Fleet Composition Research Articles

A Stochastic Approach to the Power Requirements of the Electric Vehicle Charging Infrastructure: The Case of Spain

Battery electric vehicles represent a technological pathway for reducing carbon emissions in personal road transport. However, for the widespread adoption of this type of vehicle, the user experience should be similar to that of combustion engine vehicles. To achieve this objective, a robust and reliable public charging infrastructure is essential. In Spain, the electric recharging infrastructure is growing quickly in metropolitan areas but much more slowly on roads and highways. The upcoming charging stations must be located along high-volume traffic corridors and in proximity to the Trans-European Transport Network. The main contribution of this research is to offer a method for examining the essential electricity infrastructure investments required in scenarios involving substantial electric vehicle adoption. The methodology includes a sensitivity analysis of fleet composition and market share, recharging user behavior, charging station density, and vehicle efficiency improvements. To this end, the authors have developed a simplified probabilistic model, addressing the effect of the involved parameters through a comprehensive scenario analysis. The results show that the actual number of high-capacity charging plugs on Spanish roads is significantly lower than the European regulation requirements for the year 2030 considering an electric vehicle market share according to the Spanish Integrated National Energy and Climate Plan 2021–2030 objectives and it is far from the necessary infrastructure to cover the expected demand according to the traffic flow. Under these circumstances, the charging peak power demand reaches over 7.4% of the current Spanish total power demand for an electric vehicle fleet, which corresponds to only 12% of the total.

Estimating daily road traffic pollution in Hong Kong using CFD modelling: Validation and application

Road traffic is a major source of urban air pollution, exposing residents to harmful levels of pollutants, particularly in densely populated urban centers. In this study, Computational Fluid Dynamics (CFD) modelling is applied to investigate the impact of road traffic emissions on air pollution within a dense urban environment. The model, applied in Mong Kok, Hong Kong, incorporates high-resolution traffic activity, meteorological, and background concentration data. It demonstrates strong agreement with roadside measurements, deviating by 18.8 % for NOx, 15.5 % for CO, and 0.7 % for PM10, with potential underestimation linked to unmodeled NOx-O3 photochemistry during midday hours (12:00–17:00). Results reveal that local road traffic significantly increases background NOx levels (41 % average increase), highlighting the importance of dispersion modelling for accurate pollution assessments. Traffic-induced increases in CO and PM10 background levels are less pronounced. Two applications demonstrate the model's utility. Firstly, analysis of daily pollution distribution reveals that approximately 10 % of the studied area experiences moderate to very high pollution levels during peak hours. Secondly, an investigation into pollution sources within a leisure park identifies heavy-duty and light-duty vehicles as the primary NOx polluters. This modelling approach provides a valuable decision-making tool for policymakers and local authorities. It enables the assessment of urban design changes, strategic placement of sensitive structures, and future traffic fleet composition choices to mitigate pollution exposure. As air quality regulations tighten, this study offers a robust method for guiding impactful mitigation strategies.

Engine option in aircraft purchase: Company strategies and policy implications

Airlines select an aircraft’s engine simultaneously while purchasing the aircraft. Usually, an aircraft model has multiple engine options, but recently the number of engine options has been decreasing. We develop a three-echelon supply chain model including engine manufacturers, aircraft manufacturers, and airlines to analyze the engine options in airlines’ new aircraft purchases. We show that given an airline’s existing fleet composition, larger fleet size and higher aircraft production efficiency would make it more likely to choose multiple engine options. In contrast, higher production costs and engine substitutability would lead to a shift to a single engine or hinder the airline’s fleet expansion. In the model, we highlight the impact of MRO (Maintenance, Repair, Overhaul) costs with economies of scale on airline engine option decisions as well. In particular, our theoretical findings suggest that the airline would buy more aircraft that are the majority types in their existing fleets, thus crowding out the minority ones. Our findings provide a reasonable explanation for the homogeneity of engine options and provide policy implications regarding airline expansion and aviation supply chain development.

Vehicle exhaust standards and urban air quality in China

This study quantifies the effects of increasing the share of low-emission passenger vehicles on urban air quality in China. We estimate a two-stage least squares model, using the phased implementation of National Vehicle Emission Standards VI in 2019 as an instrumental variable for fleet composition. Our findings indicate that a 1% increase in the share of vehicles compliant with the stricter vehicle exhaust standards leads to a 0.083% reduction in the average pollutant concentration across three measures of air pollution. Furthermore, our calculation suggests that the monetized health benefits from reductions in particulate matter outweigh the costs of adhering to stricter exhaust standards by a factor of two.

Assessing the diffusion of photovoltaic technology and electric vehicles using system dynamics modeling

Amidst the sweeping changes in the global electricity and automotive sectors, we observe a rapid surge in the proliferation of distributed generation (DG) and electric vehicles (EVs), primarily driven by the widespread deployment of photovoltaic systems. The widespread embrace of EVs necessitates a dual approach of financial incentives and infrastructure development to enhance the appeal of these vehicles. The findings presented in this paper hold significant importance for policymakers, underscoring the urgency of transitioning toward sustainable decentralized power systems and promoting EV adoption. While this transition offers promising opportunities, it also presents formidable challenges. Successful integration of DG and EVs demands careful attention to policy and regulatory frameworks. Some experts advocate for simultaneous adjustments in design, addressing mobility limitations and offering incentives for DG and EVs. Given the multitude of uncertainties, the authors suggest employing a system dynamics model to analyze the impact of photovoltaic technology and EV diffusion. The paper concludes that, within the Colombian context, the potential exists, under specific conditions, to increase the adoption of solar panels and EVs in households. This, in turn, contributes to a reduction in CO2 emissions and a transformative shift in the composition of the automotive fleet toward EVs.

Estimation of Traffic Emissions by Integrating COPERT and CUBE

Air pollution caused by motor vehicles is harmful to public health. Managing the cause of this type of pollution is very difficult. Effective prediction and management of pollution can be attained only by computing air pollution. It is difficult to measure emissions directly, so it is estimated using a variety of models developed worldwide. The current study made an attempt to integrate the emission model COPERT with the traffic assignment model in CUBE software for predicting the traffic-related emissions of pollutants SO2, NO2 & PM2.5 in mixed traffic conditions by considering passenger car and light duty vehicles (LDVS), two-wheeler, auto and bus. The study also aimed to include fuel variables and meteorological variable humidity along with fleet composition, average speed and average monthly temperature as inputs. This would enable us to control the rising vehicular emission, by assessing current and future air quality and thereby implementing policy decisions.

Characterizations of air pollutants at roadside monitoring stations and traffic emission effects: A case study in Tianjin, China

To investigate the response of Roadside Monitoring Stations (RSs) to traffic-related air pollution, traffic and pollutant characteristics, influencing factors, and potential source characterization in Tianjin, China were determined based on roadside monitoring of real-world data conducted at RSs in 2022. The diurnal variation trend of pollutants at RSs was consistent with that at the National Monitoring Station (NM), with notably higher pollutant fluctuations during the morning and evening peak traffic times at RSs, where the average diurnal concentration was 41.46 % higher than that at the NM. The generalized additive model (GAM) for nitrogen oxides (NOx) and carbon monoxide (CO), responding to the multiple influencing factors, performed well at RSs, with deviance explained by 86.6 and 61.4 %, respectively. The synergistic effects of wind direction and speed contributed to most of the variations in NOx and CO, which were 14.74 and 12.87 %, respectively. Pollutant concentrations were highest under windless conditions, with pollutants originating primarily from local vehicle emissions. The model results indicated that medium-duty truck (MDT) traffic flow predominantly contributed to the variability in NOx emissions, whereas passenger car (PC) traffic flow was the primary source of CO emissions from traffic variables. MDTs should be the focus of urban NOx traffic emissions control. Potential-source analysis validated the results obtained from the GAM, and both analyses showed that RSs can better characterize traffic-related air pollutants. Furthermore, more stringent emission standards have effectively mitigated the release of pollutants from motor vehicles and contributed to the modernization of vehicle fleet composition, effectively decreasing CO concentrations.

Description and validation of Vehicular Emissions from Road Traffic (VERT) 1.0, an R-based framework for estimating road transport emissions from traffic flows

Abstract. VERT (Vehicular Emissions from Road Traffic) is an R package developed to estimate traffic emissions of a wide range of pollutants and greenhouse gases based on traffic estimates and vehicle fleet composition data, following the EMEP/EEA methodology. Compared to other tools available in the literature, VERT is characterised by its ease of use and rapid configuration, while it maintains great flexibility in user input. It is capable of estimating exhaust, non-exhaust, resuspension, and evaporative emissions and is designed to accommodate future updates of available emission factors. In this paper, case studies conducted at both urban and regional scales demonstrate VERT's ability to accurately assess transport emissions. In an urban setting, VERT is integrated with the Lagrangian dispersion model GRAMM–GRAL and provides NOx concentrations in line with observed trends at monitoring stations, especially near traffic hotspots. On a regional scale, VERT simulations provide emission estimates that are highly consistent with the reference inventories for the Emilia-Romagna region (Italy). These findings make VERT a valuable tool for air quality management and traffic emission scenario assessment.

Impact assessment of future fleet compositions in vehicle emissions in urban areas: A methodological framework and a case study

This study explores the impact of emerging vehicle technologies on direct urban traffic emissions. It investigates emission reduction potential from shifts in fleet compositions and modal choices, especially considering climate change. To achieve this, three key research areas are explored for historical, current, and future scenarios (up to 2050): mode choice, emission factors for different vehicle categories, and diverse vehicle propulsion technologies. The estimation of the modal split is pivotal, developing a methodology utilizing Stated Preference survey data, discrete choice modeling, Monte Carlo simulations, and macroscopic traffic simulations. Future scenarios derive from the reference year’s modal split, and emission factors and fleet compositions are predetermined via an extensive literature review, aiding the assessment of their respective emissions. A subsequent sensitivity analysis identifies the impact of specific parameters on emissions, guiding future research focus. Study results underscore differences in greenhouse gas emissions and primary air pollutants between base and future scenarios.

Co-optimizing electric bus dispatching and charging considering limited resources and battery degradation

This paper aims to formulate a mathematical model for a multi-type electric bus scheduling problem to determine the optimal fleet composition, bus-to-trip assignment, and partial charging schedule, where the battery degradation, nonlinear charging, and the constraint of charging station capacity are considered. A time-expanded network is proposed to represent the bus-to-trip assignment and partial charging. An adaptive large neighborhood search algorithm is designed to solve the problem. Using a multi-line bus network in Nanjing as the case, empirical operational data is used to generate monthly timetable samples to simulate the uncertainty of trip travel time and energy consumption. The result shows that the charging station capacity can be reduced from 20 (real-world case) to 12, considering the cost-effectiveness and robustness of the bus system. The result of this study also provides suggestions on the charging duration choices and the starting state-of-charge for different periods of the day. In peak and off-peak hours, 20-30-minute charging is recommended for electric buses with state-of-charge lower than 30 %, and 10-minute charging is more recommended when the state-of-charge of the electric bus is between 30 % and 70 %.

Optimizing the fleet of emergency medical service vehicles

This study addresses the problem of optimizing composition and distribution of an ambulance fleet within an existing emergency medical system. The primary objective is to formulate a strategy aimed at minimizing response times and enhancing the system's responsiveness, particularly to critically ill patients. The optimal spatial distribution of various types of vehicles is proposed using a hierarchical p-median model. The effects of these strategies are evaluated through a detailed computer simulation model. Experimental studies have shown that optimizing station locations contributes more significantly than altering the fleet composition and associated dispatching protocols. In the context of the Slovak Republic, the proportion of high-priority incidents responded to within 8 minutes could be increased by 8.7% in urban areas and 10.5% in rural areas, solely through optimal deployment of currently active ambulances. An additional improvement of nearly 2% could be achieved by introducing 13 patient transport ambulances, optimally distributed throughout the country.

Optimal Network Design for Municipal Waste Management: Application to the Metropolitan City of Rome

This work is part of the regional research project PIPER—Intelligent Platform for the Optimization of Recycling Operations, which aims to develop a network design model for waste collection in the metropolitan city of Rome, Italy. The goal is to enhance the strategic planning of treatment and disposal facilities by incorporating transshipment locations to improve the efficiency of waste collection. The motivation for this study stems from the stringent targets set by the European Green Deal to achieve near-zero emissions and the critical role of waste management in realizing circular economy practices. Methods: The problem is formulated as a mixed integer linear program (MILP) that includes constraints on vehicle allocation to shifts and driver requirements. An additional feature of the model is its ability to account for vehicle maintenance issues when deployed on consecutive shifts. Results: The model was tested against a real-world case study in Rome and demonstrated significant potential savings, with a reduction in total traveled distance exceeding 10%. The mathematical model was also used for evaluating strategic scenarios in a “what-if” analysis, allowing the municipal collection company to assess location options for depots, waste treatment facilities, and transshipment points, as well as to optimize the fleet composition and driver allocation during work shifts. Conclusions: This study presents a robust tool for strategic planning in waste collection operations, highlighting the benefits of using transshipment locations to improve efficiency. The findings indicate substantial potential for cost savings and operational improvements, making the model a valuable asset for municipal waste management planning.

Electrified fleet and infrastructure aware energy efficient routing

This paper presents an optimization framework to improve the energy efficiency and cost-effectiveness of fleets of commercial trucks operating pickups and deliveries in urban areas. As the electrification of transportation is moving from passenger cars to medium- and heavy-duty vehicles, the proposed analysis considers a fleet of pickup and delivery trucks that includes conventional internal combustion engine vehicles (ICEV), as well as battery electric vehicles (BEV), and plug-in hybrid electric vehicles (PHEV). Given a set of pickups and deliveries, and a fleet including different types of vehicles, the goal is twofold: assign the best vehicle to each task, and solve the vehicle routing problem, i.e., find the optimal route to navigate the vehicle from the origin to the destination(s). To estimate the energy consumption of the different vehicles, vehicle dynamics are considered, together with actual charging infrastructure and road data, including speed limits, road grade, and stop signs. Moreover, the total cost of ownership (TCO) is evaluated to estimate the cost-effectiveness of different fleet compositions and operations. To solve this problem, a hybrid simulated annealing (HSA) heuristic algorithm is proposed. The algorithm is validated against a benchmark exact solver based on mixed integer linear programming (MILP). The proposed methodology achieves optimal results with a 1.2% optimality gap compared to the benchmark, surpassing MILP in computational efficiency. The research findings highlight how fleet composition and operational strategies can vary significantly based on whether the focus is on energy efficiency, total cost of ownership, or a combination of the two, also depending on the number of years of operation. Simulation case studies in the Columbus, OH area demonstrate that integrating fleet and recharging infrastructure information alongside energy savings in vehicle routing problem solutions can achieve 20% to 50% savings in fleet operation costs compared to solely optimizing for minimum energy consumption.

Analysis of Mixed Traffic Flow Characteristics Based on Fleet Composition

In urban road networks, the integration of connected and autonomous vehicles (CAV) significantly influences traffic flow patterns, with the Fleet Composition—representing the positioning of these vehicles within convoys—being crucial in dictating the symmetry of information exchange amongst them. First, the vehicle composition of the mixed traffic flow is analyzed, and the mathematical analytical expressions of the random distribution characteristics of different types of vehicles are constructed. Second, we analyze the vehicle according to human characteristics in different situations. Then, consider the following characteristics and reaction times of manual drivers, establish a mixed traffic flow following model, and validate the established following. Finally, the basic graph model considering Fleet Composition is derived, and the effects of reaction time, Fleet Composition, driver following characteristics, and other parameters on road capacity under different penetration rates of CAV are analyzed by a Python and SUMO joint simulation. Finally, the characteristics of mixed traffic flow at intersections were analyzed.

Personalization of the Car-Sharing Fleet Selected for Commuting to Work or for Educational Purposes—An Opportunity to Increase the Attractiveness of Systems in Smart Cities

Car-sharing services, which provide short-term vehicle rentals in urban centers, are rapidly expanding globally but also face numerous challenges. A significant challenge is the effective management of fleet selection to meet user expectations. Addressing this challenge, as well as methodological and literature gaps, the objective of this article is to present an original methodology that supports the evaluation of the suitability of vehicle fleets used in car-sharing systems and to identify the vehicle features preferred by users necessary for specific types of travel. The proposed methodology, which incorporates elements of transportation system modeling and concurrent analysis, was tested using a real-world case study involving a car-sharing service operator. The research focused on the commuting needs of car-sharing users for work or educational purposes. The study was conducted for a German car-sharing operator in Berlin. The research was carried out from 1 January to 30 June 2022. The findings indicate that the best vehicles for the respondents are large cars representing classes D or E, equipped with a combustion engine with a power of 63 to 149 kW, at least parking sensors, navigation, hands-free, lane assistant, heated seats, and high safety standards as indicated by Euro NCAP ratings, offered at the lowest possible rental price. The results align with market trends in Germany, which focus on the sale of at least medium-sized vehicles. This suggests a limitation of small cars in car-sharing systems, which were ideologically supposed to be a key fleet in those kinds of services. The developed methodology supports both system operators in verifying whether their fleet meets user needs and urban policymakers in effectively managing policies towards car-sharing services, including fleet composition, pricing regulations, and vehicle equipment standards. This work represents a significant step towards enhancing the efficiency of car-sharing services in the context of smart cities, where personalization and optimizing transport are crucial for sustainable development.

Integrating system dynamics and agent-based modeling: A data-driven framework for predicting electric vehicle market penetration and GHG emissions reduction under various incentives scenarios

As the growing deployment towards transportation electrification, a critical focus has emerged on quantifying the reduction contribution of greenhouse gas emissions from electric vehicles towards achieving carbon neutrality under diverse policy scenarios in the future. This necessitates a dynamic model that captures the evolving composition of the vehicle fleet and accurately forecasts the penetration and developmental trajectory of the electric vehicles in the car market. However, previous studies have largely overlooked the heterogeneity in user usage attributes, rendering them less effective in evaluating the impact of usage-based incentives on electric vehicle market penetration. To bridge this research gap, this study introduces an innovative, data-driven framework that integrates system dynamics and agent-based model. The proposed model can predict levels of electric vehicle penetration and corresponding greenhouse gas emission reductions within the private passenger vehicle sector, under a variety of policy scenarios. Our findings indicate that usage-based incentives, when implemented with optimal intensity, yield more significant emission reduction impacts and long-term economic benefits compared to conventional purchase-based subsidy. These insights not only furnish actionable policy suggestions to expedite the electric vehicle industry's growth in China but also offer valuable implications for other countries seeking to implement effective strategies for combating climate change and fostering sustainable transportation initiatives.

A fix and optimize method based approximate dynamic programming approach for the strategic fleet sizing and delivery planning problem

AbstractLogistics related costs constitute a major part in total cost of a product in general. Considering a company that delivers goods to its customers using its owned fleet, fleet ownership and operational costs together with the inventory costs compose the total logistics costs. In this study, we suggest an approximate Dynamic Programming algorithm, with a look ahead strategy, that uses the fix and optimize method as the imbedded heuristic for solving integrated fleet composition and replenishment planning problem. The total annual distribution cost factors considered in the problem are vehicle ownership costs, approximate routing costs, and inventory related costs. In this problem, we aim to minimize the total logistic cost by optimizing the fleet composition, replenishment patterns, and customers assigned to each vehicle in the fleet. We produced a set of reasonably large instances randomly and showed the efficacy of the suggested solution method.

Differentiated impact of low-exhaust-emission vehicles on NO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_\ ext {2}$$\\end{document} and particle concentrations in the Paris region

Higher concentrations of air pollutants, such as nitrogen dioxide (NO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_\ ext {2}$$\\end{document}) and particulate matter (PM), are observed in streets compared to the urban background. These concentrations could be predominantly attributed to road traffic, which remains an important source despite relentless efforts. In this study, performed over the Paris area, the impacts of the evolution of the fleet composition on urban air quality down to the street scale is assessed with two scenarios assuming the introduction of very-low-emission vehicles. Exhaust emission factors for these vehicles are based on the improvement of engine and after-treatment technologies, leading to factors lower than those proposed for the European emission standard Euro 7. Using the year 2014 as the baseline, very-low-emission vehicles are introduced up to the year 2030 for the Paris region. NO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_\ ext {2}$$\\end{document} emissions are thus reduced by 68 % in streets, and concentrations by 53 %. However, PM concentration reduction is limited to 18 % in streets as non-exhaust emissions from tyre and brake wear and road abrasion are preponderant. PM emissions from non-exhaust sources represent 59 % of the total road-traffic emission of PM in 2014 and 89 % in 2030. Non-regulated pollutant concentrations are also reduced, by 42 % for black carbon and 30 % for organic matter. Considering only very-low-emission and electric vehicles in the fleet further reduce NO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_\ ext {2}$$\\end{document} emissions and concentrations by 99.5 % and 80 % respectively. PM concentrations are only reduced by 22 %. This study highlights the high reduction potential of NO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_\ ext {2}$$\\end{document} concentrations with very-low-emission and electric vehicles, because of reduction of exhaust emissions. However, difficulties remain to reduce PM concentrations in urban areas, with the large majority of PM traffic emissions coming from non-exhaust sources.

Demolition of the European ships fleet: A scenario analysis

This paper conducts a detailed exploration of projected trends in the demolition and recycling of the European ship fleet, in light of the dismantling capacity in Europe. Utilizing a robust predictive model based on the Weibull distribution, the study synthesizes historical data and current operational metrics to forecast trends up to the year 2050, such as ship typologies, their average age, and recycling frequency. By estimating the rate and volume of ships reaching their end-of-life (EOL), the study aims to delineate the required capacity for ship recycling in Europe over the coming decades. The analysis reveals the key factors influencing the demolition trajectory and provide essential insights for future planning and policy formulation in the field of ship recycling. The study also underscore the importance of developing adaptive strategies in ship dismantling catering to the anticipated shifts in fleet composition and evolving environmental regulations, offering a foundation for informed decision-making and policy development in the maritime industry.

Транспортная энергоэффективность и экономические принципы организации морского судоходства

Modern problems of ensuring energy efficiency and carbon intensity of marine vessels are considered, taking into account the achievement of profitability of flights. Since January 2023, such issues have been particularly acute for all maritime convention vessels engaged in international transport, taking into account the introduction of amendments to annex VI of the International Convention for the Prevention of Pollution from Ships (MARPOL), developed within the framework of the IMO short-term strategy until 2030 to reduce emissions greenhouse gases from ships. The composition of the world merchant fleet by ship types and deadweight was analyzed, the work of the tram freight market and the features of achieving optimal economic operational speed of the vessel were evaluated, taking into account operational and technical conventional restrictions on carbon intensity. The transport work of the universal vessel Singularity over the past three years has been analyzed, taking into account changes in operational speed and the level of freight rates. An adequate mathematical model has been proposed for assigning an optimal average operating speed to a ship according to a carbon intensity rating. The proposed methodological materials can be used by shipping companies in the commercial and technical operation of the fleet by assigning an operational speed in preparation for the voyage and when carrying out corrective energy efficiency measures to achieve an acceptable level of carbon intensity ratings for marine vessels.