scholarly journals High-resolution mapping of vehicle emissions of atmospheric pollutants based on large-scale, real-world traffic datasets

2019 ◽  
Author(s):  
Daoyuan Yang ◽  
Shaojun Zhang ◽  
Tianlin Niu ◽  
Yunjie Wang ◽  
Honglei Xu ◽  
...  
Keyword(s):  
Urban Area ◽  
Real World ◽  
Vehicle Emissions ◽  
Large Scale ◽  
Emission Inventory ◽  
Road Traffic ◽  
Dispersion Model ◽  
Traffic Monitoring ◽  
Atmospheric Pollutants ◽  
Link Model

Abstract. On-road vehicle emissions are a major contributor to elevated air pollution levels in populous metropolitan areas. We developed a link-level emissions inventory of vehicular pollutants, called EMBEV-Link, based on multiple datasets extracted from the extensive road traffic monitoring network that covers the entire municipality of Beijing, China (16 400 km2). We employed the EMBEV-Link model under various traffic scenarios to capture the significant variability in vehicle emissions, temporally and spatially, due to the real-world traffic dynamics and the traffic restrictions implemented by the local government. The results revealed high carbon monoxide (CO) and total hydrocarbon (THC) emissions in the urban area (i.e., within the Fifth Ring Road) and during rush hours, both associated with the passenger vehicle traffic. By contrast, considerable fractions of nitrogen oxides (NOX), fine particulate matter (PM2.5) and black carbon (BC) emissions were present beyond the urban area, as heavy-duty trucks (HDTs) were not allowed to drive through the urban area during daytime. The EMBEV-Link model indicates that non-local HDTs could for 29 % and 38 % of estimated total on-road emissions of NOX and PM2.5, which were ignored in previous conventional emission inventories. We further combined the EMBEV-Link emission inventory and a computationally efficient dispersion model, RapidAir®, to simulate vehicular NOX concentrations at fine resolutions (10 m × 10 m in the entire municipality and 1 m × 1 m in the hotspots). The simulated results indicated a close agreement with ground observations and captured sharp concentration gradients from line sources to ambient areas. During the nighttime when the HDT traffic restrictions are lifted, HDTs could be responsible for approximately 10 μg m−3 of NOX in the urban area. The uncertainties of conventional top-down allocation methods, which were widely used to enhance the spatial resolution of vehicle emissions, are also discussed by comparison with the EMBEV-Link emission inventory.

scholarly journals High-resolution mapping of vehicle emissions of atmospheric pollutants based on large-scale, real-world traffic datasets

2019 ◽  
Vol 19 (13) ◽  
pp. 8831-8843 ◽  
Author(s):  
Daoyuan Yang ◽  
Shaojun Zhang ◽  
Tianlin Niu ◽  
Yunjie Wang ◽  
Honglei Xu ◽  
...  
Keyword(s):  
Urban Area ◽  
Real World ◽  
Vehicle Emissions ◽  
Large Scale ◽  
Emission Inventory ◽  
Road Traffic ◽  
Dispersion Model ◽  
Traffic Monitoring ◽  
Atmospheric Pollutants ◽  
Link Model

Abstract. On-road vehicle emissions are a major contributor to elevated air pollution levels in populous metropolitan areas. We developed a link-level emissions inventory of vehicular pollutants, called EMBEV-Link (Link-level Emission factor Model for the BEijing Vehicle fleet), based on multiple datasets extracted from the extensive road traffic monitoring network that covers the entire municipality of Beijing, China (16 400 km2). We employed the EMBEV-Link model under various traffic scenarios to capture the significant variability in vehicle emissions, temporally and spatially, due to the real-world traffic dynamics and the traffic restrictions implemented by the local government. The results revealed high carbon monoxide (CO) and total hydrocarbon (THC) emissions in the urban area (i.e., within the Fifth Ring Road) and during rush hours, both associated with the passenger vehicle traffic. By contrast, considerable fractions of nitrogen oxides (NOx), fine particulate matter (PM2.5) and black carbon (BC) emissions were present beyond the urban area, as heavy-duty trucks (HDTs) were not allowed to drive through the urban area during daytime. The EMBEV-Link model indicates that nonlocal HDTs could account for 29 % and 38 % of estimated total on-road emissions of NOx and PM2.5, which were ignored in previous conventional emission inventories. We further combined the EMBEV-Link emission inventory and a computationally efficient dispersion model, RapidAir®, to simulate vehicular NOx concentrations at fine resolutions (10 m × 10 m in the entire municipality and 1 m × 1 m in the hotspots). The simulated results indicated a close agreement with ground observations and captured sharp concentration gradients from line sources to ambient areas. During the nighttime when the HDT traffic restrictions are lifted, HDTs could be responsible for approximately 10 µg m−3 of NOx in the urban area. The uncertainties of conventional top-down allocation methods, which were widely used to enhance the spatial resolution of vehicle emissions, are also discussed by comparison with the EMBEV-Link emission inventory.

The Modification Design of Road Traffic Information Mobile Monitoring Vehicle

2012 ◽  
Vol 490-495 ◽  
pp. 721-725
Author(s):  
Ying Jie Qi ◽  
Qiang Wang
Keyword(s):  
Large Scale ◽  
Road Traffic ◽  
Three Dimensional ◽  
Traffic Monitoring ◽  
Traffic Information ◽  
Information Collection ◽  
Mobile Monitoring ◽  
Element Analysis ◽  
Three Dimensional Modeling ◽  
The Cost

At present, video monitoring equipments on highways in china are mostly used in a fixed position of the main roads, and the wired mode is generally used to transmit data. The purpose to improve the monitoring areas can be achieved by increasing the monitoring equipments and other means, but the cost of these methods are too high and the data transmission is limited by area, greatly restricting the large-scale application of those traffic monitoring equipments. In this paper, in order to achieve the function of road traffic information mobile monitoring, it completed the modification design of road traffic information mobile monitoring vehicle by use of CAD/CAE computer-aided designing tools. In this process, Jinbei SY6480 minibus was selected as the modification object, and the new re-layout in the car was implemented, and the lifting mechanism and vehicle wind-resistant design on the information collection equipment were especially included. Finally the three-dimensional modeling and finite element analysis were advanced by use of Pro/E and ANSYS software, ensuring the rationality of design. The vehicle designed by this paper can ensure that the information collection equipment achieve the intelligent monitoring function safely, stably and quickly.

scholarly journals A Low-Cost Open Hardware System for Collecting Traffic Data Using Wi-Fi Signal Strength

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3623 ◽  
Author(s):  
Shivam Gupta ◽  
Albert Hamzin ◽  
Auriol Degbelo
Keyword(s):  
Large Scale ◽  
Road Traffic ◽  
Smart Cities ◽  
Low Cost ◽  
Heavy Traffic ◽  
Traffic Monitoring ◽  
Traffic Data ◽  
Open Hardware ◽  
Loop Detectors ◽  
Hardware System

Road traffic and its impacts affect various aspects of wellbeing with safety, congestion and pollution being of significant concern in cities. Although there have been a large number of works done in the field of traffic data collection, there are several barriers which restrict the collection of traffic data at higher resolution in the cities. Installation and maintenance costs can act as a disincentive to use existing methods (e.g., loop detectors, video analysis) at a large scale and hence limit their deployment to only a few roads of the city. This paper presents an approach for vehicle counting using a low cost, simple and easily installable system. In the proposed system, vehicles (i.e., bicycles, cars, trucks) are counted by means of variations in the WiFi signals. Experiments with the developed hardware in two different scenarios—low traffic (i.e., 400 objects) and heavy traffic roads (i.e., 1000 objects)—demonstrate its ability to detect cars and trucks. The system can be used to provide estimates of vehicle numbers for streets not covered by official traffic monitoring techniques in future smart cities.

scholarly journals High-resolution mapping of regional traffic emissions by using land-use machine learning models

2021 ◽  
Author(s):  
Xiaomeng Wu ◽  
Daoyuan Yang ◽  
Jiajun Gu ◽  
Yifan Wen ◽  
Shaojun Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
High Resolution ◽  
Vehicle Emissions ◽  
Road Traffic ◽  
Fine Particulate Matter ◽  
Gaussian Process Regression ◽  
Traffic Monitoring ◽  
Emission Inventories ◽  
Learning Approaches

Abstract. On-road vehicle emissions are a major contributor to significant atmospheric pollution in populous metropolitan areas. We developed an hourly-based, link-level emissions inventory of vehicular pollutants using two land-use machine learning methods based on the datasets of road traffic monitoring in the Beijing-Tianjin-Hebei (BTH) region. The results indicate that a land-use random forest (LURF) model is more capable of predicting traffic profiles than a Gaussian process regression (GPR) model. The inventories under three different traffic scenarios depict a significant temporal and spatial variability in vehicle emissions. One notable finding is that NOX, fine particulate matter (PM2.5) and black carbon (BC) emissions from heavy-duty trucks (HDTs) in general have higher emission intensity on the highways connecting to regional ports. Even when traffic restrictions were implemented, a detour of the HDTs in Hebei was observed resulting in relatively lower emission reductions in Hebei than Beijing. This study demonstrates the power of machine learning approaches to generate data-driven and high-resolution emission inventories, which provides a platform to realize the near real-time process of establishing high-resolution vehicle emission inventories for policy makers to engage in sophisticated traffic management.

Road Traffic Monitoring

IEE Review ◽  
1989 ◽  
Vol 35 (5) ◽  
pp. 188
Author(s):  
P.L. Belcher
Keyword(s):  
Road Traffic ◽  
Traffic Monitoring

1588-P: Therapy Trends in Initial 6 Months of the First Large-Scale Longitudinal Nationwide Study on Management and Real-World Outcomes of Diabetes in India (LANDMARC)

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1588-P ◽  
Author(s):  
ROMIK GHOSH ◽  
ASHOK K. DAS ◽  
AMBRISH MITHAL ◽  
SHASHANK JOSHI ◽  
K.M. PRASANNA KUMAR ◽  
...  
Keyword(s):  
Real World ◽  
Large Scale ◽  
Nationwide Study

2258-PUB: Impact of Cardiovascular (CV) Risk Factors during the Initial 6 Months of the First Large-Scale Longitudinal Nationwide Study on Management and Real-World Outcomes of Diabetes in India (LANDMARC)

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 2258-PUB
Author(s):  
ROMIK GHOSH ◽  
ASHOK K. DAS ◽  
SHASHANK JOSHI ◽  
AMBRISH MITHAL ◽  
K.M. PRASANNA KUMAR ◽  
...  
Keyword(s):  
Risk Factors ◽  
Real World ◽  
Large Scale ◽  
Nationwide Study

scholarly journals The graph neural networking challenge

2021 ◽  
Vol 51 (3) ◽  
pp. 9-16
Author(s):  
José Suárez-Varela ◽  
Miquel Ferriol-Galmés ◽  
Albert López ◽  
Paul Almasan ◽  
Guillermo Bernárdez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computer Networks ◽  
Real World ◽  
Large Scale ◽  
Lessons Learned ◽  
Educational Resources ◽  
Global Competition ◽  
International Telecommunication Union ◽  
International Telecommunication ◽  
Broad Audience

During the last decade, Machine Learning (ML) has increasingly become a hot topic in the field of Computer Networks and is expected to be gradually adopted for a plethora of control, monitoring and management tasks in real-world deployments. This poses the need to count on new generations of students, researchers and practitioners with a solid background in ML applied to networks. During 2020, the International Telecommunication Union (ITU) has organized the "ITU AI/ML in 5G challenge", an open global competition that has introduced to a broad audience some of the current main challenges in ML for networks. This large-scale initiative has gathered 23 different challenges proposed by network operators, equipment manufacturers and academia, and has attracted a total of 1300+ participants from 60+ countries. This paper narrates our experience organizing one of the proposed challenges: the "Graph Neural Networking Challenge 2020". We describe the problem presented to participants, the tools and resources provided, some organization aspects and participation statistics, an outline of the top-3 awarded solutions, and a summary with some lessons learned during all this journey. As a result, this challenge leaves a curated set of educational resources openly available to anyone interested in the topic.

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