k-nearest neighbors combined with a linear regression provided similar results with much lower training time and complexity.

Influence of transportation network on transmission heterogeneity of COVID-19 in China

In this paper, we propose a novel approach to model spatial heterogeneity for epidemic spreading, which combines the relevance of transport proximity in human movement and the excellent estimation accuracy of deep neural network. We apply this model to investigate the effects of various transportation networks on the heterogeneous propagation of COVID-19 in China. We further apply it to predict the development of COVID-19 in China in two scenarios, i.e., i) assuming that different types of traffic restriction policies are conducted and ii) assuming that the epicenter of the COVID-19 outbreak is in Beijing, so as to illustrate the potential usage of the model in generating various policy insights to help the containment of the further spread of COVID-19. We find that the most effective way to prevent the coronavirus from spreading quickly and extensively is to control the routes linked to the epicenter at the beginning of the pandemic. But if the virus has been widely spread, setting restrictions on hub cities would be much more efficient than imposing the same travel ban across the whole country. We also show that a comprehensive consideration of the epicenter location is necessary for disease control.

The Assessment of the Change in the Share of Public Transportation by Applying Demand Management Policies: Evidence from Extending Traffic Restriction to the Air Pollution Control Area in Tehran

The growing car ownership has caused a lot of problems such as increased travel time and environmental pollution. In recent years, different policies have been proposed for travel demand management. Among these plans conducted in Tehran, the Odd-Even day plan starting from the door of each house or the extension of traffic congestion zone to the Odd-Even plan zone can be mentioned. In the present study, to determine the change in the behavior of the people traveling in the air pollution control area in Tehran in return for the payment of a various toll and exploring their pro-environmental beliefs and attitudes which supports the Value-Belief-Norm (VBN) theory, a stated preference questionnaire has been designed, and 500 of it were distributed among the individuals in this area and then collected. The results show that 51% of people have used their private cars to travel within the area. 24% of people have used semipublic transportation, and 25% have used public transportation (bus and subway) for their traveling. Based on the tolling design scenario, which was with an increase of 15 to 18% of the base traffic congestion zone prices of 2016, the relative frequency of using four types of non-public transportation (which is the sum of private vehicles and semipublic transportation) decreased 20 and 21% for different types of tolls throughout the day.

Air Traffic Management during Rare Events Such as a Pandemic: Paris Charles de Gaulle Case Study

Paris Charles de Gaulle Airport was the second European airport in terms of traffic in 2019, having transported 76.2 million passengers. Its large infrastructures include four runways, a large taxiway network, and 298 aircraft parking stands (131 contact) among three terminals. With the current pandemic in place, the European air traffic network has declined by −65% flights when compared with 2019 traffic (pre-COVID-19), having a severe negative impact on the aviation industry. More and more often taxiways and runways are used as parking spaces for aircraft as consequence of the drastic decrease in air traffic. Furthermore, due to safety reasons, passenger terminals at many airports have been partially closed. In this work we want to study the effect of the reduction in the physical facilities at airports on airspace and airport capacity, especially in the Terminal Manoeuvring Area (TMA) airspace, and in the airport ground side. We have developed a methodology that considers rare events such as the current pandemic, and evaluates reduced access to airport facilities, considers air traffic management restrictions and evaluates the capacity of airport ground side and airspace. We built scenarios based on real public information on the current use of the airport facilities of Paris Charles de Gaulle Airport and conducted different experiments based on current and hypothetical traffic recovery scenarios. An already known optimization metaheuristic was implemented for optimizing the traffic with the aim of avoiding airspace conflicts and avoiding capacity overloads on the ground side. The results show that the main bottleneck of the system is the terminal capacity, as it starts to become congested even at low traffic (35% of 2019 traffic). When the traffic starts to increase, a ground delay strategy is effective for mitigating airspace conflicts; however, it reveals the need for additional runways.