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•Air pollution, traffic noise and greenness, and myocardial infarction incidence.•Time-varying information on exposures, socioeconomic and lifestyle factors.•Inverse fully adjusted ...association between greenness and myocardial infarction.•Associations with air pollution (PM2.5) in subgroups only, e.g. less educated.•No associations between road traffic noise and myocardial infarction.
Emerging evidence shows that long-term exposure to air pollution, road traffic noise, and greenness can each be associated with cardiovascular disease, but only few studies combined these exposures. In this study, we assessed associations of multiple environmental exposures and incidence of myocardial infarction using annual time-varying predictors.
In a population-based cohort of 20,407 women in Sweden, we estimated a five-year moving average of residential exposure to air pollution (PM2.5, PM10 and NO2), road traffic noise (Lden), and greenness (normalized difference vegetation index, NDVI in 500 m buffers), from 1998 to 2017 based on annually varying exposures and address history. We used adjusted time-varying Cox proportional hazards regressions to estimate hazard ratios (HR) and 95 % confidence intervals (95 % CI) of myocardial infarction per interquartile range (IQR). Furthermore, we investigated interactions between the exposures and explored potential vulnerable subgroups.
In multi-exposure models, long-term exposure to greenness was inversely associated with incidence of myocardial infarction (HR 0.89; 95 % CI 0.80, 0.99 per IQR NDVI increase). Stronger associations were observed in some subgroups, e.g. among women with low attained education and in overweight (BMI ≥ 25 kg/m2) compared to their counterparts. For air pollution, we observed a tendency of an increased risk of myocardial infarction in relation to PM2.5 (HR 1.07; 95 % CI 0.93, 1.23) and the association appeared stronger in women with low attained education (HR 1.30; 95 % CI 1.06, 1.58). No associations were observed for PM10, NO2 or road traffic noise. Furthermore, there were no clear interaction patterns between the exposures.
Over a 20-year follow-up period, in multi-exposure models, we found an inverse association between residential greenness and risk of myocardial infarction among women. Furthermore, we observed an increased risk of myocardial infarction in relation to PM2.5 among women with low attained education. Road traffic noise was not associated with myocardial infarction.
The study aims to develop simplified rational criteria for identification of accident blackspots along the National Highways, so that the practitioners can quickly identify the blackspots and ...intervene. A statistical approach has been used to identify the rational criteria, i.e., the threshold value of each of the different crash parameters were identified, above which a location will be treated as a blackspot. The analysis is based on past 3-years’ crash data of NH of West Bengal, India. The identified blackspots have been compared with the Ministry of Road Transport and Highways (MoRTH), Govt. of India guidelines and it is found that the number of blackspots by MoRTH guidelines is grossly over-estimated. Finally, the blackspots have been prioritized based on its severity as observed from the analysis. This will help the practitioners prioritize the blackspot locations while planning the countermeasures. Although the work has been demonstrated based on crash data of West Bengal, the technique can be adopted by other researchers or practitioners to determine the simplified rational criteria for other regions as well.
Comparison of effective road safety approaches with those of relatively similar countries can be used to identify possibilities for safety improvement. Since there is no clear and comprehensive study ...of countries' current and successful approaches to road safety in the world, the aim of this study was to identifying common road safety approaches in the world.
This study was performed using scoping review and thematic analysis. The study followed the approach proposed by Arksey and O'Malley. In this study all articles were selected without time limit by searching in the following databases: Web of Science, PubMed, Scopus, ProQuest, and Embase. An initial search of 5612 papers was found and finally, 20 papers met the inclusion criteria and were analyzed.
There were different road safety approaches in different countries around the world, which were classified in three themes: traditional approach, systemic approach, and vision zero. The traditional approach includes the sub-theme of the road-user approach, and the causal approach. The systemic approach also includes sub-themes of sustainable safety, safety system, and the United Nations plan for decade of action.
A systemic approach to road safety seems to be welcomed by most developed and developing countries, and a paradigm shift towards a safe system has taken place. Also, given the successful results of implementing vision zero in leading countries, most countries are trying to design and implement this approach. Finally, the choice and implementation of road safety approaches varies according to the principles, priorities and infrastructure of each country.
Fog computing extends the facility of cloud computing from the center to edge networks. Although fog computing has the advantages of location awareness and low latency, the rising requirements of ...ubiquitous connectivity and ultra-low latency challenge real-time traffic management for smart cities. As an integration of fog computing and vehicular networks, vehicular fog computing (VFC) is promising to achieve real-time and location-aware network responses. Since the concept and use case of VFC are in the initial phase, this article first constructs a three-layer VFC model to enable distributed traffic management in order to minimize the response time of citywide events collected and reported by vehicles. Furthermore, the VFC-enabled offloading scheme is formulated as an optimization problem by leveraging moving and parked vehicles as fog nodes. A real-world taxi-trajectory-based performance analysis validates our model. Finally, some research challenges and open issues toward VFC-enabled traffic management are summarized and highlighted.
Computer vision has evolved in the last decade as a key technology for numerous applications replacing human supervision. Timely detection of traffic violations and abnormal behavior of pedestrians ...at public places through computer vision and visual surveillance can be highly effective for maintaining traffic order in cities. However, despite a handful of computer vision–based techniques proposed in recent times to understand the traffic violations or other types of on-road anomalies, no methodological survey is available that provides a detailed insight into the classification techniques, learning methods, datasets, and application contexts. Thus, this study aims to investigate the recent visual surveillance–related research on anomaly detection in public places, particularly on road. The study analyzes various vision-guided anomaly detection techniques using a generic framework such that the key technical components can be easily understood. Our survey includes definitions of related terminologies and concepts, judicious classifications of the vision-guided anomaly detection approaches, detailed analysis of anomaly detection methods including deep learning–based methods, descriptions of the relevant datasets with environmental conditions, and types of anomalies. The study also reveals vital gaps in the available datasets and anomaly detection capability in various contexts, and thus gives future directions to the computer vision–guided anomaly detection research. As anomaly detection is an important step in automatic road traffic surveillance, this survey can be a useful resource for interested researchers working on solving various issues of Intelligent Transportation Systems (ITS).
Covid19-induced lockdown measures caused modifications in atmospheric pollutant and greenhouse gas emissions. Urban road traffic was the most impacted, with 48–60% average reduction in Italy. This ...offered an unprecedented opportunity to assess how a prolonged (∼2 months) and remarkable abatement of traffic emissions impacted on urban air quality. Six out of the eight most populated cities in Italy with different climatic conditions were analysed: Milan, Bologna, Florence, Rome, Naples, and Palermo. The selected scenario (24/02/2020–30/04/2020) was compared to a meteorologically comparable scenario in 2019 (25/02/2019–02/05/2019). NO2, O3, PM2.5 and PM10 observations from 58 air quality and meteorological stations were used, while traffic mobility was derived from municipality-scale big data.
NO2 levels remarkably dropped over all urban areas (from −24.9% in Milan to −59.1% in Naples), to an extent roughly proportional but lower than traffic reduction. Conversely, O3 concentrations remained unchanged or even increased (up to 13.7% in Palermo and 14.7% in Rome), likely because of the reduced O3 titration triggered by lower NO emissions from vehicles, and lower NOx emissions over typical VOCs-limited environments such as urban areas, not compensated by comparable VOCs emissions reductions. PM10 exhibited reductions up to 31.5% (Palermo) and increases up to 7.3% (Naples), while PM2.5 showed reductions of ∼13–17% counterbalanced by increases up to ∼9%. Higher household heating usage (+16–19% in March), also driven by colder weather conditions than 2019 (−0.2 to −0.8 °C) may partly explain primary PM emissions increase, while an increase in agriculture activities may account for the NH3 emissions increase leading to secondary aerosol formation. This study confirmed the complex nature of atmospheric pollution even when a major emission source is clearly isolated and controlled, and the need for consistent decarbonisation efforts across all emission sectors to really improve air quality and public health.
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•Air quality impacts of Covid19 lockdown over 6 major Italian cities were assessed.•Daily observations of air quality, meteorological parameters and mobility were used.•Against a dramatic road traffic reduction (48–60%), NO2 levels reduced by 24.9–59.1%.•O3 levels remained basically unchanged or slightly increased (up to 11.4–14.7%).•PM daily concentrations slightly decreased, at most by 17% (PM2.5) and 32.1% (PM10).
Main finding A 2-month urban traffic ban extended to the whole Italy only significantly reduced NO2 levels, while O3, PM2.5 and PM10 concentrations were affected to a minor extent.
•Air pollution, traffic noise and lack of green space have been associated with diabetes in analyses mainly focusing on one or two environmental factors at a time.•We aimed to investigate if air ...pollution, road traffic noise and green space are independent risk factors of type 2 diabetes.•In a multi-pollutant analysis, ultrafine particles, NO2, noise at both most and least exposed façade and two proxies of lack of green space were all associated with higher risk of type 2 diabetes.•The cumulative risk estimate of the multi-pollutant analysis was much higher than the risk estimate of any single pollutant.
Air pollution, road traffic noise and lack of greenness coexist in urban environments and have all been associated with type 2 diabetes. We aimed to investigate how these co-exposures were associated with type 2 diabetes in a multi-exposure perspective.
We estimated 5-year residential mean exposure to fine particles (PM2.5), ultrafine particles (UFP), elemental carbon (EC), nitrogen dioxide (NO2) and road traffic noise at the most (LdenMax) and least (LdenMin) exposed facade for all persons aged > 50 years living in Denmark in 2005 to 2017. For each air pollutant, we estimated total concentrations and traffic contributions. Based on land use maps, we estimated proportion of green and non-green space within 150 and 1000 m of all residences. In total, 1.9 million persons were included and 128,358 developed type 2 diabetes during follow-up. We performed analyses using Cox proportional hazards models, with adjustment for individual and neighborhood-level sociodemographic co-variates.
In single-pollutant models, all air pollutants, noise and lack of green space were associated with higher risk of diabetes. In two-, three- and four-pollutant analyses of the air pollutants, only UFP and NO2 remained associated with higher diabetes risk in all models. LdenMax, LdenMin and the two proxies of green space remained associated with diabetes in two-pollutant models of, respectively, noise and green space. In a multi-pollutant analysis, we found hazard ratios (95 % confidence intervals) per interquartile range of 1.021 (1.005; 1.038) for UFP, 1.012 (0.996; 1.028) for NO2, 1.022 (1.012; 1.033) for LdenMin, 1.013 (1.004; 1.022) for LdenMax, and 1.038 (1.031; 1.044) and 1.018 (1.010; 1.025) for lack of green space within, respectively, 150 m and 1000 m, and a cumulative risk index of 1.131 (1.113; 1.149).
Air pollution, road traffic noise and lack of green space were independently associated with higher risk of type 2 diabetes.
•A higher accuracy electric vehicle performance simulation model is developed.•An electric vehicle model is calibrated by the experimental data.•Average vehicle speed of driving cycle has main impact ...on energy consumption.•Vehicle with regenerative braking saves 2.43% energy under congested traffic.
In order to predict and study the effects of different parameters on performance characteristics of electric vehicles. A vehicle simulation model of pure battery electric vehicles equipped with single pedal control system is established and calibrated by the experimental data based on vehicle energy flow and driving range analysis, the simulation doesn’t include thermal aspect of the battery/vehicle. Next, the effects of different environmental and control parameters on energy consumption and driving range of pure electric vehicles are analyzed. The main findings are: (1) for the single driving cycle, the relative error of battery power and current is below 5%, and the absolute error of battery voltage is below 2.5 V. For the whole driving range, the absolute error of driving range is only about 5.75 km. (2) The main factors influencing energy consumption and driving range are average vehicle speed, running time and the frequency distribution of braking process, besides, the energy consumption of congested traffic with/without regenerative brake control system are 46.07 kW·h/100 km and 47.19 kW·h/100 km, respectively, meanwhile, vehicle with regenerative braking saves 2.43% energy under congested traffic. (3) The threshold of quitting the working condition of energy recovery for the motor can be set in a certain value based on the safety of driver in the emergencies and energy conversion. Further, the model and data in the paper can be applied to evaluate and optimize the energy consumption and driving range by changing different technologies or strategies in the future.
In the Republic of Korea, Environmental Impact Assessment (EIAs) precedes development projects to predict and analyze potential environmental effects. Generally, EIA noise evaluations utilize 2D ...noise prediction equations and correction coefficients. This method, however, offers only a sectional noise evaluation and has limitations in complex environments with diverse noise sources. Moreover, the determination of various variables during the EIA process based on subjective human judgment raises concerns about the reliability of the results. Thus, this study aims to develop software accessible via a web environment for user-friendly EIA noise evaluations. This software supports integrated data management and generates a 3D noise prediction model for more precise and realistic analysis of noise impacts, specifically focusing on road-traffic noise at this stage of development. The 3D noise prediction model and noise map generated by the developed software have been validated against through comparison with the results of onsite noise measurements and commercial EIA software, SoundPLAN. This validation aimed to assess the practical utility of the application.
•Developed WONSE, an innovative EIA (Environment Impact Assessment) software for noise.•Practicality of WONSE verified through field noise measurements and SoundPLAN comparison.•Offers cost-effective, transparent noise EIA solutions.
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