Fine-grained vehicle emission management using intelligent transportation system data

Zhang, Shaojun; Niu, Tianlin; Wu, Ye; Zhang, K. Max; Wallington, Timothy J.; Xie, Qianyan; Wu, Ye; Xu, Honglei

doi:10.1016/j.envpol.2018.06.016

Cited by 93 publications

(38 citation statements)

References 23 publications

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“…Ma et al [23] estimated the passenger traffic CO 2 emissions in urban Guangzhou in 2010 at the sub-district level by an improved bottom-up method and found an evident variation in the individual travel-related CO 2 emissions between the workday (420 g per person) and the weekend (407 g per person). The higher daily traffic CO 2 emissions on weekdays and the peak traffic CO 2 hours were observed by Zhang et al [24] in Nanjing in August 2013. Based on the taxi GPS data on the 1st November, 2012, Li et al [25] found that the peak hour of taxi trips appeared at 10:00, 16:00 and 20:00, which showed different variations to the urban traffic demand.…”

Section: Introductionmentioning

confidence: 78%

Comparative analysis of the CO2 emissions of expressway and arterial road traffic: A case in Beijing

Zheng

Dong

et al. 2020

PLoS ONE

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Urban traffic is an important source of global CO 2 emissions. Uncovering the temporal and structural characteristics can provide scientific support to identify the variation regulation and main subjects of urban traffic CO 2 emissions. The road class is one of the most important factors influencing the urban traffic CO 2 emissions. Based on the annual traffic field monitoring work in 2014 and the localized MOVES model, this study unravels the temporal variation and structural characteristics of the urban traffic CO 2 emissions and conducts a comparative analysis of expressway (5R) and arterial road (DB), two typical classes of urban roads in Beijing. Obvious differences exist in the temporal variation characteristics of the traffic CO 2 emissions between the expressway and arterial road at the annual, week and daily scales. The annual traffic CO 2 emissions at the expressway (5R, with 47271.15 t) are more than ten times than those of the arterial road (DB, with 4139.19 t). Stronger weekly "rest effect" is observed at the expressway than the arterial road. The daily peak time and duration of the traffic CO 2 emissions between the two classes of urban roads show significant differences particular in the evening peak. The differences of the structural characteristics between the two classes of urban roads are mainly reflected on the contribution of the public and freight transportation. Passenger vehicles play a predominant role at both the two classes of urban roads. The public transportation contributed more at DB (24.76%) than 5R (5.47%), and the freight transportation contributed more at 5R (23.41%) than DB (3.49%). The results suggest that the influence of traffic CO 2 emissions on the CO 2 flux is significant at the residential and commercial mixed underlying urban areas with arterial roads (DB) but not significant at the underlying urban park area with expressway (5R) in this study. The vegetation cover in urban areas have effects on the CO 2 reduction. Increasing the design and construction of the green space along the urban roads with busy traffic flow will be an effective way to mitigate the urban traffic CO 2 emissions and build the low-carbon cities.

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Section: Introductionmentioning

confidence: 78%

Comparative analysis of the CO2 emissions of expressway and arterial road traffic: A case in Beijing

Zheng

Dong

et al. 2020

PLoS ONE

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“…The suburban and rural areas outside of the Fifth Ring Roads were scarcely covered by municipal floating cars and traffic investigations. The Ministry of Transport has established a nationwide networking to monitor intercity traffic conditions (Zhang et al, 2018). Twenty-four-hour diurnal traffic profiles including volume, speed and fleet mix were obtained from 70 highway sites in Beijing, leading to an improved understanding of traffic patterns in the outlying districts beyond the Fifth 135…”

Section: Generating Dynamic Traffic Profiles Based On Real-time Congementioning

confidence: 99%

“…Driven by the rapid development of intelligent transportation systems (ITS) in many cities during recent decades, we are able to collect real-world traffic data by multiple ITS approaches (Barth, 2003;Gately et al, 2017;Zhang et al, 2018). These ITS informed datasets are capable of capturing the dynamic traffic conditions in congested urban areas as well as actual driving 70 patterns of diesel trucks, which could contribute large fractions of NOX and PM2.5 despite small vehicle numbers (Dallmann et al, 2013;Gately et al, 2017).…”

Section: Introduction 30mentioning

confidence: 99%

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

Yang¹,

Zhang²,

Niu³

et al. 2019

Preprint

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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&#8201;400&#8201;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&#8201;% and 38&#8201;% 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&#174;, to simulate vehicular NOX concentrations at fine resolutions (10&#8201;m&#8201;&#215;&#8201;10&#8201;m in the entire municipality and 1&#8201;m&#8201;&#215;&#8201;1&#8201;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&#8201;&#956;g&#8201;m&#8722;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.

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“…Zhang et al evaluated ITS from the environmental point of view. They believed that the application of ITS data to emission management system could achieve dynamic air quality management [8]. Due to the complexity of ITS, the existing studies on ITS evaluation are mainly completed by constructing an index system and using subjective methods such as qualitative or fuzzy mathematics, with poor accuracy of the evaluation results.…”

Section: Introductionmentioning

confidence: 99%

Research on perception bias of implementation benefits of urban intelligent transportation system based on big data

Deng

2019

J Wireless Com Network

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Providing convenient and comfortable transportation services for residents is an important part of urban construction, in which the intelligent transportation system (ITS) plays an important role. This study used the big data of urban public transportation in China and the evaluation of ITS in tourism e-commerce website to compare travelers' perception of urban intelligent transportation with the objective effects of ITS and analyzed the factors that affect the perception bias of urban residents about ITS. The conclusions are as follows: (1) there are regional differences in travelers' perception of ITS. The traveler's evaluation of ITS in the cities of East China is best in China, followed by cities in the central and southwestern regions. (2) The perception bias of urban residents from ITS is generally affected by the density of road network and travel radius. The influencing factors of perception bias are obviously different between provincial capital cities and non-provincial capital cities. (3) The use of digital maps, navigation, and intelligent road conditions in the travel process can improve travelers' evaluation of ITS.

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Fine-grained vehicle emission management using intelligent transportation system data

Cited by 93 publications

References 23 publications

Comparative analysis of the CO2 emissions of expressway and arterial road traffic: A case in Beijing

Comparative analysis of the CO2 emissions of expressway and arterial road traffic: A case in Beijing

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

Research on perception bias of implementation benefits of urban intelligent transportation system based on big data

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