Evaluating the cumulative impacts of a long range regional transportation plan: Particulate matter exposure, greenhouse gas emissions, and transportation system performance

Tayarani, Mohammad; Nadafianshahamabadi, Razieh; Poorfakhraei, Amir; Rowangould, Gregory

doi:10.1016/j.trd.2018.05.014

Cited by 12 publications

(4 citation statements)

References 26 publications

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“…Transportation sector is the main source of the greenhouse gases (27%) and CO 2 (32%) (Fan et al, 2018) and many other criteria air pollutants in the U.S (US EPA, 2019). Worries over the pollution from fossil fuels in the transportation sector and concerns about cleaner production strategies have instigated the tendency to use Plug-in Electric Vehicles (PEVs) instead of the conventional internal combustion engines (Li et al, 2019;Tayarani et al, 2018bTayarani et al, , 2018a. It is estimated that at least 10 percent of the US fleet will be replaced by electric vehicles by 2020 and their market share could reach 50 percent by 2050 (Mukherjee and Gupta, 2017).…”

Section: Background and Motivationmentioning

confidence: 99%

Charging demand of Plug-in Electric Vehicles: Forecasting travel behavior based on a novel Rough Artificial Neural Network approach

Jahangir

Tayarani

Ahmadian

et al. 2019

Journal of Cleaner Production

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151

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The market penetration of Plug-in Electric Vehicles (PEVs) is escalating due to their energy saving and environmental benefits. In order to address PEVs impact on the electric networks, the aggregators need to accurately predict the PEV Travel Behavior (PEV-TB) since the addition of a great number of PEVs to the current distribution network poses serious challenges to the power system. Forecasting PEV-TB is critical because of the high degree of uncertainties in drivers' behavior. Existing studies mostly simplified the PEV-TB by mapping travel behavior from conventional vehicles. This could cause bias in power estimation considering the differences in PEV-TB because of charging pattern which consequently could bungle economic analysis of aggregators. In this study, to forecast PEV-TB an artificial intelligence-based method -feedforward and recurrent Artificial Neural Networks (ANN) with Levenberg Marquardt (LM) training method based on Rough structure -is developed. The method is based on historical data including arrival time, departure time and trip length. In this study, the correlation among arrival time, departure time and trip length is also considered. The forecasted PEV-TB is then compared with Monte Carlo Simulation (MCS) which is the main benchmarking method in this field. The results comparison

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Section: Background and Motivationmentioning

confidence: 99%

Charging demand of Plug-in Electric Vehicles: Forecasting travel behavior based on a novel Rough Artificial Neural Network approach

Jahangir

Tayarani

Ahmadian

et al. 2019

Journal of Cleaner Production

Self Cite

151

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“…The rapid growth in urban mobility and the corresponding increase in energy use, greenhouse gas emissions, (GHG), air quality [1], and their externalities have driven more people to switch from their conventional cars to cleaner fleets, electric cars. The population of plug-in electric vehicles (PEVs) increased drastically over the past decade [2].…”

Section: Introductionmentioning

confidence: 99%

Optimal Charging of Plug-In Electric Vehicle: Considering Travel Behavior Uncertainties and Battery Degradation

et al. 2019

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The negative environmental impacts of using fossil fuel-powered vehicles underlined the need for inventing an alternative eco-friendly transportation fleet. Plug-in electrical vehicles (PEVs) are introduced to cut the continuing increase in energy use and carbon emission of the urban mobility. However, the increased demand for mobility, and therefore energy, can create constraints on the power network which can reduce the benefits of electrification as a certain and reliable source. Thus, the rise in the use of electric vehicles needs electric grids to be able to feed the increased energy demand while the current infrastructure supports it. In this paper, we introduce a methodological framework for scheduling smart PEVs charging by considering the uncertainties and battery degradation. This framework includes an economic model for charging and discharging of PEVs which has been implemented in a 21-node sample distribution network with a wind turbine as a distributed generation (DG) unit. Our proposed approach indicates that the optimal charging of the PEVs has a high impact on the distribution network operation, particularly under the high market penetration of PEVs. Thus, the smart grid to vehicle (G2V) charging mode is a potential solution to maximize the PEV’s owner profit, while considering the battery degradation cost of the PEVs. The simulation result indicates that smart charging effectuation is economical.

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“…In addition to short-term impacts on travel behavior and traffic conditions, congestion pricing would cause long-term effects such as household relocation decisions since travel cost is one the major factors affecting residential location choice [61]. According to the literature, any change in the cost of travel, directly and indirectly, affects both short-and long-term choices with regard to location choices [62]. In particular, congestion pricing that directly increases the cost of travel affects travel behavior in the short term and affects land use patterns in the long term [63].…”

Section: Congestion Pricing Studiesmentioning

confidence: 99%

Cordon Pricing, Daily Activity Pattern, and Exposure to Traffic-Related Air Pollution: A Case Study of New York City

et al. 2021

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Road pricing is advocated as an effective travel demand management strategy to alleviate traffic congestion and improve environmental conditions. This paper analyzes the impacts of cordon pricing on the population’s daily activity pattern and their exposure to particulate matter by integrating activity-based models with air quality and exposure models in the case of New York City. To estimate changes in public exposure under cordon pricing scenarios, we take a sample of employees and study their mobility behavior during the day, which is mainly attributed to the location of the work and the time spent at work. The selection of employees and their exposure during the duration of their work is due to the unavailability of exact activity patterns for each individual. We show that the Central Business District (CBD) experiences a high concentration of PM2.5 emissions. Results indicate that implementing cordon pricing scenarios can reduce the population-weighted mean of exposure to PM2.5 emissions by 7% to 13% for our sample and, in particular, by 22% to 28% for those who work in the CBD. Furthermore, using an experimental model and assuming constant conditions, we point out the positive influence on indoor exposure for two locations inside and outside the CBD in response to cordon pricing. Considering the correlation between long-term exposure to fine particulate matter and the risks of developing cardiovascular disease and lung cancer, our findings suggest that improved public health conditions could be provided by implementing cordon pricing in the New York City CBD.

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Evaluating the cumulative impacts of a long range regional transportation plan: Particulate matter exposure, greenhouse gas emissions, and transportation system performance

Cited by 12 publications

References 26 publications

Charging demand of Plug-in Electric Vehicles: Forecasting travel behavior based on a novel Rough Artificial Neural Network approach

Charging demand of Plug-in Electric Vehicles: Forecasting travel behavior based on a novel Rough Artificial Neural Network approach

Optimal Charging of Plug-In Electric Vehicle: Considering Travel Behavior Uncertainties and Battery Degradation

Cordon Pricing, Daily Activity Pattern, and Exposure to Traffic-Related Air Pollution: A Case Study of New York City

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