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Balancing the GHG emissions and operational costs for a mixed fleet of electric buses and diesel buses
Abstract
Making the urban buses electric is regarded as a major strategy to reduce greenhouse gas (GHG) emissions and environmental impacts of fossil fuels. In practice, not all diesel buses (DBs) are replaced by electric buses (EBs) because of budget constraint. This paper investigates the balance of the deployment problem for a mixed fleet with DBs and EBs in the sense of total GHG emissions and operational costs by incorporating the effect of the spatial–temporal passenger flows. The balance strategy of fleet deployment is defined the Pareto optimal allocation of EBs among bus lines to minimize simultaneously the total operational cost and GHG emissions. A real-world urban bus system of Liuzhou City in China is conducted. We find that the bus lines located in the downtown with higher passenger loading would prefer to adopt EBs at the peak hours, and most DBs are allocated to the bus lines with long travel distance at off-peak hours in the suburb. Therefore, the reduced emission by adopting EBs mainly concentrates on the center of the city, and more produced emissions of DBs are distributed far away from the downtown. When all DBs replaced by EBs, the upper bound of the carbon emission reduction ratio is 77.04%, which reduces from 207.15 tons to 47.56 tons per day.
... Comprehensive approaches to improving the environmental indicators of the use of motor vehicles are considered in works [14][15][16][17]. Garcia, et al. [14] investigate ways to reduce CO 2 emissions using the example of the 10 largest bus routes in the city of Valencia. ...
... The work by Shao, et al. [15] investigates the problem of obtaining a balance during the formation of a mixed fleet of diesel and electric buses, under the condition of minimizing the total emissions of greenhouse gases and operating costs. The simulation was performed for the bus network of the city of Liuzhou in China. ...
Purpose. To propose a modern methodological approach to determine the energy efficiency of passenger transportation by city buses by establishing the relationship between fuel consumption and the number of stops on the route, as well as an environmental assessment of the introduction of an express mode of bus traffic in the conditions of a modern metropolis. Methodology. The fuel balance equation of the vehicle was used to build a model for researching the energy resource efficiency of buses in different driving modes. Determining the criteria and limitations that determine the effectiveness of the express mode of bus traffic was carried out by methods of system analysis. Information about the number of stops (where passenger exchange takes place) and additional dynamic loads which are related to the level of occupancy of the bus cabin were used as input data for modelling. These indicators were determined on the basis of a survey of passenger flows. The values of the angles of the lateral-longitudinal slope of the road and the distances of the sections between the stops were determined with the help of the Internet resources Google Earth Pro and Google maps, respectively. The number of additional stops at traffic lights was calculated as a weighted average value according to the Bernoulli distribution. Elements of functional analysis were used to justify the introduction of the combined mode of movement in the considered example. The economic evaluation was carried out in accordance with the Directive of the European Parliament and the Council of the EU 2009/33/EU. Findings. In the conducted studies, an ecological and economic evaluation of the introduction of an express mode of bus traffic in the conditions of a modern metropolis was provided. The results of the conducted research made it possible to determine the dependence of the energy resource efficiency of bus operation in different driving modes. Increasing the energy efficiency of transportation is achieved through the introduction of more productive and less expensive modes of bus traffic on city routes. It has been proven that the most effective one is the combined mode using regular and express connections. Originality. The authors believe that one of the effective measures to reduce the environmental consequences of the operation of urban public automobile transport is to increase the energy efficiency of transportation. This conclusion is based on the fact that one of the main quantitative indicators of the operation of vehicles is fuel consumption, which directly affects the mass of pollutant emissions and depends on the bus driving mode. Practical value. The proposed methodological approach is a universal algorithm that is proposed to be used by interested parties to assess the possibilities of reducing the negative impact of transport on the environment. The use of the developed approach in practice allows transport departments of city halls and akimats of megacities together with specialists of transport companies (developers of public transport routes) to reduce emissions of pollutants into the atmospheric air, achieving a minimal negative impact on the environment.
... It thus plays an indispensable role in reducing transport congestion and improving the city environment. The study of bus operation optimization remains one of the most popular topics in the transportation field, including fleet allocation, network design, passenger behavior analysis, and timetable optimization (Ceder & Wilson, 1986;Ibarra-Rojas et al., 2015;Shao et al., 2022). But in recent years, the urban public transportation system has become one of the main mediums for the spread of the epidemic because of dynamic passenger boarding and alighting and uncertain in-vehicle movement (Borjigin et al., 2023;Jardim et al., 2022;Qian & Ukkusuri, 2021). ...
... Note that it is hard to make a certain choice of boarding and/or alighting stops for a passenger. We adopt the Logit model (Shao et al., 2022;Tan et al., 2020) to capture the choice uncertainty of passengers under a sustainable service network. Namely, the number of passengers between and at time choosing a feasible boarding-alighting pair , ∈ ( , ), can be calculated bȳ ...
The excessive traffic congestion in vehicles lowers the service quality of urban bus system, reduces the social distance of bus passengers, and thus, increases the spread speed of epidemics, such as coronavirus disease. In the post-pandemic era, it is one of the main concerns for the transportation agency to provide a sustainable urban bus service to balance the travel convenience in accessibility and the travel safety in social distance for bus passengers, which essentially reduces the in-vehicle passenger congestion or smooths the boarding-alighting unbalance of passengers. Incorporating the route choice behavior of passengers, this paper proposes a sustainable service network design strategy by selecting one subset of the stops to maximize the total passenger-distance (person × kilometers) with exogenously given loading factor and stop-spacing level, which can be captured by constrained non-linear programming model. The loading factor directly determines the in-vehicle social distance, and the stop-spacing level can efficiently reduce the ridership with short journey distance. Therefore, the sustainable service network design can be used to help the government minimize the spread of the virus while guaranteeing the service quality of transport patterns in the post-pandemic era. A real-world case study is adopted to illustrate the validity of the proposed scheme and model.
... With the recent advancements in transport and the reduction in time and cost for travelling, the rate of travel has increased noticeably [1][2][3]. This is reflected in various developments, such as an increase in the number of businesses in the European Union's (EU's) transport Open Access ...
Background
Many countries agreed to reduce CO2 emissions to limit global warming under the terms of the Paris Agreement. In Europe, this agreement is supported by the climate targets introduced under the European Green Deal and the Fit for 55 package. Although Germany has made substantial progress in reducing emissions across various sectors, the transport sector remains a notable exception, showing little improvement. It is therefore essential to reevaluate the transport sector to strengthen its contribution to achieving the emission reduction targets. The aim of this study is to identify and propose strategies for shifting from fossil fuel-based transport to a more sustainable mode centred on alternative fuels. To investigate the potential pathways, an integrated approach is developed using a SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis and multi-criteria decision analysis (MCDA).
Results
A two-step survey was used to collect data from different stakeholders in order to derive the key factors for the implementation of alternative fuels and devise transition strategies. The findings show that reducing GHG emissions, resource competition, and the impacts of environmental regulations are the most important factors for evaluating the transition strategies. On the other hand, reducing the competitiveness of fossil fuels through increased prices, as well as technical and infrastructural support, are the most promising strategies.
Conclusions
The sustainable transition in the transport sector is fundamentally driven by the use of renewable fuel alternatives as sustainable energy carriers to replace fossil fuels. The use and deployment of renewable fuel alternatives will play the most significant role in the defossilization of the transport sector, on course to achieve a 55% reduction by 2030 and reaching climate-neutrality by 2050. However, identification of the proper transition strategies in the phase-out of fossil fuels and their replacement with renewable fuel alternatives necessitates a comprehensive evaluation framework. This work contributes to this by developing a holistic evaluation framework, enabling the incorporation of multiple stakeholders within the identification and evaluation of the transition strategies. While several strategies are identified, stakeholders agree that reducing the competitiveness of fossil fuels through increased prices and lower subsidies would be the best strategy.
... The analysis for economic viability is discussed in the following Table 13. 25,26 The above 58 Having an idea of the fuel cost for running EVs makes managing the transit system easy. ...
Balancing the growing demand for mobility on congested city roads presents a significant challenge in urban areas. One potential solution is a shift towards electric‐based mass transit systems, moving away from personal vehicles. This shift would alleviate traffic congestion and reduce emissions caused by traditional transit systems. However, implementing large‐scale e‐mobility in developing countries has several challenges, including an unreliable electricity grid and limited power sources. Additionally, passenger comfort and system reliability are also raised concerns. Accomplishing sustainable e‐mobility in an urban scenario needs cost‐effective generation with controlled emissions. This study primarily computes power generation costs and emissions from plants like coal‐dominated and renewable energy resources. These two generation plant scenarios would help to understand the exact numbers of generation cost, emission reduction, and health cost reduction in case a nation plans to shift towards green energy. The analysis's findings demonstrate that using solar‐powered energy sources can reduce carbon pollution to 124.96 g/km while releasing relatively few additional pollutants. Furthermore, during 10 years from 2020 to 2030, the generating cost of e‐buses powered by solar PVs is a meager 0.93 million. The anticipated yearly costs for energy generation and health care are 0.875 million and 0.2 million in Indian rupees, respectively. All of these factors are predicted to have substantially reduced, making it clear that moving towards renewable resources in the future could lower overall health expenses and make energy more affordable.
... For example, the operational and economic impacts of EV charging demands on power systems were studied in [17] and [18], but the type of EVs was not identified in the case study. Based on the duty cycle and weight of the EV, the EVs can be classified by light-duty EVs (LDEVs), medium-and heavy-duty EVs (MHDEVs) [27], which exhibit very different travel patterns, travel distances, and charging preferences. Note that even though more than half of GHG emissions [3] are emitted by light-duty vehicles (LDVs), medium-and heavy-duty vehicles (MHDVs) also contribute nearly 25 % of GHG emissions in the United States [28]. ...
... An optimization model is used to estimate the associated carbon emissions, using a life cycle perspective and various implementation scenarios. Similar work [22] is being done in China, where it is recognized that completely replacing all diesel buses with electric Buses may not be feasible due to budget constraints. Therefore, the goal is to find an optimal solution, called "fleet deployment equilibrium strategy", which takes into account the spatial and temporal fluctuations of passenger flows in the various bus lines. ...
Climate disruptions have prompted institutions to invest in zero-emissions technologies, in recent years. As a result, the transportation sector has witnessed a shift from internal combustion engines to electric. Several public transport companies have initiated the “Zero-emissions” project to introduce alternatives to diesel in their bus service. This paper delves into the impacts of transitioning from diesel-powered to electric buses. It starts by estimating emissions produced by buses and comparing them. Subsequently, the analysis evaluates the implications of renewing the fleet on the service, considering the change in bus capacity. Additionally, a profitability analysis assesses the Total Cost of Ownership, factoring in helpful life and average distance (kilometres) driven annually. Overall, the findings indicate that switching to electric buses is a promising approach towards achieving environmental objectives. The study shows that investing in electric buses, particularly those measuring 9-m, offers significant economic benefits while aligning with sustainability goals. The research demonstrates that electric buses yield a substantial reduction in global and local emissions when compared to their diesel counterparts. Adopting a comprehensive “well-to-wheel” perspective, electric buses achieve an impressive 68 % reduction in emissions. However, concerning local emissions, certain specific lines recorded values exceeded legal limits. While the initial investment costs for electric buses may surpass diesel buses, the total cost of ownership analysis conducted over 15 years indicates that electric buses can become more cost-effective over time. This cost-effectiveness and their environmental advantages strengthen the case for adopting electric buses to pursue sustainable transportation systems.
... When carbon neutrality is formally announced as a goal, the implementation level varies depending on each country's social and economic status; however, 128 countries around the globe have already declared a goal for carbon neutrality (Park & Chung, 2023). Furthermore, the primary sources of current global CE are the manufacturing, transportation, energy, and construction industries (Liu, Deng, et al., 2022;Liu, Guo, et al., 2022;Shao et al., 2022). There is an increasing demand in the construction industry to develop a carbon-neutral strategy in construction, planning, and operation via green buildings (GBs) (Bi et al., 2022;Delanka-Pedige et al., 2021). ...
The Intergovernmental Panel on Climate Change recently stated that a swift ecological transition in transport, energy, and construction is required to reach the global emission reduction target. Around 40% of global emissions come from residential and commercial buildings. Therefore, this study developed a green building adoption model based on the Unified Theory of Acceptance and Use of Technology, which encourages firms to implement these novel green building technologies. For this, a survey of 333 respondents from Pakistani construction firms was conducted, and Partial Least Square Structural Equation Modeling approach was applied to validate the study's hypotheses. The findings showed that green building adoption provided invaluable insight by showing the strong associations between green building environmental concern, green building performance expectancy, green building effort expectancy, green building social influence, and green building innovation cost with green building behavioral intentions. Likewise, the findings also showed that green building behavioral intentions play a significant mediating role between the adoption of green buildings and its factors. The findings from this study suggest that intentions to engage in green building activities have a strong direct and mediating effect on green building adoption. Furthermore, the moderating effect of firm size demonstrated the significant differences between small, medium, and large firms size. Based on the study’ findings, the green building adoption model provided novel insights for governments and concerned stakeholders, including construction firms, to adopt green buildings for carbon neutrality in this era of climate emergency.
... While, the carbon emissions from transportation sector still dominates, even with the increased number of high-speed rails and electric vehicles [18,19]. It is crucial to mitigate the urban traffic heat emission, in order to effectively improve urban thermal environment and achieve the target of "carbon neutrality" [20,21]. ...
... In the 1990s, heightened awareness of the environmental ramifications of fossil fuel use, coupled with the volatility of oil prices, spurred a reconsideration of the reliance on diesel generators. CO 2 emissions, along with other pollutants, have led many to seek cleaner alternatives [4]. One primary limitation of DG is its high acquisition and operational expenses. ...
This article presents a robust analysis based on the data obtained from a genuine microgrid in operation, simulated by utilizing a diesel generator (DG) in lieu of the Battery Energy Storage System (BESS) to meet the same load during periods of elevated energy costs. The study reveals that the BESS significantly outperforms the DG and the conventional electrical grid in various financial and environmental aspects. Environmentally, BESS accounts for zero CO2 emissions, compared to the 67.32 tons of CO2 emitted annually by the DG. Financially, the total cost of BESS over 20 years (USD 1,553,791.31) is lower than that of DG (USD 1,564,965.18) and the electrical grid (USD 2,726,181.09). Furthermore, BESS displays a lower Required Average Discharge Price—RADP (USD 0.38/kWh) and Required Average Price Spread—RAPS (USD 0.18/kWh) compared to DG (RADP: USD 0.39/kWh; RAPS: USD 0.22/kWh) and the electrical grid (RADP: USD 0.71/kWh; RAPS: USD 0.38/kWh). During periods of high-energy tariffs, BESS provides significant environmental benefits, but it also offers a more economically advantageous option to meet the load. It offers an energy-efficient and economically feasible solution for the operation of microgrids.
... Li (2016), Zeng et al. (2022), andDai et al. (2023) analyzed the key technologies of the existing electric bus operation, and pointed out that current electric buses have great limitations in the operating range and charging time. Shao et al. (2022) studied the balance between total greenhouse gas emissions and operating costs of a mixed fleet of diesel buses and electric buses by incorporating the impact of spatial and temporal passenger flows. The replacement of traditional diesel buses by electric buses is not a one-off process, but a gradual process. ...
Owing to the high acquisition costs, maintenance expenses, and inadequate charging infrastructure associated with electric buses, achieving a complete replacement of diesel buses with electric counterparts in the short term proves challenging. A substantial number of bus operators currently find themselves in a situation where they must integrate electric buses with their existing diesel fleets. Confronted with the constraints of limited electric bus range and charging infrastructure, the primary concern for bus operators is how to effectively utilize their mixed bus fleets to adhere to pre-established bus timetables while maximizing the deployment of electric buses, known for their zero pollution and cost-effective travel. Consequently, this paper introduces the concept of the joint optimization problem for vehicle and recharging scheduling within mixed bus fleets operating under constrained charging conditions. To tackle this issue, a mixed integer linear model is formulated to optimize the coordination of bus schedules and recharging activities within the context of limited charging infrastructure. By establishing a set of feasible charging activities, the problem of electric buses queuing for charging at constrained charging stations is transformed into a linear optimization model constraint. Numerical simulations are conducted within the real transit network of the Dalian Economic Development Zone in China. The results indicate that the judicious joint optimization of vehicle and charging scheduling significantly enhances the service frequency of electric buses while reducing operational costs for bus lines. Notably, the proportion of total trips performed by electric buses rises to 80.4%.
... In addition, fixed charging locations exist for buses, even for plug-in hybrid ones, which can be easily charged and improve operational efficiency. Taking the public transportation system in Liuzhou City, China, as the research object, when all diesel buses are replaced by electric buses, carbon emissions can be reduced by 23%, from 207.15 tons per day to 47.56 tons [33]. IEA data shows that every 1,000 pure electric buses on the road could save 5 million barrels of diesel while markedly decreasing airborne pollutants. ...
... This study focused on identifying the least energy-consuming routes and potential locations for electric bus infrastructure, assuming only constant slope values in a simplified road network. In a different approach, the authors of [20] developed a bi-objective model that minimizes both total carbon emission and operational cost for a mixed fleet of electric and diesel buses in the city of Liuzhou. While this study considered travel distance in their energy consumption model, it did not include slope considerations. ...
The transition to sustainable waterways transport is imperative in the face of environmental and climate challenges. Local lakes, often overlooked, play a significant role in regional transportation networks and ecosystems. This study focuses on Orta lake, Italy, and aims to facilitate its transition to sustainable inland waterways transport by substituting its diesel-based fleet with electric vessels. Firstly, a comprehensive market analysis was conducted to understand the available electric vessel models and their technical characteristics. This included parameters such as capacity, range, and charging time. Based on the market analysis, an optimization model was developed to determine the minimum number of electric vessels required to completely replace the existing diesel-based fleet. This model considers various constraints and objectives, such as meeting transport demand, minimizing the number of vessels, and reducing environmental impact. The developed model was then applied to the case study of Orta lake using the collected market data. The results indicate an optimal fleet configuration and provide insights into the feasibility and implications of the transition. This study contributes to the growing body of knowledge on sustainable inland waterways transport and offers a methodology that can be replicated and adapted for other local lakes or maritime settings.
... They assumed only constant slope values in a simplified road network with varying distances, but focused on the potential location of the infrastructure for electric buses. The authors of [15] combined energy consumption, carbon emission, and operational cost models and bi-objective model, which minimizes the total carbon emission and operational cost of all bus lines applied for the fleet of the city of Liuzhou for a mix of electric and diesel buses. Scholars considered travel distance in their energy consumption model; however, no slope considerations were included. ...
Public bus decarbonization is increasingly important to address the global issue of climate change. There are several challenges associated with large-scale introduction of zero-emission technologies in public fleets. This is especially the case in an extra-urban context, of mountain regions with challenging weather conditions. In this work the analysis of the state-of-the-art ZEBs, local bus lines, and timetables was performed to understand the best fit of technology—battery electric buses (BEBs) or fuel cell electric buses (FCEBs)—for each line in such a region. Further, a simulation tool was developed to calculate the compatibility of zero-emission technologies with the current needs of the public transportation considering distance, altitude difference, and climate conditions. The results show that a complete switch of the fleet is possible with a slight increase in the number of buses and that there is no clear difference in the distance covered in mountainous areas by BEBs versus FCEBs, but that both technologies can cover similar distances. The tool developed is not limited to bus fleets but can be applied to all kinds of fleets that cover clearly defined daily routes.
Reducing carbon dioxide emissions in transportation has become a priority for achieving emission targets. Transitioning to electric vehicles significantly decreases global CO2 emissions and reduces urban noise and air pollution. The selection of efficient charging strategies for electric bus fleets substantially influences their environmental impact. This study analyzes the charging strategy for electric bus fleets based on real operational data from Győr, Hungary. It evaluates the impact of different charging times and strategies on CO2 emissions, considering the energy mixes of Hungary, Poland, Germany, and Sweden. A methodology has been developed for defining sustainable and environmentally friendly charging strategies by incorporating operational conditions as well as daily, monthly, and seasonal fluctuations in emission factors. Results indicate substantial potential for emission reduction through the recommended alternative charging strategies, although further studies regarding battery lifespan and economic feasibility of infrastructure investments are recommended. The novelty of this work lies in integrating real charging data with hourly country-specific emission intensity values to assess environmental impacts dynamically. A comparative framework of four charging strategies provides quantifiable insights into emission reduction potential under diverse national energy mixes.
The urban bus system is an important part of the urban transportation system. Buses commute daily between terminals and depots, generating a large number of deadhead kilometers, thus increasing carbon emissions. Current bus-to-depot allocation methods lack the consideration of management requirements during the optimization process and are unable to provide intuitive comparison of different schemes for effective decision making. In this study, we present BusAllocVis, an interactive visual analytics system designed to optimize bus-to-depot allocation schemes to reduce carbon emissions from bus operations. First, a carbon emission calculation model for electric buses is defined. Then, different bus-to-depot allocation schemes are calculated that can consider different management preferences and minimize carbon emissions from deadhead kilometers. Finally, multiple visual views are designed to provide an intuitive explanation and comparison of different allocation schemes. The results of the case study and the task-based user study have demonstrated the effectiveness of our system in identifying the suitable allocation schemes for practical application scenarios.
The high investment costs of battery electric buses (BEBs), influenced directly by battery and infrastructure costs, pose a challenge that limits their large-scale adoption. Therefore, the success of this socio-technical transition requires economic feasibility analyses to identify the optimum adoption pathway. This study presents a system dynamics model (SDM) covering adoption scenarios from 2023 to 2030. The development of the model was established through interdisciplinary workshops conducted with local researchers from Brazil for collaborative conceptualization and modeling. We provide details of the costs involved, considering purchase, operation, maintenance, and infrastructure costs. Dynamic simulations show the estimated Total Cost of Ownership (TCO) for overnight and opportunity charging over the years, allowing a better understanding of the trade-off between large and small batteries for purchasing decisions and selecting charging infrastructure to reduce total costs. Our results indicate potential transport management concerning long-term cost planning associated with various battery sizes and charging strategies.
With ongoing global industrialization, the demand for refined oil products, particularly in developing countries, is increasing significantly. Shipping companies typically transport refined oil from a primary refinery to multiple oil depots, addressing various demand tasks. To manage uncertain refined oil demand, shipping companies use both self-owned tankers and outsourced tankers, including time-chartered and voyage-chartered tankers. A time charter is a contract where the shipping company pays charter money for a specific period, while a voyage charter involves payments based on voyage frequency. This paper develops a nonlinear programming model to optimize fleet deployment, considering transportation costs and penalty costs for capacity loss during a planning period. Additionally, the model is extended to allow flexible charter types, meaning that time-chartered and voyage-chartered tankers are interchangeable based on shipping demands. A heuristic algorithm based on tabu search is designed to solve the proposed models, and four search operators are incorporated to enhance algorithm efficiency. The models and algorithms are validated using a real tanker fleet. Numerical experiments demonstrate the efficiency of the improved tabu search algorithm in obtaining exact solutions for small-scale instances. The case study indicates that the shipping company prefers waiting for tasks to avoid ship delay penalties and provide high-quality services. Moreover, the flexible charter strategy can reduce shipping costs by 16.34%. These findings offer management insights for determining charter contracts for ship fleets.
Public attitudes and sentiments are considered important factors driving the development of new energy vehicles (NEVs) in China, yet few studies have effectively articulated the public attitudes and sentiments on a national scale. Social media platforms provide open and large-scale data on how the public views NEVs. Accordingly, based on data collected from Sina Weibo, the largest open social media platform in China, this study adopts web crawler and text mining to explore public attitudes and sentiments toward NEVs. The results show that the majority of NEVs followers belong to the media and broadcasting unit, which may lead to exaggerated advertising of NEVs performance. Public pay more attention to NEVs in environmental pollution and developed economies regions than others. In addition, it is found that the public is generally concerned about driving range, policies, battery charging and safety performance, and NEVs-related attributes. Some interesting findings are that preferential policies such as unrestricted driving are more popular with the public than tax incentives, and the public attaches a high degree of importance to the brand in the attributes of NEVs. Moreover, the Chinese public has relatively positive sentiments on NEVs, mainly due to the impact of China's carbon reduction targets. Negative sentiments are mainly due to safety and battery technology issues. Finally, some policy implications derived from the discussion are given.
This work aims to study the role and current situation of Low-Carbon Technology (LCT) in realizing urban Carbon Neutrality goals and Sustainable Urban Development (SUD). This work focuses on the three main aspects of Carbon Neutrality by means of investigation: the new energy-supported LCTs, building-oriented Energy Conservation and Emission Reduction (ECER), and Carbon Capture, Storage, and Utilization (CCSU) technology. The research of the past ten years (2013–2022) is reviewed from the Web of Science, Google Academic, and other literature databases. Over 20,000 articles are selected by using the keywords "Carbon Neutrality Technology," such as "IEEE," "Elsevier," and other journal documents. Finally, more than 150 papers with high citation rates and relatively novel research results are selected for specific analysis. This work also establishes a case study of urban green development with multiple co-governance and a case study of building energy conservation and carbon reduction solutions. Finally, suggestions are put forward for achieving SUD. China's energy consumption and Carbon Dioxide (CO2) emissions have increased year by year, but the Carbon Peak has not yet been reached. About 85% of CCUS technologies extract CO2 from the air and use it for other emission reduction processes. In addition, the carbon storage time and storage efficiency need to be improved, and the general level can only reach about 60% of the storage efficiency. The secondary use of CO2 by technology is of practical significance to many industries and must be fully implemented from the perspective of the whole life cycle. Especially building energy consumption is a huge carbon emitter and poses a challenge for SUD. How to take effective measures to control emissions is still an arduous task
Using rooftop solar photovoltaics (PV) and batteries together to power electric buses is considered a novel and feasible approach to reducing carbon emissions and tackling street-level air pollution in high-density cities like Hong Kong. However, associated optimal deployment is highly challenging due to the involved high-dimensional system planning, non-linear component sizing, and complex solar potential accurate characterization. To address this challenge, this study proposed a strategy to minimize the payback period of the deployed rooftop PV and batteries for achieving net-zero energy of electric bus transportation by taking associated grid impacts into consideration. In this strategy, a genetic algorithm-integer linear programming (GA-ILP) approach was developed to solve the high-dimensional rooftop PV planning problem and the non-linear PV and battery sizing problem. Our 3D-GIS (geographic information system) and DL (deep learning) integrated approach was used to attain accurate spatiotemporal rooftop solar power generation profiles. To verify the proposed strategy, a case study covering 5 bus terminuses with 28 bus routes and a total of 1,224 building rooftops surrounding the terminuses was conducted in a real region of Hong Kong. The effectiveness of the developed strategy was verified by comparing the optimized deployment with 150 million Monte-Carlo-generated solutions. The optimized deployment achieved the shortest payback period of 3.98 years by effectively tackling design issues such as battery oversizing, PV misallocation, and battery misallocation. To meet the increasing requirement on peak export power reduction (representing the grid impact constraint in the study), the proposed strategy can find a cost-effective balance between shifting PVs from higher-density solar potential terminuses to lower ones and increasing battery capacity. The proposed strategy can be adopted in practice to facilitate the development of cost-effective and grid-friendly community-solar-powered electric bus networks in high-density cities.
Electric city bus gains popularity in recent years for its low greenhouse gas emission, low noise level, etc. Different from a passenger car, the weight of a city bus varies significantly with different amounts of onboard passengers. After analyzing the importance of battery aging and passenger load effects on an optimal energy management strategy, this study introduces the passenger load prediction into the hybrid-electric city buses energy management problem, which is not well studied in the existing literature. The average model, Decision Tree, Gradient Boost Decision Tree, and Neural Networks models are compared in the passenger load prediction. The Gradient Boost Decision Tree model is selected due to its best accuracy and high stability. Given the predicted passenger load, a dynamic programming algorithm determines the optimal power demand for supercapacitor and battery by optimizing the battery aging and energy usage leveraging cloud techniques. Then, rule extraction is conducted on dynamic programming results, and the rule is real-time loaded to the vehicle onboard controller to handle prediction errors and uncertainties. The proposed cloud-based Dynamic Programming and rule extraction framework with the passenger load prediction show 4% and 11% lower bus operating costs in off-peak and peak hours, respectively. The operating cost by the proposed framework is less than 1% of the dynamic programming with the true passenger load information.
This study analyses the influence of passenger load, driving cycle, fuel price and four different types of buses on the cost of transport service for one bus rapid transit (BRT) route in Curitiba, Brazil. First, the energy use is estimated for different passenger loads and driving cycles for a conventional bi-articulated bus (ConvBi), a hybrid-electric two-axle bus (HybTw), a hybrid-electric articulated bus (HybAr) and a plug-in hybrid-electric two-axle bus (PlugTw). Then, the fuel cost and uncertainty are estimated considering the fuel price trends in the past. Based on this and additional cost data, replacement scenarios for the currently operated ConvBi fleet are determined using a techno-economic optimisation model. The lowest fuel cost ranges for the passenger load are estimated for PlugTw amounting to (0.198–0.289) USD/km, followed by (0.255–0.315) USD/km for HybTw, (0.298–0.375) USD/km for HybAr and (0.552–0.809) USD/km for ConvBi. In contrast, the coefficient of variation () of the combined standard uncertainty is the highest for PlugTw (: 15–17%) due to stronger sensitivity to varying bus driver behaviour, whereas it is the least for ConvBi (: 8%). The scenario analysis shows that a complete replacement of the ConvBi fleet leads to considerable higher cost of transport service on the BRT route, amounting to an increase by 64% to 139%, depending on the bus fleet composition. Meanwhile, the service quality is improved resulting in 42% up to 64% less waiting time for passengers at a bus stop.
With the increase in renewable energy penetration, the impact of uncertain factors on the efficient operation of multi-energy microgrids (MEMGs) is becoming more and more prominent. Considering the source-load uncertainties of MEMGs, a two-stage stochastic optimization approach based on scenario analysis is proposed in this paper. First, mixed distribution and conditional distribution were used to fit the forecast errors of wind power and multiple loads respectively, so as to provide basic data for scenario generation. Then, an improved K-means clustering algorithm based on relative entropy was used for scenario reduction. This algorithm ensured the scenario reduction speed while maintaining the probability distribution characteristics of the generated scenarios. Taking the day-ahead forecast and scenario analysis results of the sources and loads as inputs, a two-stage stochastic optimization model of MEMG based on random fluctuation stabilization was constructed. In the first stage, equipment outputs are formulated by deterministic optimization based on day-ahead forecasts. In the second stage, the forecast errors are regarded as fluctuations and combined with scenarized source and load variables, energy storage equipment are given priority to stabilize scenario fluctuations. At the same time, based on the conditional value at risk (CVaR), the outputs of energy supply and storage equipment can be flexibly adjusted under different risk preferences to realize the efficient operation of MEMG. The example simulation showed that the proposed stochastic optimization approach make better use of energy storage equipment, make scheduling plans according to different risk preferences to deal with uncertainty flexibly.
Within the context of the energy transition, there are several alternatives under study for the gradual replacement of diesel fuel based urban transport vehicles. This paper proposes an answer to the following question: Which bus technology and energy mix is more efficient in terms of cost, energy consumption and greenhouse gas emissions? A method is proposed to compare different urban bus fleet technologies, using an integrated index composed of three indices that measure well-to-wheel energy use, global warming potential in terms of carbon dioxide equivalent emissions, and total cost of ownership. The method is applied to the case of Argentina, from the 2019 scenario to the year 2030, and the results for each index show that, (i) even for the current energy scenario, battery and hydrogen fuel cell buses show a decrease in greenhouse gas emissions; that (ii) today the compressed natural gas bus is a better mean of passenger transport for both urban and intercity uses (it could reduce the carbon dioxide equivalent emissions 10.07% and the total cost of ownership 5.3%); and that (iii) both battery and hydrogen fuel cell vehicles become cost competitive with compressed natural gas and diesel vehicles over the course of the current decade. In addition, (iv) the battery electric bus is shown to become the best option by 2023 and (v) the hydrogen fuel cell bus proves to be the best option from 2027 onwards. The transition of the entire urban bus fleet in Argentina to zero-emission technologies is expected to be beneficial from the point of view of energy consumption, environmental emissions and the economy. If transition of the whole fleet to Hydrogen fuel cell buses is carried out, 1.3 Mt of carbon dioxide equivalent emissions could be reduced, which represents a 87% reduction in green house gases emissions, and if the transition is to battery electric buses, the energy consumption would be reduced by between 25 and 38% and emissions by between 52 and 61% abating around 0.93 Mt of carbon dioxide equivalent per year.
Operating short turning line is an efficient strategy to satisfy the unevenly distributed demand during peak periods while reducing operational cost. However, for the battery electric bus (BEB) system, the application of the strategy is challenging due to the disadvantages of BEBs, such as limited driving mileage and long charging time. Improper vehicle configuration and charging scheduling may dramatically increase the operational cost and cut the benefits of these strategies. In this work, we propose a general framework to design an effective short turning strategy for the BEB system at a tactical planning level. First, the trade-off relationship between the battery capacity and the average trip time is identified by modeling the BEBs operations. Second, a microeconomic model is formulated to jointly optimize the frequencies and charging schedules of the whole bus line and the short turning line, to effectively minimize passengers’ waiting time and operational cost. Finally, numerical experiments have been carried out for an illustrative linear line to demonstrate the potential benefits of the sub-line operating strategy compared with the normal operation.
Bus rides have become a routine choice for the large population to travel in Beijing. By the end of November 2020, 82.84% of all buses in Beijing had switched to electric vehicles. A solution to the problem of charging a huge number of battery electric buses (BEBs) in urban areas, especially in crowded cities with large populations, has to be found. In the present paper, a calculation model based on the levelized cost of electricity (LCOE) is established for comparison and analysis of the BEB charging. The calculation results show that the hybrid charging mode is economically feasible. Moreover, from the perspectives of optimizing operation shifts and responding to emergency situations, hybrid charging mode is applicable to the current electric bus system. The calculation results also show that the use of BEBs as the main ground public transportation in Beijing can substantially reduce CO2 emissions and save huge fuel costs. Its application is a very wise choice. The analogy calculation on Tongzhou District, the sub center of Beijing which is under construction, shows that the use of BEBs in Tongzhou can reduce CO2 emissions by about 34.6% on average compared with fuel buses. The population is the main influencing factor of analogy calculation.
Many countries are gradually adopting the latest energy driverless buses to reduce harmful environmental emissions, mainly in the transportation sector. The power supply is the main obstacle to developing new energy driverless buses while assuming the absence of battery technology advances. This paper proposes a new energy regenerative shock absorber to capture the wasted kinetic energy of the vehicle suspension system and produces electrical power. The regenerative shock absorber is divided into four modules: vibration energy capture module, motion conversion module, generator module and electric energy storage module. The random vibration of suspension, caused by certain factors, such as rugged roads and speed variation, acts on the vibration energy capture module. The motion conversion module mainly consists of two helical racks with opposite threads and converts the bidirectional vibration into unidirectional rotation of the generator. The utilization of helical gears with different diameters makes the damping coefficients different, so that the regenerative shock absorber can take full advantage of elastic elements to improve vehicle comfort during compression strokes, and quickly absorb vibration during extension strokes. The generator module generates electricity and the electric energy storage module accumulates the electricity in the supercapacitor. A prototype was fabricated, and the power generation performance of the regenerative shock absorber was evaluated by bench test under different sinusoidal excitations. At input sinusoidal displacement of 7 mm and 2.5 Hz frequency, the average output power of 4.25 W and maximum and average efficiencies 65.02% and 39.46% were calculated. The results demonstrate that the designed regenerative shock absorber can effectively scavenge renewable energy and apply it to the new energy driverless buses.
The use of smartphone applications (apps) to acquire real-time information for trip planning has become and progressively continues becoming a more instinctive behavior among public transport (PT) users. Thus, it becomes an integral part of the design and management of PT systems, but corresponding transit assignment models for improving the prediction of passenger ridership have yet to be developed. This work proposes a novel stochastic transit assignment model that predicts passenger ridership. Two new features are incorporated into a transit assignment model, namely, personalization and bounded rationality. Personalization refers to a personalized route-ranking methodology so that the app recommends paths with respect to a traveler’s preference considering various PT attributes. Bounded rationality is modeled over three route-choice strategies representing different levels of cognitive effort exercised by a traveler in selecting a path from the set of paths recommended by the app. The transit assignment model is formulated as a fixed-point problem. Because the mapping function of the fixed-point formulation is not necessarily continuous, the model constructs an approximated fixed point existing under certain measures of discontinuity. The method of successive averages (MSA) is applied to solve the problem. Numerical studies are conducted to demonstrate the properties of the new transit assignment model, the effect of demand on the path choice probability, and the effect of passengers’ heterogeneity on the convergence of the algorithm. The results reveal that, with a personalized path recommendation, passenger’s preferences could stabilize the differences of path choice probability when adopting route-choice strategies relying on the path order. In addition, although the MSA may not always converge and oscillate, the fluctuation is below the derived measure of discontinuity, indicating that an approximated fixed point can be found.
The bus-bridging service has always faced problems in severe emergencies or catastrophes that require the large-scale evacuation of passengers. This paper provides an alternative evacuation scheme which uses the urban bus network in the case of common metro service disruptions; this is modeled by minimizing the total cost of the affected metro passengers, through jointly selecting the bus lines and frequencies. The uncertain recovery time of the service disruption and the heterogeneous risk-taking behavior of the affected metro passengers are incorporated in the scheme. Therefore, we build a linkage between the evacuation service design and the risk-taking behavior of passengers. A heuristic algorithm is proposed to calculate the optimal evacuation scheme. A numerical experiment using a real-world network is conducted to illustrate the validity of the model and algorithm.
Flexible-route bus systems serving passengers at their doorsteps may be preferable to fixed-route bus systems in areas with low demand densities or whose roads cannot accommodate relatively large fixed-route buses. Flexible-route systems may also be preferable for elderly or handicapped riders for whom accessing the pre-determined stops on fixed routes is difficult. Since bus systems with flexible demand-responsive routes retain the economic and environmental advantages of public transportation, it is important to analyze them and optimize their characteristics to match their operating environments. This study shows how the total cost can be minimized for a flexible-route bus system with a many-to-one demand pattern by jointly optimizing its headway and service zone size. Numerical results demonstrate the model’s applicability and indicate how such flexible-route systems should be adapted to demand characteristics and planning constraints.
United States in 2017 emitted about 14.36% of the total global Greenhouse Gas (GHG), 27% of which comes from the transportation sector. In order to address some of these emission sources, alternative fuel technology vehicles are becoming more progressive and market ready. Transit agencies are making an effort to reduce their carbon footprint by adopting these technologies. The overarching objective of this paper is to aid transit agencies make more informed decisions regarding the process of replacing a diesel fleet with alternative-technology buses to minimize GHG emissions. This study investigates the complete course of fleet replacement using a deterministic mixed integer programming. Bus fleet replacement is optimized by minimizing the Life Cycle Cost (LCC) of owning and operating a fleet of buses and required infrastructures while reducing GHG emission simultaneously. Buses operated by Connecticut Department of Transportation (CTDOT) were used as a case-study. Results also show a significant reduction in both cost and emission for optimized replacement schedule vs the unoptimized one. Results also show that a fleet consisting of 79% Battery Electric Bus and 21% Diesel Hybrid Bus yields the least cost solution which conforms to the other operational and environmental constraints. This study also includes various sensitivity tests, that illustrates that although the magnitude of the results may vary depending on the input data, the direction remains the same. The problem formulated in this study can help any transit agencies determine the most optimized solution to their fleet replacement problem under customizable constraints or desired set of outcomes.
Transit bus passenger loading changes significantly over the course of a workday. Therefore, time-varying vehicle mass as a result of passenger load becomes an important factor in instantaneous energy consumption. Battery-powered electric transit buses have restricted range and longer “fueling” time compared with conventional diesel-powered buses; thus, it is critical to know how much energy they require. Our previous work has shown that instantaneous transit bus mass can be obtained by measuring the pressure in the vehicle’s airbag suspension system. This paper leverages this novel technique to determine the impact of time-varying mass on energy consumption. Sixty-five days of velocity and mass data were collected from in-use transit buses operating on routes in the Twin Cities, MN metropolitan area. The simulation tool Future Automotive Systems Technology Simulator was modified to allow both velocity and mass as time-dependent inputs. This tool was then used to model an electrified and conventional bus on the same routes and determine the energy use of each bus. Results showed that the kinetic intensity varied from 0.27 to 4.69 mi⁻¹ and passenger loading ranged from 2 to 21 passengers. Simulation results showed that energy consumption for both buses increased with increasing vehicle mass. The simulation also indicated that passenger loading has a greater impact on energy consumption for conventional buses than for electric buses owing to the electric bus’s ability to recapture energy. This work shows that measuring and analyzing real-time passenger loading is advantageous for determining the energy used by electric and conventional diesel buses.
Comparisons of the energy and emission intensity of transportation modes are standard features of sustainable transportation research, policy, and advocacy. These comparisons are typically based on average energy and emission factors per passenger trip or per passenger-kilometer traveled. However, as acknowledged in the energy production sector, comparing average emission factors can misinform policy and other decisions because it fails to represent the marginal impact of changing demand. The objective of this paper is to quantify the difference between average and marginal energy and emission factors for passenger transportation modes. Transportation system operations data are used to estimate energy and emission factors per passenger-kilometer traveled for U.S. urban and intercity travel. Marginal emission factors range from 30% (intercity rail) to 90% (private vehicles) of average factors. For urban travel, private vehicles and public transit have similar average emission factors, but marginal factors are 50% lower for transit. The average emission factor for intercity rail is 10% lower than air travel and 30% lower than private vehicles, but the marginal factor is 60% and 80% lower, respectively. Using average energy and emission factors to represent the impacts of travel by different modes is biased against public transit and discounts the benefits of shifting travel away from private passenger vehicles.
This paper presents a multiobjective optimization model to find efficient bus fleet combinations taking into account greenhouse gas emissions, conventional air pollutant emissions and costs. The goal is to minimize, simultaneously, three objective functions, Z1 (CO2 emissions), Z2 (Other Types of Emissions), and Z3 (total costs), for a buses fleet of a transit agency. For this case, there were four types of buses (diesel, electric bus, electric bus of fast charging, and CNG (compressed natural gas bus), which were analyzed in three different lines (South-North, Itinga, and South-Central) in Joinville city, Brazil. The respective data were modeled and optimized using MS Excel. Two different scalarization methods (the Weighted Tchebycheff and the Augmented Weighted Tchebycheff) are used for solving this buses fleet management problem. Different tradeoffs, in terms of the objectives, were obtained. The choice of a bus type is directly related to the characteristics (number of stops, average speed among others) of each line. The electrical bus is the best choice for reducing emissions but has a high initial cost and low autonomy. The results indicate that in the South–North line due to the large number of stops and low average speed, the electric bus is the type of dominant. In the other lines, the dominant ones were the diesel bus in the Itinga line and the CNG bus in the South line.
Growing concerns about energy conservation and the environmental impacts of greenhouse gas emissions over the world have promoted the development of the electric vehicles (EVs) market. However, one of the biggest barriers in the development of the EV market is the lack of the public charging infrastructure. This paper reviews the factors that can directly and indirectly influence the economics of the public charging infrastructure. The knowledge gaps, barriers and opportunities in the development of the charging infrastructure have been identified and analyzed. In order to promote the development of the public charging infrastructure, more research efforts should be paid on the impacts of psychological factors of customers and the technical development of charging infrastructures and EV batteries. The government support has been proved to play an important role, so that how the government policy can be tailored for the development of the charging infrastructure market should receive more attentions. In addition, the charging price as an endogenous factor should be considered more carefully in modelling the charging infrastructure market. New business models are also urgently needed to accelerate the future development of the public charging infrastructure.
Battery electric buses are seen as a well-suited technology for the electrification of road-based public transport. However, the transition process from conventional diesel to electric buses faces major hurdles caused by range limitations and required charging times of battery buses. This work addresses these constraints and provides a methodology for the cost-optimized planning of depot charging battery bus fleets and their corresponding charging infrastructure. The defined problem covers the scheduling of battery buses, the fleet composition, and the optimization of charging infrastructure in a joint process. Vehicle schedule adjustments are monetized and evaluated together with the investment and operational costs of the bus system. The resulting total cost of ownership enables a comparison of technical alternatives on a system level, which makes this approach especially promising for feasibility studies comprising a wide range of technical concepts. Two scenarios of European cities are analyzed and discussed in a case study, revealing that the cost structure is influenced significantly by the considered bus type and its technical specifications. For example, the total energy consumption of the considered lightweight bus is up to 32% lower than the total consumption of the high range bus, although the deadheading mileage increases. However, the total costs of ownership for operating both bus types are relatively close, due to the increased fleet size and driver expenses required for the lightweight bus system. The case study furthermore reveals that a mixed fleet of different bus types could be advantageous depending on the operational characteristics of the bus route.
Existing bus fuel consumption models produce a bang–bang type of control, implying that drivers would have to either accelerate at full throttle or brake at full braking in order to minimize their fuel consumption levels. This is obviously not correct. The paper is intended to enhance bus fuel consumption modeling by circumventing the bang–bang control problem using the Virginia Tech Comprehensive Power-based Fuel consumption Model (VT-CPFM) framework. The model is calibrated for a series of diesel-powered buses using in-field second-by-second data because of a lack of publicly available bus fuel economy data. The results reveal that the bus fuel consumption rate is concave as a function of vehicle power instead of convex, as was the case with light duty vehicles. The model is calibrated for an entire bus series and demonstrated to accurately capture the fuel consumption behavior of each individual bus within its series. Furthermore, the model estimates are demonstrated to be consistent with in-field measurements. The optimum fuel economy cruising speeds range between 40 and 50 km/h, which is slightly lower than that for gasoline-powered light duty vehicles (60–80 km/h). Finally, the model is demonstrated to capture transient fuel consumption behavior better than the Motor Vehicle Emission Simulator (MOVES) and produces a better fit to field measurements compared to the Comprehensive Modal Emission Model (CMEM).
Vehicle Specific Power (VSP) has been increasingly used as a good indicator for the instantaneous power demand on engines for real world driving in the field of vehicle emission and fuel consumption modeling. A fixed vehicle mass is normally used in VSP calculations. However, the influence of passenger load was always been neglected. The major objective of this paper is to quantify the influence of passenger load on diesel bus emissions and fuel consumption based on the real-world on-road emission data measured by the Portable Emission Measurement System (PEMS) on urban diesel buses in Nanjing, China. Meanwhile, analyses are conducted to investigate whether passenger load affected the accuracy of emission and fuel consumption estimations based on VSP. The results show that the influence of passenger load on emission and fuel consumption rates were related to vehicle's speed and acceleration. As for the distance-based factors, the influence of passenger load was not obvious when the buses were driving at a relative high speed. However the effects of passenger load were significant when the per-passenger factor was used. Per-passenger emission and fuel consumption factors decreased as the passenger load increased. It was also found that the influence of passenger load can be omitted in the emission and fuel consumption rate models at low and medium speed bins but has to be considered in the models for high speed and VSP bins. Otherwise it could lead to an error of up to 49%. The results from this research will improve the accuracy of urban bus emission and fuel consumption modeling and can be used to improve planning and management of city buses and thus achieve energy saving and emission reduction.
A total of 13 diesel buses and 12 diesel trucks in Macao were tested using portable emission measurement systems (PEMS) including a SEMTECH-DS for gaseous emissions and a SEMTECH-PPMD for PM2.5. The average emission rates of gaseous pollutants and CO2 are developed with the operating mode defined by the instantaneous vehicle specific power (VSP) and vehicle speed. Both distance-based and fuel mass-based emission factors for gaseous pollutants (e.g., CO, THC and NOX) are further estimated under typical driving conditions. The average distance-based NOX emission of heavy-duty buses (HDBs) is higher than 13 g km-1. Considering the unfavorable conditions for selective reductions catalyst (SCR) systems, such as low-speed driving conditions, more effective technology options (e.g., dedicated natural gas buses and electric buses) should be considered by policy makers in Macao. We identified strong effects of the vehicle size, engine displacement and driving conditions on real-world CO2 emission factors and fuel consumption for diesel vehicles. Therefore, detailed profiles regarding vehicle specifications can reduce the uncertainty in their fleet-average on-road fuel consumption. In addition, strong correlations between relative emission factors and driving conditions indicated by the average speed of generated micro-trips are identified based on a micro-trip method. For example, distance-based emission factors of HDBs will increase by 39% for CO, 29% for THC, 43% for NOX and 26% for CO2 when the average speed decreases from 30 km h-1 to 20 km h-1. The mitigation of on-road emissions from diesel buses and trucks by improving traffic conditions through effective traffic and economic management measures is therefore required. This study demonstrates the important role of PEMS in understanding vehicle emissions and mitigation strategies from science to policy perspectives.
This paper proposes a Linear Complementarity Problem (LCP) formulation for the reliability-based stochastic transit assignment problem with capacity constraints and non-additive link costs, where in-vehicle travel times and waiting times are uncertain. The capacity constraints are developed via the notions of effective capacity and chance constraints. An equivalent route-based linear program (LP) for the proposed problem is formulated to determine the patronage of each line section, critical links, critical service frequencies, unmet demand and the network capacity, which considers the risk-aversive behavior of travelers. A solution method is developed, utilizing the K-shortest path algorithm, the column generation technique, and the revised simplex method, to solve the proposed LP with guaranteed finite convergence. Numerical experiments are also set up to illustrate the properties of the problem and the application of the proposed model for reliability analysis.
We propose a new formulation for the assignment problem over congested transit networks. The congestion effects due to insufficient capacity of system elements (transit lines) are considered to be concentrated at transit stops. Waiting times on access links are therefore dependent on passenger flows. A special formulation of the transit network is used in order to model correctly the congestion effects. Finally, algorithms for solution are analyzed.
In this paper, we propose a user equilibrium transit assignment model that takes into account transit schedules and individual vehicle capacities explicitly. The model assumes that passengers use travel strategies that can be adaptive over time and graphically represented as subgraphs. When loading a vehicle, on-board passengers continuing to the next stop have priority and waiting passengers can be loaded on a first-come-first-serve basis or in a random manner. The latter is appropriate when passengers mingle on waiting platforms. When a vehicle is full, passengers unable to board must wait for the next vehicle to arrive. The equilibrium conditions can be stated as a variational inequality involving a vector-valued function of expected strategy costs. Although the function is not necessarily continuous or monotonic, a solution to the variational inequality exists. To find a solution, we propose a method that takes successive averages as its iterates and generates strategies during each iteration by solving a dynamic program. Numerical examples empirically demonstrate that the algorithm converges to an equilibrium solution.
This paper presents a first approach to dynamic frequency-based transit assignment. As such the model aims to close the gap between schedule-based and frequency-based models. Frequency-based approaches have some advantages compared to schedule-based models, however, when modelling highly congested networks a static frequency-based approach is not sufficient as it does not reveal the peaked nature of the capacity problem. The central idea for dealing with the line capacity constraints is the introduction of a “fail-to-board” probability as in some circumstances passengers are not able to board the first service arriving due to overcrowding. The common line problem is taken into account and the search for the shortest hyperpath is influenced by the fail-to-board probability. An assumption that passengers mingle on the platform allows a Markov network loading process which respects the priority of on-board passengers with respect to those wishing to board. The study period is divided into several time intervals and those passengers who failed to board are added to the demand in the subsequent time interval and so might reconsider their route choice. Trips that are longer than one interval are also carried over to subsequent time intervals. The approach is first illustrated with a small example network and then with a case study relating to London, where transit capacity problems are experienced daily during the peak period.
The price of diesel in Liuzhou
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PD. The price of diesel in Liuzhou. 2022, (Accessed on 1 2022) https://www.
icauto.com.cn/oil/price_450200_7.html.