Integrating a simplified emission estimation model and mesoscopic dynamic traffic simulator to efficiently evaluate emission impacts of traffic management strategies

Zhou, Xuesong; Tanvir, Shams; Lei, Hao; Taylor, Jeffrey; Liu, Bin; Rouphail, Nagui M.; Frey, H. Christopher

doi:10.1016/j.trd.2015.04.013

Cited by 87 publications

(38 citation statements)

References 17 publications

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“…The accuracy of the VSP-based modeling approach relies on how VSP bins are defined. Like many of other studies related to vehicle emissions (Carrera, 2015;Zhou et al, 2015;Hoover et al, 2015;Li et al, 2015a), the cut points of the VSP by MOVES 2010b were adopted in this study. The operating mode ID was categorized to describe vehicle behaviors based on the instantaneous speed and VSP characteristics.…”

Section: Emission Estimation Methodsmentioning

confidence: 99%

Vehicle emission implications of drivers’ smart advisory system for traffic operations in work zones

Qiao

2016

Journal of the Air & Waste Management Association

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Wireless communication systems have been broadly applied in various complicated traffic operations to improve mobility and safety on roads, which may raise a concern about the implication of the new technology on vehicle emissions. This paper explores how the wireless communication systems improve drivers' driving behaviors and its contributions to the emission reduction, in terms of Operating Mode (OpMode) IDs distribution used in emission estimation. A simulated work zone with completed traffic operation was selected as a test bed. Sixty subjects were recruited for the tests, whose demographic distribution was based on the Census data in Houston, Texas. A scene of a pedestrian's crossing in the work zone was designed for the driving test. Meanwhile, a wireless communication system called Drivers Smart Advisory System (DSAS) was proposed and introduced in the driving simulation, which provided drivers with warning messages in the work zone. Two scenarios were designed for a leading vehicle as well as for a following vehicle driving through the work zone, which included a base test without any wireless communication systems, and a driving test with the trigger of the DSAS. Subjects' driving behaviors in the simulation were recorded to evaluate safety and estimate the vehicle emission using the Environmental Protection Agency (EPA) released emission model MOVES. The correlation between drivers' driving behavior and the distribution of the OpMode ID during each scenario was investigated. Results show that the DSAS was able to induce drivers to accelerate smoothly, keep longer headway distance and stop earlier for a hazardous situation in the work zone, which driving behaviors result in statistically significant reduction in vehicle emissions for almost all studied air pollutants (pvalues range from 4.10E-51 to 2.18E-03). The emission reduction was achieved by the switching the distribution of the OpMode IDs from higher emission zones to lower emission zones.Implications: Transportation section is a significant source of greenhouse gas emissions. Many studies demonstrate that the wireless communication system dedicated for safety and mobility issues may contribute to the induction in vehicle emissions through changing driving behaviors. An insight into the correlation between the driving behaviors and the distribution of Operating Mode (OpMode) IDs is essential to enhance the emission reduction. The result of this study shows that with a Drivers Smart Advisory System (DSAS) drivers accelerated smoothly and stopped earlier for a hazardous situation, which induce the switch of the OpMode IDs from high emission zones to lower emission zones. PAPER HISTORY

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Section: Emission Estimation Methodsmentioning

confidence: 99%

Vehicle emission implications of drivers’ smart advisory system for traffic operations in work zones

Qiao

2016

Journal of the Air & Waste Management Association

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“…traffic volume, speed, fleet composition) displaying the traffic flow characteristics of each road (Wang et al, 2008). With the increase of research, some higher-resolution vehicle emission inventories in Chinese cities were established based on bottom-up methodology (Wang et al, 2008;Huo et al, 2009;Wang and Xie, 2009;Zhou et al, 2015). However, most of those inventories had some limitations regarding reflection on real-time variation of vehicle emissions on each road due to the lack of collection methodology of real-time traffic data.…”

Section: Introductionmentioning

confidence: 99%

Development of a vehicle emission inventory with high temporal–spatial resolution based on NRT traffic data and its impact on air pollution in Beijing – Part 1: Development and evaluation of vehicle emission inventory

Jing¹,

Wu²,

Mao³

et al. 2016

Atmos. Chem. Phys.

121

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Abstract. This paper presents a bottom-up methodology based on the local emission factors, complemented with the widely used emission factors of Computer Programme to Calculate Emissions from Road Transport (COPERT) model and near-real-time traffic data on road segments to develop a vehicle emission inventory with high temporal-spatial resolution (HTSVE) for the Beijing urban area. To simulate real-world vehicle emissions accurately, the road has been divided into segments according to the driving cycle (traffic speed) on this road segment. The results show that the vehicle emissions of NO x , CO, HC and PM were 10.54 × 10 4 , 42.51 × 10 4 and 2.13 × 10 4 and 0.41 × 10 4 Mg respectively. The vehicle emissions and fuel consumption estimated by the model were compared with the China Vehicle Emission Control Annual Report and fuel sales thereafter. The gridbased emissions were also compared with the vehicular emission inventory developed by the macro-scale approach. This method indicates that the bottom-up approach better estimates the levels and spatial distribution of vehicle emissions than the macro-scale method, which relies on more information. Based on the results of this study, improved air quality simulation and the contribution of vehicle emissions to ambient pollutant concentration in Beijing have been investigated in a companion paper (He et al., 2016).

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“…The same is valid when traffic intensity data is applied in a local scale, using traffic simulators such as VISUM, VISSIM, TREM and VADIS [7]. Similarly, the use of complex systems, such as results of empirical research and vehicle traffic modelling, including neural networks [24] or Newell kinematic waveforms for vehicle traffic modelling [35], do not provide opportunities for identifying the intensity of car use, taking into account the different vehicle classification criteria, due to the requirements of software for emission estimation, e.g. COPERT.…”

Section: Introductionmentioning

confidence: 89%

“…Numerous publications, including both scientific papers [6,7,18,24,25,27,31,35] and methodological manuals, as well as reports from pollutant emission inventories [1, 8, 14-17, 19-21, 23, 28, 30, 32], do not contain any models of intensity of using motor vehicles. In case of quantifying the emission of pollutants at local scale, as e.g.…”

Section: Introductionmentioning

confidence: 99%

Model of the structure of motor vehicles for the criterion of the technical level on account of pollutant emission

Chłopek

Bebkiewicz

2017

EiN

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Integrating a simplified emission estimation model and mesoscopic dynamic traffic simulator to efficiently evaluate emission impacts of traffic management strategies

Cited by 87 publications

References 17 publications

Vehicle emission implications of drivers’ smart advisory system for traffic operations in work zones

Vehicle emission implications of drivers’ smart advisory system for traffic operations in work zones

Development of a vehicle emission inventory with high temporal–spatial resolution based on NRT traffic data and its impact on air pollution in Beijing – Part 1: Development and evaluation of vehicle emission inventory

Model of the structure of motor vehicles for the criterion of the technical level on account of pollutant emission

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