Driving cycle tracking device big data storing and management

<span>Driving cycle is commonly known as a series of speed-time profile. Research on this discipline aids vehicle manufacturing industries in vehicle manufacturing, environmentalists to study on environment quality and profile in accordance to vehicle emissions besides traffic engineers to further investigate the behavior of drivers and the conditions of roads in a certain area or cluster. This also assists automotive industries to innovate energy efficient vehicles which reduce vehicle emissions and energy wastages which lead to air pollution in which a major threat for human health according to Goal 3 of united nations (UN) sustainable development goals (SDG). To construct an accurate driving cycle, data based on real-world driving behavior is crucial and as the world is advancing in technology, the usage of internet of things (IoT) plays an important role in innovatietcons. IoT is an idea of computing every day physical object and information into computers, devices and software. These devices work by using sensors that transmit data to a computer or software allowing them to perform important tasks as needed. In this research, an idea of data collecting device, driving cycle tracking device (DC-TRAD) is constructed with implementation of IoT in which the collected data will be saved into my structured query language (MySQL) database instantly for data storing.</span>

Investigating the Effect of Traffic Flow on Pollution, Noise for Urban Road Network

Congestion has a significant impact on the environment. It’s the predominant source of pollution, as noise and air pollution. The sound produced by vehicles as well as horns creates the worst possible environment. High motorized traffic flow nowadays is the major contributor to rising externalities, vehicle emissions, and other pollutants that impact the environment and the atmosphere, which result in negative atmospheric phenomena, global warming, and climate change. Vehicle emissions cause numerous vulnerabilities, so a serious consequence may arise in the long term, both regional and global. This study investigated Noise and pollution for different roads in the different cities based on field data at peak periods of traffic flow, shows that the major pollutants that are emitted from engines are: nitrogen oxides (NOX), carbon monoxide (CO), unburned hydrocarbons (CxHy), sulfur oxides (SOX), solid particles, including aerosols, as well as carbon dioxide (CO2).

Spatial-temporal variation of CO2 emissions from private vehicle use in Japan

The transport sector is a major contributor to anthropogenic climate change through the emissions of large amounts of greenhouse gases (GHGs) from fossil fuel combustion. Private vehicles account for almost half of the transport energy demand, and are thus a major target of climate change mitigation efforts. However, emissions from private vehicles can have large variability due to various geographic, demographic and socioeconomic factors. This study aims to understand how such factors affect private vehicle emissions in Japan using a nationally representative survey of household energy consumption (n=7,370) for 2017. The results indicate a large temporal and spatial variability in private vehicle emissions. Annual emissions show three peaks associated with major holiday seasons in winter and summer. Some of the more noteworthy spatial patterns are the higher emissions in prefectures characterized by low population density and mountainous terrain. Income, city size and the fuel-saving driving behavior all have a significant effect on emissions. The results indicate the need for sub-regional and socioeconomically-sensitive mitigation efforts that reflect the very different emission patterns, and the factors affecting them. The strong effect of city size, which is often much more clear-cut than between prefectures, suggests that it is more appropriate to approach transport decarbonization in Japan at the city level.

Hyperfine-resolution mapping of on-road vehicle emissions with comprehensive traffic monitoring and an intelligent transportation system

Urban on-road vehicle emissions affect air quality and human health locally and globally. Given uneven sources, they typically exhibit distinct spatial heterogeneity, varying sharply over short distances (10 m–1 km). However, all-around observational constraints on the emission sources are limited in much of the world. Consequently, traditional emission inventories lack the spatial resolution that can characterize the on-road vehicle emission hotspots. Here we establish a bottom-up approach to reveal a unique pattern of urban on-road vehicle emissions at a spatial resolution 1–3 orders of magnitude higher than current emission inventories. We interconnect all-around traffic monitoring (including traffic fluxes, vehicle-specific categories, and speeds) via an intelligent transportation system (ITS) over Xiaoshan District in the Yangtze River Delta (YRD) region. This enables us to calculate single-vehicle-specific emissions over each fine-scale (10 m–1 km) road segment. Thus, the most hyperfine emission dataset of its type is achieved, and on-road emission hotspots appear. The resulting map shows that the hourly average on-road vehicle emissions of CO, NOx, HC, and PM2.5 are 74.01, 40.35, 8.13, and 1.68 kg, respectively. More importantly, widespread and persistent emission hotspots emerged. They are of significantly sharp small-scale variability, up to 8–15 times within individual hotspots, attributable to distinct traffic fluxes, road conditions, and vehicle categories. On this basis, we investigate the effectiveness of routine traffic control strategies on on-road vehicle emission mitigation. Our results have important implications for how the strategies should be designed and optimized. Integrating our traffic-monitoring-based approach with urban air quality measurements, we could address major data gaps between urban air pollutant emissions and concentrations.