Congestion Control for Mixed-Mode Traffic with Emission Cost

This paper presents two models to investigate the traffic assignment problem. In the two models, the emission cost for gasoline vehicles (GVs) is considered. The credit schemes are considered in the constraint of the models. The operation costs for battery electric vehicles (BEVs) and GVs are also studied. Particularly, the constraints related to the credit schemes can be utilized to adjust the number of GVs and to promote growth of the number of BEVs, which is a novel idea that was not studied. Preliminary numerical experiments demonstrate that the models are effective and the extended distance limit of BEVs can raise the volume of BEVs under the condition that the unit traffic cost of BEVs is lower than GVs. Therefore, it is feasible to control the quantity of GVs by adjusting the total credit schemes, and it is viable to reduce the emission by enlarging the number of BEVs’ users.

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A Greedy Path-Based Algorithm for Traffic Assignment

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198118774236 ◽

2018 ◽

Vol 2672 (48) ◽

pp. 36-44 ◽

Cited By ~ 9

Author(s):

Jun Xie ◽

Yu (Marco) Nie ◽

Xiaobo Liu

Keyword(s):

Assignment Problem ◽

Numerical Experiments ◽

Traffic Assignment ◽

User Equilibrium ◽

Greedy Method ◽

Wide Margin ◽

Decomposition Scheme ◽

Traffic Assignment Problem

This paper presents a new path-based algorithm for the static user equilibrium traffic assignment problem. Path-based algorithms are generally considered less efficient than bush-based counterparts, such as Algorithm B, traffic assignment by paired alternative segments (TAPAS), and iTAPAS, an improved version of TAPAS, because explicitly storing and manipulating paths appears wasteful. However, our numerical experiments indicate that the proposed path-based algorithm can outperform TAPAS or iTAPAS by a wide margin. The proposed algorithm, sharing the same Gauss-Seidel decomposition scheme with existing path-based algorithms, delivered a surprising performance, most likely due to its two main features. First, it adopts a greedy method to solve the restricted subproblem defined on each origin–destination (O-D) pair. Second, instead of sequentially visiting every O-D pair in each iteration, it introduces an intelligent scheme to determine which O-D pairs need more or less work. The proposed algorithm is also more straightforward to implement than bush-based algorithms.

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Environmental Implications of the National Power Roadmap with Policy Directives for Battery Electric Vehicles (BEVs)

Sustainability ◽

10.3390/su11236657 ◽

2019 ◽

Vol 11 (23) ◽

pp. 6657 ◽

Cited By ~ 1

Author(s):

Solhee Kim ◽

Rylie E. O. Pelton ◽

Timothy M. Smith ◽

Jimin Lee ◽

Jeongbae Jeon ◽

...

Keyword(s):

Environmental Impact ◽

Electric Vehicles ◽

Environmental Performance ◽

Power Grid ◽

Environmental Index ◽

Environmental Implications ◽

Battery Electric Vehicles ◽

Trade Offs ◽

Gasoline Vehicles ◽

National Power

The environmental impact of battery electric vehicles (BEVs) largely depends on the environmental profile of the national electric power grid that enables their operation. The purpose of this study is to analyze the environmental performance of BEV usage in Korea considering the changes and trajectory of the national power roadmap. We examined the environmental performance using a weighted environmental index, considering eight impact categories. The results showed that the weighted environmental impact of Korea’s national power grid supply would increase overall by 66% from 2015 to 2029 using the plan laid out by the 7th Power Roadmap, and by only 33% from 2017 to 2031 using the 8th Power Roadmap plan. This change reflects the substantial amount of renewables in the more recent power mix plan. In 2016, BEV usage in Korea resulted in emissions reductions of about 37% compared with diesel passenger vehicles, and 41% compared with gasoline vehicles per kilometer driven (100 g CO2e/km versus 158 g and 170 g CO2e/km, respectively) related to transportation sector. By 2030, BEV usage in Korea is expected to achieve a greater emissions reduction of about 53% compared with diesel vehicles and 56% compared with gasoline vehicles. However, trade-offs are also expected because of increased particulate matter (PM) pollution, which we anticipate to increase by 84% compared with 2016 conditions. Despite these projected increases in PM emissions, increased BEV usage in Korea is expected to result in important global and local benefits through reductions of climate-changing greenhouse gas (GHG) emissions.

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Regional Emissions Analysis of Light-Duty Battery Electric Vehicles

Atmosphere ◽

10.3390/atmos12111482 ◽

2021 ◽

Vol 12 (11) ◽

pp. 1482

Author(s):

Andrew Burnham ◽

Zifeng Lu ◽

Michael Wang ◽

Amgad Elgowainy

Keyword(s):

Electric Vehicles ◽

State Level ◽

Air Pollutant ◽

Regional Variability ◽

Emission Data ◽

Battery Electric Vehicles ◽

Light Duty ◽

Vehicle Operation ◽

Gasoline Vehicles ◽

Light Duty Vehicle

Light-duty battery electric vehicles (BEVs) can reduce both greenhouse gas (GHG) and criteria air pollutant (CAPs) emissions, when compared to gasoline vehicles. However, research has found that while today’s BEVs typically reduce GHGs, they can increase certain CAPs, though with significant regional variability based on the electric grid mix. In addition, the environmental performance of electric and gasoline vehicles is not static, as key factors driving emissions have undergone significant changes recently and are expected to continue to evolve. In this study, we perform a cradle-to-grave life cycle analysis using state-level generation mix and vehicle operation emission data. We generated state-level emission factors using a projection from 2020 to 2050 for three light-duty vehicle types. We found that BEVs currently provide GHG benefits in nearly every state, with the median state’s benefit being between approximately 50% to 60% lower than gasoline counterparts. However, gasoline vehicles currently have lower total NOx, urban NOx, total PM2.5, and urban PM2.5 in 33%; 15%; 70%; and 10% of states, respectively. BEV emissions will decrease in 2050 due to a cleaner grid, but the relative benefits when compared to gasoline vehicles do not change significantly, as gasoline vehicles are also improving over this time.

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