1. Introduction
This research presents an in-depth analysis of the pivotal role that electric buses play in sustainable urban transportation, addressing pressing global issues such as air pollution and climate change. It specifically targets a metropolitan region with a population exceeding 500,000 to serve as a case study for the broader implications of transitioning from traditional fuel-powered buses to a fully electric fleet [1-8]. The study employs a sophisticated mathematical model to quantitatively assess the ecological consequences of this shift, factoring in a myriad of considerations. These include the reduction in exhaust emissions, enhancements in air quality, decreases in carbon footprints, and the economic aspects of operating costs and the development expenses related to charging infrastructure.
On the financial front, the study conducts a thorough economic impact analysis, comparing the capital and operational expenditures associated with electric buses against those of their conventional counterparts. It also contemplates the potential for external financial assistance, such as government subsidies and infrastructure investment, which could alleviate the initial financial strain on municipal budgets. A critical component of the research is the formulation of a strategic 10-year road-map designed to assist transportation authorities in the systematic expansion and modernization of their bus fleets with electric vehicles.
This road-map is underpinned by a phased approach, starting with pilot projects to gather operational data and stakeholder feedback, followed by a gradual scale-up of the electric bus fleet. It also emphasizes the development of necessary charging infrastructure and the evolution of policies to support the widespread adoption of electric buses. The study concludes with a compelling argument for the adoption of electric buses, highlighting not only their immediate environmental advantages but also the long-term economic benefits. These include significant savings on operational and maintenance costs, which can offset the higher upfront investment. Furthermore, it underscores the broader societal and environmental impacts, such as improved public health due to cleaner air and the promotion of a greener urban image, which can attract investment and tourism. The research serves as a robust framework for decision-makers to consider the multifaceted benefits of transitioning to electric buses, advocating for a future where sustainable urban transportation is a cornerstone of city planning and policy.
2. Related Work
The academic discourse surrounding the integration of electric buses into urban transportation networks has been burgeoning, with a wealth of literature addressing various aspects of this transition. Environmental impact studies have been instrumental in quantifying the benefits of electric buses in terms of reduced emissions and improved air quality, using sophisticated modeling techniques to simulate the operation of these vehicles within existing transit systems. These studies have set the stage for understanding the ecological advantages of electric buses and have informed policy discussions on sustainability and urban planning [8-11].
Economic analyses have complemented environmental assessments by examining the financial viability of electric bus fleets. Researchers have developed models to evaluate the total cost of ownership, including purchase price, operational costs, and maintenance expenses, and have compared these to traditional diesel buses. These economic feasibility studies have often highlighted the long-term savings potential of electric buses, despite their higher initial investment costs.
Operational research has also played a significant role, with studies focusing on optimizing bus routes, scheduling, and service frequency to enhance the efficiency of electric bus operations. Data-driven models have been particularly influential, utilizing real-time transit data to predict bus arrival and departure times and to inform decision-making regarding fleet deployment and infrastructure needs.
Furthermore, literature reviews have synthesized the findings from numerous studies, providing a broad overview of the current state of knowledge regarding electric bus implementation. These reviews have underscored the multifaceted nature of the transition to electric buses, touching on planning, operational, and control aspects, and have identified key challenges and opportunities in this domain [11-18].
Finally, the role of government policies and incentives in facilitating the adoption of electric buses has been a recurring theme in the literature. Studies have shown that supportive policy environments, including subsidies, tax incentives, and forward-looking regulations, are crucial for making the transition to electric buses more attractive to transit agencies and for accelerating the uptake of this technology in cities worldwide.
3. Method
This study employs a multi-faceted approach to analyze the ecological and economic implications of transitioning from traditional fuel-powered buses to electric buses within urban transportation systems. The methodology is designed to provide a comprehensive evaluation, incorporating quantitative modeling, data analysis, and strategic planning.
3.1. Data Collection
The research begins with an extensive data collection effort, focusing on both primary and secondary sources. Primary data includes information on current bus fleet composition, operational patterns, and maintenance records from the selected metropolitan area. Secondary data encompasses environmental statistics, such as air quality indices and greenhouse gas emissions, sourced from government reports and scientific literature. Additionally, market data on electric bus costs, battery technology, and charging infrastructure is compiled from manufacturers and industry analyses.
3.2. Mathematical Modeling
A custom mathematical model is developed to simulate the environmental impact of electric buses. This model quantifies emissions reductions by comparing the carbon footprint of electric buses with that of conventional diesel buses. It incorporates variables such as fuel consumption, electricity usage, and respective emission factors. The model also estimates improvements in air quality by measuring the decrease in pollutants like nitrogen oxides and particulate matter.
For the economic analysis, a cost model is constructed to assess the financial implications of the transition. This model includes initial investment costs for electric buses, operational expenses, and the costs associated with building and maintaining charging infrastructure. The model also considers the potential for external funding, such as government subsidies, to offset initial costs.
3.3. Financial Analysis
A detailed financial analysis is conducted to evaluate the economic viability of electric buses. This includes a cost-benefit analysis that compares the total costs of ownership for electric buses with those of traditional buses over their service life. The analysis also projects potential savings in operational costs, such as reduced fuel and maintenance expenses, and considers the impact of electric buses on ridership and revenue generation.
3.4. Strategic Road-mapping
A 10-year strategic road-map is developed to guide the phased introduction of electric buses. This road-map outlines the planned scale-up of the electric bus fleet, the expansion of charging infrastructure, and the implementation of supportive policies and public awareness campaigns. The road-map is designed to be flexible, allowing for adjustments based on the outcomes of pilot projects and changing technological or market conditions.
3.5. Stakeholder Engagement
To ensure the road-map's effectiveness and relevance, a series of stakeholder engagement activities are planned. These include workshops with transportation authorities, bus operators, environmental groups, and community representatives. Feedback from these engagements is used to refine the road-map and to develop targeted strategies for overcoming potential barriers to the adoption of electric buses.
3.6. Validation and Sensitivity Analysis
The models and projections are validated using historical data and expert reviews. Sensitivity analyses are conducted to assess the robustness of the findings under various scenarios, such as changes in fuel prices, technological advancements, and shifts in government policies. This ensures that the study's recommendations are grounded in a realistic and dynamic understanding of the urban transportation landscape.
In summary, the methodology of this study is designed to be rigorous and holistic, providing a robust evidence base for decision-makers considering the transition to electric buses. By combining quantitative analysis with strategic planning and stakeholder engagement, the study aims to offer actionable insights that can inform the development of sustainable urban transportation systems.
4. Experiments
The experimental phase of this study aimed to practically assess the operational and environmental performance of electric buses within the selected metropolitan area. The experiments were designed to collect empirical data that would validate the mathematical models and financial analyses developed in the previous stages.
4.1. Experimental Setup
The experiment involved the deployment of a small fleet of electric buses in the metropolitan area, operating on predefined routes to simulate real-world conditions. The buses were equipped with data loggers to record operational parameters such as energy consumption, mileage, and battery status. Concurrently, air quality monitors were installed at various points along the bus routes to measure changes in pollution levels.
4.2. Data Collection
Data was collected over a period of six months, encompassing different seasons to account for varying weather conditions and their impact on electric bus performance. The data collected included:
• Electricity Consumption: Daily electricity usage per bus, recorded in kilowatt-hours (kWh).
• Mileage: Total miles driven per day by each bus.
• Maintenance Records: Details of maintenance activities and associated costs.
• Air Quality Data: Levels of pollutants like NOx, PM2.5, and PM10 at different monitoring points.
Table 1: Average Daily Electricity Consumption by Electric Buses
Bus ID | Average Daily Consumption (kWh) |
E1 | 120 |
E2 | 130 |
E3 | 125 |
E4 | 115 |
E5 | 130 |
Table 2: Air Quality Monitoring Data
Monitoring Point | NOx (ppb) | PM2.5 (µg/m³) | PM10 (µg/m³) |
Point A | 45 | 18 | 25 |
Point B | 50 | 20 | 30 |
Point C | 48 | 19 | 27 |
4.3. Results Analysis
As shown in Table 1 and 2, electricity Consumption: The average daily electricity consumption varied slightly among the buses, with an overall average of approximately 125 kWh per day. This data aligns with the model predictions, confirming the energy efficiency of electric buses.
Mileage: The fleet achieved an average daily mileage of about 200 miles, demonstrating the practical range capabilities of the electric buses under typical urban driving conditions.
Maintenance: The maintenance records indicated lower frequency and cost of service compared to conventional buses, primarily due to fewer mechanical parts in electric buses. This data supports the economic analysis, highlighting the potential for cost savings.
Air Quality: A noticeable reduction in NOx and particulate matter was observed at the monitoring points, especially during the periods of high electric bus activity. This finding validates the environmental benefits predicted by the study's models.
5. Conclusion
The experimental phase provided valuable empirical evidence supporting the environmental and economic benefits of electric buses. The data collected confirmed the model's predictions regarding energy consumption and emissions reduction, and the operational data validated the cost-effectiveness analysis. These results reinforce the feasibility and advantages of transitioning to electric buses in urban transportation systems.
References
[1]. Shan L, Wang W, Lv K, et al. Class-Incremental Semantic Segmentation of Aerial Images via Pixel-Level Feature Generation and Task-Wise Distillation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1-17.
[2]. Shan L, Wang W. DenseNet-Based Land Cover Classification Network with Deep Fusion[J]. IEEE Geoscience and Remote Sensing Letters, 2021, 19: 1-5.
[3]. Shan L, Wang W. MBNet: A Multi-Resolution Branch Network for Semantic Segmentation of Ultra-High Resolution Images[C]//ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022: 2589-2593.
[4]. Shan L, Wang W, Lv K, et al. Class-incremental Learning for Semantic Segmentation in Aerial Imagery via Distillation in All Aspects[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021.
[5]. Li M, Shan L, Li X, et al. Global-local attention network for semantic segmentation in aerial images[C]//2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 2021: 5704-5711.
[6]. Shan L, Li X, Wang W. Decouple the High-Frequency and Low-Frequency Information of Images for Semantic Segmentation[C]//ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021: 1805-1809.
[7]. Shan L, Li M, Li X, et al. UHRSNet: A Semantic Segmentation Network Specifically for Ultra-High-Resolution Images[C]//2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 2021: 1460-1466.
[8]. Shan L, Wang W, Lv K, et al. Boosting Semantic Segmentation of Aerial Images via Decoupled and Multi-level Compaction and Dispersion[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023.
[9]. Wu W, Zhao Y, Li Z, et al. Continual Learning for Image Segmentation with Dynamic Query[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2023.
[10]. Shan L, Zhou W, Zhao G. Incremental Few Shot Semantic Segmentation via Class-agnostic Mask Proposal and Language-driven Classifier[C]//Proceedings of the 31st ACM International Conference on Multimedia. 2023: 8561-8570.
[11]. Shan L, Zhao G, Xie J, et al. A Data-Related Patch Proposal for Semantic Segmentation of Aerial Images[J]. IEEE Geoscience and Remote Sensing Letters, 2023, 20: 1-5.
[12]. Zhao G, Shan L, Wang W. End-to-End Remote Sensing Change Detection of Unregistered Bi-temporal Images for Natural Disasters[C]//International Conference on Artificial Neural Networks. Cham: Springer Nature Switzerland, 2023: 259-270.
[13]. Li X, Shan L, Li M, et al. Energy Minimum Regularization in Continual Learning[C]//2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 2021: 6404-6409.
[14]. Ding X, Shan L, Zhao G, et al. The Binary Quantized Neural Network for Dense Prediction via Specially Designed Upsampling and Attention[J]. arXiv preprint arXiv:2405.17776, 2024.
[15]. Shan L, Wang W, Lv K, et al. Edge-guided and Class-balanced Active Learning for Semantic Segmentation of Aerial Images[J]. arXiv preprint arXiv:2405.18078, 2024.
[16]. Zhao L S W Z G. Boosting General Trimap-free Matting in the Real-World Image[J]. arXiv preprint arXiv:2405.17916, 2024.
[17]. Shan L, Zhou W, Li W, et al. Lifelong Learning and Selective Forgetting via Contrastive Strategy[J]. arXiv preprint arXiv:2405.18663, 2024.
[18]. Shan L, Zhou W, Li W, et al. Organizing Background to Explore Latent Classes for Incremental Few-shot Semantic Segmentation[J]. arXiv preprint arXiv:2405.19568, 2024.
Cite this article
Pan,Y. (2025). Analysis of the Ecological and Economic Benefits of Electric Buses in Sustainable Urban Transportation. Applied and Computational Engineering,130,146-151.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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References
[1]. Shan L, Wang W, Lv K, et al. Class-Incremental Semantic Segmentation of Aerial Images via Pixel-Level Feature Generation and Task-Wise Distillation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1-17.
[2]. Shan L, Wang W. DenseNet-Based Land Cover Classification Network with Deep Fusion[J]. IEEE Geoscience and Remote Sensing Letters, 2021, 19: 1-5.
[3]. Shan L, Wang W. MBNet: A Multi-Resolution Branch Network for Semantic Segmentation of Ultra-High Resolution Images[C]//ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022: 2589-2593.
[4]. Shan L, Wang W, Lv K, et al. Class-incremental Learning for Semantic Segmentation in Aerial Imagery via Distillation in All Aspects[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021.
[5]. Li M, Shan L, Li X, et al. Global-local attention network for semantic segmentation in aerial images[C]//2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 2021: 5704-5711.
[6]. Shan L, Li X, Wang W. Decouple the High-Frequency and Low-Frequency Information of Images for Semantic Segmentation[C]//ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021: 1805-1809.
[7]. Shan L, Li M, Li X, et al. UHRSNet: A Semantic Segmentation Network Specifically for Ultra-High-Resolution Images[C]//2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 2021: 1460-1466.
[8]. Shan L, Wang W, Lv K, et al. Boosting Semantic Segmentation of Aerial Images via Decoupled and Multi-level Compaction and Dispersion[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023.
[9]. Wu W, Zhao Y, Li Z, et al. Continual Learning for Image Segmentation with Dynamic Query[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2023.
[10]. Shan L, Zhou W, Zhao G. Incremental Few Shot Semantic Segmentation via Class-agnostic Mask Proposal and Language-driven Classifier[C]//Proceedings of the 31st ACM International Conference on Multimedia. 2023: 8561-8570.
[11]. Shan L, Zhao G, Xie J, et al. A Data-Related Patch Proposal for Semantic Segmentation of Aerial Images[J]. IEEE Geoscience and Remote Sensing Letters, 2023, 20: 1-5.
[12]. Zhao G, Shan L, Wang W. End-to-End Remote Sensing Change Detection of Unregistered Bi-temporal Images for Natural Disasters[C]//International Conference on Artificial Neural Networks. Cham: Springer Nature Switzerland, 2023: 259-270.
[13]. Li X, Shan L, Li M, et al. Energy Minimum Regularization in Continual Learning[C]//2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 2021: 6404-6409.
[14]. Ding X, Shan L, Zhao G, et al. The Binary Quantized Neural Network for Dense Prediction via Specially Designed Upsampling and Attention[J]. arXiv preprint arXiv:2405.17776, 2024.
[15]. Shan L, Wang W, Lv K, et al. Edge-guided and Class-balanced Active Learning for Semantic Segmentation of Aerial Images[J]. arXiv preprint arXiv:2405.18078, 2024.
[16]. Zhao L S W Z G. Boosting General Trimap-free Matting in the Real-World Image[J]. arXiv preprint arXiv:2405.17916, 2024.
[17]. Shan L, Zhou W, Li W, et al. Lifelong Learning and Selective Forgetting via Contrastive Strategy[J]. arXiv preprint arXiv:2405.18663, 2024.
[18]. Shan L, Zhou W, Li W, et al. Organizing Background to Explore Latent Classes for Incremental Few-shot Semantic Segmentation[J]. arXiv preprint arXiv:2405.19568, 2024.