Sensitive Resource and Traffic Density Risk Analysis of Marine Spill Accidents Using Automated Identification System Big Data

This study developed a new methodology for analyzing the risk level of marine spill accidents from two perspectives,namely,marine traffic density and sensitive resources.Through a case study conducted in Busan,South Korea,detailed procedures of the methodology were proposed and its scalability was confirmed.To analyze the risk from a more detailed and microscopic viewpoint,vessel routes as hazard sources were delineated on the basis of automated identification system(AIS)big data.The outliers and errors of AIS big data were removed using the density-based spatial clustering of applications with noise algorithm,and a marine traffic density map was evaluated by combining all of the gridded routes.Vulnerability of marine environment was identified on the basis of the sensitive resource map constructed by the Korea Coast Guard in a similar manner to the National Oceanic and Atmospheric Administration environmental sensitivity index approach.In this study,aquaculture sites,water intake facilities of power plants,and beach/resort areas were selected as representative indicators for each category.The vulnerability values of neighboring cells decreased according to the Euclidean distance from the resource cells.Two resulting maps were aggregated to construct a final sensitive resource and traffic density(SRTD)risk analysis map of the Busan–Ulsan sea areas.We confirmed the effectiveness of SRTD risk analysis by comparing it with the actual marine spill accident records.Results show that all of the marine spill accidents in 2018 occurred within 2 km of high-risk cells(level 6 and above).Thus,if accident management and monitoring capabilities are concentrated on high-risk cells,which account for only 6.45%of the total study area,then it is expected that it will be possible to cope with most marine spill accidents effectively.

Smart Parking Management System “MOTAH”

With a surge in the university’s student and staff population, parking problems and congestion have rapidly intensified. The recent inclusion of women drivers, particularly during official working hours, has exacerbated these challenges. This pressing issue underscores the critical necessity for a structured approach to managing university entries and overseeing parking at the gates. The proposed smart parking management system aims to address these concerns by introducing a design concept that restricts unauthorized access and provides exclusive parking privileges to authorized users. Through image processing, the system identifies available parking spaces, relaying real-time information to users via a mobile application. This comprehensive solution also generates detailed reports (daily, weekly, and monthly), aiding university safety authorities in future gate management decisions.

Multiple Extreme Learning Machines Based Arrival Time Prediction for Public Bus Transport

Due to fast-growing urbanization,the traffic management system becomes a crucial problem owing to the rapid growth in the number of vehicles The research proposes an Intelligent public transportation system where informa-tion regarding all the buses connecting in a city will be gathered,processed and accurate bus arrival time prediction will be presented to the user.Various linear and time-varying parameters such as distance,waiting time at stops,red signal duration at a traffic signal,traffic density,turning density,rush hours,weather conditions,number of passengers on the bus,type of day,road type,average vehi-cle speed limit,current vehicle speed affecting traffic are used for the analysis.The proposed model exploits the feasibility and applicability of ELM in the travel time forecasting area.Multiple ELMs(MELM)for explicitly training dynamic,road and trajectory information are used in the proposed approach.A large-scale dataset(historical data)obtained from Kerala State Road Transport Corporation is used for training.Simulations are carried out by using MATLAB R2021a.The experiments revealed that the efficiency of MELM is independent of the time of day and day of the week.It can manage huge volumes of data with less human intervention at greater learning speeds.It is found MELM yields prediction with accuracy in the range of 96.7%to 99.08%.The MAE value is between 0.28 to 1.74 minutes with the proposed approach.The study revealed that there could be regularity in bus usage and daily bus rides are predictable with a better degree of accuracy.The research has proved that MELM is superior for arrival time pre-dictions in terms of accuracy and error,compared with other approaches.

A census tract-level assessment of social determinants of health,traffic exposure,and asthma exacerbations in New York State's Medicaid Population(2005–2015)

This study aims to evaluate the association between social determinants,environmental exposure metrics,and the risk of asthma emergency department(ED)visits in the New York State(NYS)Medicaid population using smallarea analysis.Traffic densities for each census tract in NYS were calculated using the length of road segments within each tract and total area of the tract to produce a measure of average number of vehicles per square meter per day.Data on social determinants of health including internal and external environments and other demographic factors were obtained from various sources.Poisson regression analyses were conducted to identify significant factors associated with asthma ED visits in Medicaid claim and encounter data for years 2005–2015.High traffic density in NYS excluding New York City(NYC)correlated with increased risk of asthma ED visits(RR 1.69;95%CI:1.42,2.00),mitigated by adjusting for environmental and social determinants(RR 1.00;95%CI:0.85,1.19).Similar trends were observed in NYC only(RR 1.19;95%CI:1.00,1.41),with the adjusted risk remaining elevated(RR 1.14;95%CI:0.98,1.33)albeit not statistically significant.Living in census tracts with high concentrated disadvantage index,high proportions of minorities,and less green space predicted higher asthma ED visits.We mapped predicted rates and model residuals to identify areas of high risk.Our results support previous findings that environmental and social risk factors in poor and urban areas contribute to asthma exacerbations in the NYS Medicaid population,even if they may not necessarily contribute to its development.

Iterative learning control approach for ramp metering

An iterative learning control scheme is developed to the traffic densitycontrol in a macroscopic level freeway environment. With rigorous analysis, the proposed intelligentcontrol scheme guarantees the asymptotic convergence of the traffic density to the desired one. Thecontrol scheme is applied to a freeway model, and simulation results confirm the efficacy of theproposed approach.

Lead levels in Chromolaena odorata (Compositae) along rural and urban roads in Akwa Ibom State, Nigeria

Concentrations of lead(Pb) were determined in leaf samples of \%Chromolaena adorata\% from a rural road and an urban road in Akwa Ibom State of Nigeria in relation to vehicular traffic density. Mixed results were obtained wich no trend to indict automobile exhaust fumes. Lead was not detected in rain water from these areas and leaf lead levels seemed to relate more to soil lead concentrations. There was no significant difference in mean leaf lead levels between rural and urban leaf samples and between washed and unwashed leaf samples. It was suggested that the lack of concordance between vehicular traffic density and lead levels in leaves along the highways indicates either a low level use of tetraethyl leaded petrol or, a relatively low vehicular traffic density with lead from exhaust fumes not reaching a threshold to affect levels in plants or be detected in the atmosphere. Implications of concentrations obtained in leaves to consumers when compared with international standards were discussed.

Comparison of Mosses as Bioindicator of Heavy Metal Pollution in Aramoko-Ekiti and Are-Ekiti, Nigeria

The increasing global concern over the public health impacts attributed to environmental pollution led us to investigate and compare the deposition of some heavy metals in mosses from an urban area, Aramoko Ekiti and a rural area, Are Ekiti. Both towns are located in the SouthWest, Nigeria. Moss samples were collected at different sites in the two towns. These samples were then digested in acid and analysed for Cd, Cr, Ni, Pb and Cu, using a flame atomic absorption spectrometer. The results of the study show variations in the concentrations of the heavy metals among the different sites in each town as well as between the two towns. Apart from cd which was suspected to have originated from natural sources in the investigated samples, the relatively higher concentrations of the other metals in Aramoko-Ekiti suggests an important anthropogenic source which we suspect to be automobiles since there are little or no industrial or mining activities within the town. Furthermore, the relatively higher concentrations of the metals exhibited by moss samples collected around locations prone to higher traffic situations in the two towns such as roadsides, filling stations and garages stresses the significance of traffic density in heavy metal pollution of the environment. These places （filling stations and garages） should be sited far away from residential areas. Also, residences should be sited at considerably far distances from major roads. These will prevent the bioaccumulation of the heavy metals in residents. Though, the results show that Aramoko-Ekiti is more polluted with the heavy metals than Are-Ekiti, the concentration of the heavy metals were still within the permissible limits. Given the results of this work and similar ones, there is need to evaluate the pollution status of the environment from time to time especially the urban areas and high traffic areas.

ComplexTrans-Global Rail-Road Transportation System Economical and Clustered Individual and Individualised Public Transport also Prevents the Spread of Covid 19

Land transport can no longer meet the requirements.European transport can be described by these words−crowded motorways and cities,dangerous emissions,ubiquitous traffic accidents,delays,expensive railways.Solutions are being sought to transfer a large part of passengers and especially freight transport to(high-speed)rail,and efforts are moving towards electromobility,car-sharing,5G-connectivity,autonomous driving,MaaS(Mobility as a Service)-coordinated transport or hyperloop-type solutions.However,all these solutions have additional challenges and limitations.Solutions are not being searched where they really exist-in the mutual adaptation of road and rail vehicles and their deep cooperation.The ComplexTrans project shows that simply adapting the dimensions and functions of road and rail vehicles can eliminate(or at least significantly reduce)all the problems of existing land transport.The main features of the ComplexTrans system are sufficient parking spaces,reduction of urban and non-urban congestion,electric vehicles with unlimited range and cheaper than standard cars,cheaper and more accessible battery charging,“autonomous ride”,solving the overlap between passenger and freight rail transport and making it self-financing,transferring intercity freight transport to rail,replacing part of continental air transport and many others.The cost-effective and clustered individual transport and individualised public transport of the ComplexTrans system also bring very significant reductions in the risk of transmission of covid-19 and other contagious diseases during transport.

Metal Accumulation Capability by <i>Platanus acerifolia</i>(Aiton) Willd., <i>Ailantus altissima</i>(Mill.) Swingle, <i>Robinia pseudoacacia</i>L. and <i>Quercus ilex</i>L., Largely Distributed in the City of Rome

The main of the research was to analyze the leaf metal accumulation capability of Platanus acerifolia (Aiton) Willd., Ailantus altissima (Mill.) Swingle, Robinia pseudoacacia L. and Quercus ilex L., largely distributed in Rome. In addition, metal concentration was analyzed in the soil, sampling sites were chosen in historical parks (A sites) and high traffic level sites (B sites). The results highlight significant higher leaf and soil metal concentrations in B than in A sites. The ratio between metal concentration in leaves and soils (Biological Absorption Coefficient, BAC) for all the considered sites was significantly different among the species. Morphological and anatomical leaf traits of the considered species show significant differences in A and B sites in response to traffic level. Overall, the results highlight the importance of the selection of tree species in urban areas for their ability to lower pollution levels.

Integrated Spacing Policy Considering Micro- and Macroscopic Characteristics

An appropriate spacing policy improves traffic flow and traffic efficiency while reducing commuting time and energy con-sumption.In this paper,the integrated spacing policy that combines the benefits of the constant time headway(CTH)and safety distance(SD)spacing policies is proposed in an attempt to improve traffic flow and efficiency.Firstly,the performance of the CTH and SD spacing policies is analyzed from the perspective of the microscopic characteristics of human-vehicle and the macroscopic characteristics of traffic flow.The switching law between CTH and SD spacing policies and the integrated spacing policy are then proposed to increase traffic efficiency according to the traffic conditions,and the critical speed for the proposed integrated spacing policy is derived.Using the proposed switching law,the integrated spacing policy utilizes the safety redundancy difference between the CTH and SD spacing policies in a flexible manner.Simulation tests demon-strate that the proposed integrated spacing policy increases traffic flow and that the traffic flow maintains string stability in a wider range of traffic flow density.