Optimization model for bus priority control considering carbon emissions under non-bus lane conditions

Hu, Xinghua; Chen, Xinghui; Guo, Jianpu; Dai, Gao; Zhao, Jiahao; Long, Bing; Zhang, Tingting; Chen, Shanzhi

doi:10.1016/j.jclepro.2023.136747

Cited by 10 publications

(6 citation statements)

References 20 publications

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“…To date, EBs have been widely promoted in most cities in China, but some DBs remain in operation 26 1 and 2, respectively.…”

Section: Methodsmentioning

confidence: 99%

“…To date, EBs have been widely promoted in most cities in China, but some DBs remain in operation. 26 Parameters for both types of buses are obtained through interviews with the factory's production supervisor, as shown in Tables 1 and 2, respectively. The DB primarily consists of the body, powertrain system, chassis, and drivetrain system, while the EB includes these components and traction motors, electronic controllers, and power lithium battery packs.…”

Section: Vehicle Cyclementioning

confidence: 99%

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Measuring carbon emissions throughout the lifecycles of urban conventional buses

Zhang,

Fu,

Zhou

et al. 2024

Energy Science & Engineering

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In this paper, we selected representative electric buses (EBs) and diesel buses as research cases, aiming to explore a localized method suitable for various vehicle types that can be implemented in any region to measure the carbon emissions of urban buses throughout the lifecycle. We take into account the influence of seasonal factors over the lifecycle. We fully consider the production, assembly, and recycling stages of vehicle systems. We construct a model for estimating CO2 emissions during the fuel cycle. We analyze and compare the impacts of the optimized power grid structure on the lifecycle CO2 emissions of the two types of buses. The results indicate that seasonal factors affect both bus types' energy consumption, with EBs being more sensitive. Carbon emissions primarily stem from manufacturing the body, chassis, and notably, the lithium‐ion batteries, with the latter having a pronounced impact on EBs. A decrease in coal power generation from 71% to 21% in the grid reduced EB fuel‐cycle emissions by 70.7%. Although EBs are sensitive to seasonal factors, they still have the greatest potential for carbon reduction, depending on the proportion of clean energy in the power grid and the production processes of vehicle components.

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“…To date, EBs have been widely promoted in most cities in China, but some DBs remain in operation 26 1 and 2, respectively.…”

Section: Methodsmentioning

confidence: 99%

Section: Vehicle Cyclementioning

confidence: 99%

Measuring carbon emissions throughout the lifecycles of urban conventional buses

Zhang,

Fu,

Zhou

et al. 2024

Energy Science & Engineering

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“…The day-to-day functioning of modern cities generates significant amounts of GHGs, and the transport sector is among the key emitters, accounting for 22% of the total carbon dioxide (CO 2 ) emissions, with road transport accounting for three quarters of this [1]. As an important part of urban road transportation, public transportation is essential in promoting green travel modes to reduce carbon emissions, and scholars are committed to promoting green travel for this reason [2]. The electrification of urban public transport systems can reduce the use of fossil fuels and environmental pollution and is the key to building clean and efficient urban transport, as well as the basis for future urban development.…”

Section: Introductionmentioning

confidence: 99%

Research Progress and Prospects of Public Transportation Charging Station Layout Methods

Lei,

Hu,

Zhao

et al. 2024

WEVJ

Self Cite

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Electric buses have been vigorously promoted and implemented in major countries worldwide and have generated a huge demand for charging stations. Optimizing the daily charging experience of electric buses, adapting the daily operation scheduling, improving the utilization rate of charging stations, reducing the load on the power grid, and improving the operation efficiency of electric bus line networks require the reasonable layout of the charging stations. In this study, public transportation charging station layout and siting is the research object. We summarize the progress of analysis methods from the charging station and vehicle sides; introduce related research on the planning and layout of charging stations based on optimization models, including cost analysis and siting and layout for electric bus systems; summarize the data-driven station planning and siting research; and provide an overview of the current charging demand estimation, accuracy, and charging efficiency. Finally, we address the problems of the charging demand estimation accuracy, the mismatch between the charging station layouts for electric buses, and the charging demand on a long time scale. We suggest that research be conducted on data fusion for the temporal and spatial refinement of charging demand prediction in the context of the electrification of public transportation systems and the big data of telematics.

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“…Vehicles that previously had to stop and wait according to the original signal timing control can now pass through the intersection without stopping due to the green extension. Consequently, the carbon emissions of private vehicles in the priority phase also decrease 31 . The reduction in carbon emissions can be calculated using Equation (21).…”

mentioning

confidence: 99%

“…To mitigate the adverse effects on non-priority phase vehicles caused by bus priority measures and simultaneously minimize carbon emissions from intersection traffic as much as possible, the optimal guidance speed for buses under strategy 4 should satisfy the constraint specified by Equation (34).…”

mentioning

confidence: 99%

Cooperative Control Method Combining Signal Control and Eco-Speed Guidance under Non-bus Lane Conditions in the Connected Environment

Li,

Ge,

Duan

et al. 2024

Preprint

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The transit signal priority (TSP), as an effective method to address public transport operation issues, has been widely applied. With the continuous advancement of connected technology, research on developing bus priority strategies using vehicle-to-everything (V2X) technology is gaining increasing attention. However, existing studies on TSP mainly focus on exclusive bus lanes, overlooking the adverse impact of private vehicle queues at intersections on bus priority strategies. To more effectively evaluate the optimization effect of bus priority under non-bus lane conditions, this paper proposes a cooperative control method combining signal control and eco-speed guidance in a connected environment. The objective is to maximize the reduction in delays for both buses and private vehicles at intersections and optimize carbon emission reduction. Initially, an Extended Kalman Filter (EKF) is employed to predict the arrival time of buses at intersections and the signal status. Building upon this, optimal timings for phase adjustments and the best bus trajectories are calculated using signal control and eco-speed guidance models. This approach aims to enhance bus operational efficiency while further lowering traffic carbon emissions. The effectiveness of the proposed model is verified using Zhengzhou city as a case study and compared against scenarios involving NTSP, TSP, and speed guidance. The results demonstrate that, in the absence of exclusive bus lanes, the proposed method not only ensures the stability of bus operations but also achieves significant bus priority and carbon emission benefits while mitigating the adverse impact of bus priority on private vehicles to a certain extent.

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Optimization model for bus priority control considering carbon emissions under non-bus lane conditions

Cited by 10 publications

References 20 publications

Measuring carbon emissions throughout the lifecycles of urban conventional buses

Measuring carbon emissions throughout the lifecycles of urban conventional buses

Research Progress and Prospects of Public Transportation Charging Station Layout Methods

Cooperative Control Method Combining Signal Control and Eco-Speed Guidance under Non-bus Lane Conditions in the Connected Environment

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