Vehicle Fleet Research Articles

Green Public Transport in Poland—Planning the Process of the Electrification of the Bus Fleet of Vehicles

The concept of green public transport in Poland is a challenge posed to Polish cities, which reflects the AFIR (Regulation for the Deployment of Alternative Fuels Infrastructure) regulation and the amended Polish Law on Electromobility and Alternative Fuels. The fulfillment of the established prerequisites requires taking steps toward the implementation of the electrification process of the fleet of bus vehicles. The goal of this process is to replace the fleet of buses equipped with internal combustion engines (conventional buses) with zero-emission buses. In the category of zero-emission vehicles, we distinguish between electric-powered buses (electric buses), hydrogen-powered buses (hydrogen buses) and trolleybuses. It is forecast that such exchanges will be spread out over time and may last 16 to 20 years. Thus, an important element in these activities is planning, which, as a process, leads to the establishment of tasks, resources and activities aimed at realizing or achieving the desired goal. It was assumed that the purpose of the article is to present the essence of planning the process of electrification of the bus fleet of vehicles in Polish cities. The content of the article refers to the concept of planning the process of electrification of the bus fleet of vehicles in urban areas, taking into account economic aspects, sources of financing and analysis of the feasibility of this process on the example of a selected Polish city. The planning process was presented using the example of a city integrated with the surrounding nature. The choice of the city is not accidental because the analyzed urban area lies in the green part of the West Pomeranian Voivodeship, in the vicinity of which there are four nature reserves. The city is situated on two lakes, which together occupy 11.2% of the city’s area, and there are also numerous green areas in the city. The green nature of the city argued for taking action to reduce local emissions of pollutants generated by public transport. The process of planning the electrification of the bus fleet was carried out based on environmental and economic aspects. In economic terms, an important element is the possibility of obtaining external financing and the analysis of the feasibility of the planned process of electrification of the bus fleet. The conducted research clearly indicated that the presented process of planning the electrification of the bus fleet in the selected urban area allows for a precise interpretation of the considered planning in environmental and economic terms. Such an interpretation is important for the evaluation process of the analyzed planning and the implementation of the concept of green public transport in Polish cities, as well as the transparency of this process to other European cities and beyond.

Enhancing Urban Electric Vehicle (EV) Fleet Management Efficiency in Smart Cities: A Predictive Hybrid Deep Learning Framework

Enhancing Urban Electric Vehicle (EV) Fleet Management Efficiency in Smart Cities: A Predictive Hybrid Deep Learning Framework

Least-cost light-duty vehicle fleet decarbonization and the electric vehicle conundrum

This paper evaluates the greenhouse gas (GHG) emissions and financial performance of the U.S. light-duty vehicle (LDV) fleet. It develops dynamic models for conventional (ICEV), hybrid (HEV), plugin hybrid (PHEV) and battery electric (BEV) LDVs in four vehicle classes (compact, sedan, SUV and pickup truck), estimating their component sizes, purchase prices, embodied emissions, fuel consumption, life cycle GHG emissions and total cost of ownership (TCO) as a function of vehicle miles travelled (VMT). These dynamic models are applied to the US LDV fleet with its wide range of VMT per vehicle, revealing that ICEVs have the lowest TCO at lower VMT, but HEVs and BEVs become more cost-effective as VMT increases. Financially optimal powertrain choice can then reduce fleet-wide GHG emissions by one-third with current technologies, without any subsidies or increased costs. Technological advances, particularly in battery costs and electricity emissions, are then powerful drivers of further fleet decarbonization.

Cooperative navigation using constrained convex generators

In this article, we suggest a technique of set-based cooperative navigation for a fleet of vehicles under communication constraints. In the suggested approach, only the distances and bearings to other vehicles or known locations can be measured. Each vehicle in the fleet must determine its location and the positions of the other vehicles based on the measurements as well as information shared among the vehicles. Since the measurement set may be nonconvex, it must be approximated by a convex set. To address this issue, we employ constrained convex generators, which is a generalization of the definition for constrained zonotopes that allows ℓ2 unit norm balls and convex cones. To keep the amount of exchanged information low the vehicles transmit an ellipse containing the state estimate to the other vehicles. The obtained estimates are worst-case bounds for the true position, which is important in certain applications such as collision avoidance. The computed sets are then applied to vehicle control algorithms, taking into account the positions of all the agents. Through numerical simulations, we illustrate the application of this method to the problem of cooperative navigation of autonomous underwater vehicles and the problem of fire detection with multiple UAVs.

Comprehensive energy footprint of electrified fleets: School bus fleet case study

This paper proposes a comprehensive framework for estimating the energy footprint and benefits of electrified vehicle fleets prior to their deployment. To support this analysis, it introduces a control-oriented electric bus simulator model that not only captures driving power requirements but also incorporates a thermal model for cabin behavior and a Heating Ventilation and Air Conditioning (HVAC) system for heating and cooling. By analyzing current bus routes and road terrain data, the energy demand and economic effects are estimated, taking into account the current operational characteristics of school buses. As a case study, it examines the potential advantages of electrifying school bus fleets in the Central School District in Ohio, USA, with a focus on energy savings and environmental impact reduction. Our findings suggest that transitioning to electric school buses could achieve up to 76% energy savings compared to gasoline buses and 67% energy savings compared to diesel buses. Economically, when converted to operational costs, this results in a savings of 52%–65% compared to gasoline and 27%–47% compared to diesel under the average price rate. The accuracy of our model is calibrated using actual operational data from school bus fleets. Furthermore, this study provides foundational insights into the charging requirements through the energy footprint analysis. This study contributes to the advancement of sustainable transportation by presenting comprehensive preliminary analysis results for vehicle electrification through a specific case study. It emphasizes the practical implementation of electric school buses and optimized vehicle efficiency, aligning with broader eco-friendly initiatives in the transportation sector.

Optimizing same-day delivery with vehicles and drones: A hierarchical deep reinforcement learning approach

The advent of same-day delivery services has achieved immense popularity, driven by escalating customer expectations on fast shipping and the need for market competitiveness. To optimize such services, the use of heterogeneous fleets with vehicles and drones has proven effective in reducing the resource requirements needed for delivery. This paper focuses on investigating the same-day delivery dispatching and routing problem with a fleet of multiple vehicles and drones. In this problem, stochastic and dynamic requests, coupled with their stringent time constraints, require dispatchers to make real-time decisions about optimally assigning vehicles and drones, ensuring both efficiency and effectiveness in delivery operations while taking into account the routing. To tackle this complex problem, we model it with a route-based Markov decision process and develop a novel hierarchical decision approach based on deep reinforcement learning (HDDRL). The first level of the hierarchy is tasked with determining the departure times of vehicles, balancing the trade-offs between the delivery frequency and efficiency. The second level of the hierarchy is dedicated to determining the most suitable delivery mode for each request, whether by vehicles or drones. The third level is responsible for planning routes for vehicles and drones, thereby enhancing route efficiency. These three levels in the hierarchical framework collaborate to solve the problem in a synchronized manner, with the objective of maximizing the service requests within a day. Empirical results from computational experiments highlight the superiority of the HDDRL over benchmark methods, demonstrating not only its enhanced efficacy but also its robust generalization across diverse data distributions and fleet sizes. This underscores the HDDRL’s potential as a powerful tool for enhancing operational efficiency in same-day delivery services.

An air emission risk assessment of non-criteria pollutants in a tunnel study in Rio de Janeiro, Brazil.

Brazil's significantly large fleet of ethanol-powered vehicles makes it unique with regard to mobile source emissions. An accurate estimation of emission rates is best obtained from studies reflecting real-world vehicular conditions, such as tunnel investigations. Although laboratory-based estimations are standardized, they may not reflect reality. This study evaluates two methods for estimating emission factors that arise from the circulating fleet of vehicles that travel through the Rebouças Tunnel in Rio de Janeiro. The two monitoring points were located in the L1 gallery of the tunnel. Thirteen sampling campaigns took place between 2017 and 2020. The Pierson correlation method yielded emission rates for total aldehydes of 22.1 ± 6.9mgkm-1, while the fuel consumption testing method resulted in rates of 17.7 ± 7.8mgkm-1. Acetaldehyde predominated in both methods, as it is a major constituent of the exhaust emissions using ethanol as fuel, which is one of the major component fuels in the Brazilian fuel matrix. Benzene emerged as the most abundant BTEX compound, with 3.7 ± 3.2mgkm-1, when the Pierson method was used, and 2.5 ± 0.3mgkm-1 with the fuel consumption testing method. Naphthalene, which was mainly collected in the gas phase, showed rates of 1.4 ± 1.0mgkm-1 (Pierson) and 0.9 ± 0.4mgkm-1 (fuel consumption). Despite these methodological variations, our findings converge, which suggests that the choice of method should depend on available resources. The Pierson method, which requires a vehicle counting method for traffic flow, obviates the need for CO and CO2 concentration measurements, that are essential for determining fuel consumption.

An IoT-based Fuel Monitoring and Tracking System with Real-time Fuel Level Detection using Accelerometer

In the transportation sector, effective fuel management is essential due to increasing environmental and economic concerns. However, traditional fuel level indicators often lack the necessary precision for optimizing fuel consumption. This paper introduces a novel IoT-based fuel monitoring and tracking system designed to overcome these challenges. By combining an accelerometer for real-time fuel level detection with GPS for location tracking and mileage calculation, the system provides comprehensive monitoring capabilities. Additionally, cloud-based data processing and a user-friendly mobile application enable real-time data visualization and analysis. This integrated approach empowers fleet managers and vehicle owners to make informed, data-driven decisions that enhance fuel efficiency, lower operational costs, and reduce environmental impact.

TELEMETRY AS AN IOT TOOL IN VEHICLE FLEET SAFETY MANAGEMENT

Telemetry is one of the main tools of the Internet of Things (IoT) applied to fleet management, enabling geographic tracking, speed measurement, and monitoring of other variables in real time through wireless communication in remote and urban areas. This study aimed to verify the application of this IoT technology in the safety management of vehicle fleets, exploring its contribution to reducing incidents and increasing operational efficiency. Telemetry technology, with data transmission and continuous monitoring, enables managers to make more assertive decisions about the safety of drivers and employees, in addition to optimizing logistics and reducing costs related to fleet operations. The methodology was based on an exploratory and analytical approach, combining a conceptual review of articles and books with a case study applied to a large mining company located in Minas Gerais, in 2018. The results indicated that, after the implementation of the telemetry system, there was a significant reduction in the frequency of speeding and, consequently, in the number of accidents. Furthermore, it was possible to identify patterns of recidivism among drivers, which reinforces the importance of continuous monitoring. It was concluded that telemetry, as an IoT tool, not only contributes to increased safety, but also improves operational efficiency, enabling proactive fleet management and opening opportunities for future research, such as the use of big data for predictive analysis.

The Heterogeneous-Fleet Electric Vehicle Routing Problem with Nonlinear Charging Functions

This paper introduces the Heterogeneous-Fleet Electric Vehicle Routing Problem with Nonlinear Charging Functions (HEVRP-NL). This problem involves routing a heterogeneous fleet of electric vehicles, utilizing multiple charging modes, and accounting for time-dependent waiting time functions at charging stations. The problem is modeled using a path-based mixed-integer linear programming formulation. To solve this problem, we present an algorithmic framework that alternates between two components. The first component is an iterated local search algorithm with a problem-specific route evaluation function, which obtains local optimal solutions and generates a pool of high-quality routes. The second component is a set-partitioning model that combines a subset of routes from the pool, which is constructed based on reduced costs, into a feasible solution. We design HEVRP-NL benchmark instances based on the publicly available electric fleet size and mix vehicle routing problem instances, which are used to evaluate our methods. For small-scale HEVRP-NL instances, the proposed model can be employed in a general-purpose mixed integer programming solver to achieve optimal solutions or find good upper bounds. This exact approach serves as an evaluation of our heuristic algorithm’s ability to attain optimal solutions rapidly. Extensive computational results on large-scale HEVRP-NL instances illustrate the advantages of considering non-linear charging functions and show the impact of waiting time at the charging stations. Finally, we conduct experiments on 120 benchmark instances for the E-VRP-NL and 168 benchmark instances for the E-FSMFTW-PR, which are the special cases of our problem. The results indicate that our algorithm outperforms existing approaches from the literature and identifies 32 new best solutions for the E-VRP-NL and 33 new best solutions for the E-FSMFTW-PR, respectively.

Modelling the impacts of en-route ride-pooling service in a mixed pooling and non-pooling market

En-route ride-pooling services, e.g. Didi Pinche, UberPool, have been considered a promising way to alleviate traffic congestion during rush hours. Yet in reality, its market share is still small compared to non-pooling service. For a ride-hailing platform that provides both en-route pooling and non-pooling services, this paper endeavors to answer under what circumstance would it have incentive to encourage ride-pooling, and whether drivers, riders, the platform and the society can simultaneously benefit from the introduction of en-route ride-pooling service. We propose an aggregate model to characterize the equilibrium of a ride-hailing market where a platform operates a fleet of vehicles to provide en-route pooling and non-pooling services. The model captures the complex interactions among riders’ mode choices between pooling and non-pooling, the waiting times of pooling and non-pooling users, the pairing probability and the expected detour time of pooling users, the matching rates of cruising and halfly occupied vehicles with different types of riders, and the numbers of cruising, halfly occupied and fully occupied vehicles at each instant. Based on the model, we theoretically reveal the impacts of the total ride-hailing demand, vehicle fleet size, and the platform’s pricing and vehicle dispatching strategies on the market equilibrium performance through partial derivative-based sensitivity analysis, and establish conditions under which the introduction of en-route ride-pooling service improves platform profit, driver income, rider utility and social welfare.

Exploring the dynamics of the transport system energy consumption in Australia − A preliminary systems modelling effort

In the face of an ever-increasing trend in the Australian transport demand with minimal improvement in fuel efficiency, petroleum products continue to dominate the primary energy source for the transport sector. The persistence of this reliance hinges on the urgent formulation and robust implementation of effective policies. This paper endeavours to elucidate how the application of system dynamics modelling can facilitate the development and assessment of efficacious policies to steer the trajectory of transport energy consumption in Australia. To illustrate this approach, we present two compelling case studies − the urban passenger transport system in New South Wales (NSW) and the freight transport system in Australia. The first case study explores the intricate dynamics of urban passenger transport mode choice, which shapes different pathways for energy consumption. These pathways are influenced by relative vehicle utility, corresponding to changes in GDP per capita, congestion levels, toll and fuel costs, and historical travel patterns. The second case study delves into Australia’s complex dynamics of heavy vehicle fleet transition. This transition is contingent on many variables, such as changes in GDP, freight tasks, infrastructure capacity requirements, operational and capital costs of various heavy vehicle classes, and fluctuating fuel costs, among others. Simulation results present the dynamic interplay between supply and demand factors. This showcases how an integrated system dynamics model of the transport system can be developed as a holistic policy assessment tool to achieve a balanced energy consumption in the transport system and thereby the sustainability goals. This study calls for more in-depth research about this problem to support policymakers with a decision-support tool to assist scenario analysis and intervention design over time toward achieving sustainable development goals within Australia’s transport system.

INTELIGÊNCIA ARTIFICIAL NO TRÂNSITO

This study examines the role of Artificial Intelligence (AI) in urban traffic management, considering the growing challenges arising from the increase in the vehicle fleet and rapid urbanization. These factors contribute to frequent traffic congestion, increased pollutant emissions and a higher accident rate, which generates an urgent need for innovative solutions that promote safer, more efficient and sustainable traffic. The main objective is to evaluate how AI can be used to optimize urban mobility, reduce the frequency of accidents and minimize traffic-related pollution, in addition to identifying the main challenges and opportunities in the implementation of such technologies in modern urban contexts. The research was carried out through a literature review, analyzing scientific articles, publications and specialized documents. This survey sought to synthesize the existing knowledge on advances in intelligent transportation systems (ITS) and explore the practices and technologies that interconnect data and communication applied to traffic. The sources consulted included databases such as Google Scholar and recognized publications in the area, with a focus on developing countries, where the adoption of AI in urban mobility is still incipient. The use of AI-powered ITS can not only improve traffic flow and reduce congestion, but also contribute to a more sustainable urban environment. The research suggests that investment in infrastructure and capacity building will be crucial to the successful implementation of AI in traffic systems, especially in developing countries.

Effect of adaptive cruise control on fuel consumption in real-world driving conditions

This paper presents a comprehensive analysis of the impact of adaptive cruise control on energy consumption in real-world driving conditions based on a natural experiment: a large-scale observational dataset of driving data from a diverse fleet of vehicles and drivers. The analysis is conducted at two different fidelity levels: (1) a macroscopic trip-level benefit estimate that compares trips with and without cruise control in a counterfactual way using statistical methods, and (2) a situation-based comparison achieved through the segmentation of trips into distinct driving situations such as acceleration, braking, cruising, and other maneuvers. The results of this research show that the effect of cruise control on energy consumption varies across different driving situations and levels of analysis. In a macroscopic trip-level analysis, cruise control engagement is associated with a slight increase in fuel consumption across the fleet. As revealed later by the situation-based analysis, this result can be attributed to the negative impact of cruise control on energy consumption in cruising mode, which is the most common driving situation. However, the situation-based comparison demonstrates that cruise control can provide fuel consumption benefits in situations involving acceleration and braking, particularly when a preceding vehicle is present. The study also emphasizes the importance of controlling for various factors that can influence both fuel consumption and the likelihood of cruise control engagement to properly evaluate its effects.

Towards Cleaner Cities: Estimating Vehicle-Induced PM2.5 with Hybrid EBM-CMA-ES Modeling.

In developing countries, vehicle emissions are a major source of atmospheric pollution, worsened by aging vehicle fleets and less stringent emissions regulations. This results in elevated levels of particulate matter, contributing to the degradation of urban air quality and increasing concerns over the broader effects of atmospheric emissions on human health. This study proposes a Hybrid Explainable Boosting Machine (EBM) framework, optimized using the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), to predict vehicle-related PM2.5 concentrations and analyze contributing factors. Air quality data were collected from Open-Seneca sensors installed along the Nairobi Expressway, alongside meteorological and traffic data. The CMA-ES-tuned EBM model achieved a Mean Absolute Error (MAE) of 2.033 and an R2 of 0.843, outperforming other models. A key strength of the EBM is its interpretability, revealing that the location was the most critical factor influencing PM2.5 concentrations, followed by humidity and temperature. Elevated PM2.5 levels were observed near the Westlands roundabout, and medium to high humidity correlated with higher PM2.5 levels. Furthermore, the interaction between humidity and traffic volume played a significant role in determining PM2.5 concentrations. By combining CMA-ES for hyperparameter optimization and EBM for prediction and interpretation, this study provides both high predictive accuracy and valuable insights into the environmental drivers of urban air pollution, providing practical guidance for air quality management.

Electromobility in local administration offices of provincial cities in Poland – status at the end of 2022

ABSTRACT: The objective of this article is to investigate behaviours related to electromobility in terms of owned vehicle fleets and charging stations in local administration units, the role of zero-emission vehicles in building the image of the administration and the promotion of environmentally friendly behaviours among its employees as at the end of 2022. The completion of the objective was based on surveys of the data collected using the CATI technique from provincial offices (POs) and municipal offices (MOs)[1] of provincial cities. The survey showed that the primary means of transport owned by the entities surveyed are internal combustion engine vehicles. Of these, the largest group is made up of passenger cars – the average age of vehicles at the time of the survey was approx. eight years. In sum, the local administration in the provincial cities is approaching the target value established by law. The average share of BEVs in the passenger vehicle fleet is 9%. In terms of charging station ownership, there were three dominant patterns. There is a perception among respondents that a fleet of environmentally friendly vehicles builds a positive, pro-environmental image of the office, while in none of the offices was there any action to support the environmentally friendly behaviour of employees in terms of travelling to and from work. [1] The survey covered local government administrative units in 16 cities where local government offices are located and involved provincial local government offices (POs) and municipal local government offices (MOs).

A Web-Interface Based Decision Support System for Optimizing Home Healthcare Waste Collection Vehicle Routing

Background: The significant increase in home healthcare (HHC) driven by technological advancements, an ageing population, and heightened disease outbreaks—especially evident during the COVID-19 pandemic—has created an urgent need for improved medical waste management. Methods: This paper presents the development of a decision support system with a web-based interface designed for efficient medical waste collection in the HHC sector. Results: The system utilises Flask for backend operations, with HTML and CSS for the user interface, and manages data using JSON files. Its flexible design supports real-time adjustments for various vehicle types and changing waste production locations. It incorporates dynamic routing by employing two sophisticated metaheuristic algorithms: the Strength Pareto Evolutionary Algorithm (SPEA-2) and the Non-Dominated Sorting Genetic Algorithm (NSGA-II). This setup supports different dataset sizes and vehicle fleets, including Internal Combustion Engine (ICE) vehicles and Electric Vehicles (EVs). Conclusions: The automation reduces uncertainties in waste collection by minimising human intervention. The system is built to be easily adaptable for other sectors with minor modifications and can be expanded to test various scenarios with new selectable parameters.

A Matheuristic Approach Based on Variable Neighborhood Search for the Static Repositioning Problem in Station-Based Bike-Sharing Systems

In this paper, we study a novel static bike-sharing repositioning problem. There is a set of stations spread over a given area, each containing a number of operative bikes, damaged bikes, and free slots. The customers may pick up an operative bike from a station, use it, and return it to another station. Each station should have a target number of operative bikes to make it likely to meet customer demands. Furthermore, the damaged bikes should be removed from the stations. Given a fleet of available vehicles, the repositioning problem consists of designing the vehicles’ routes and calculating the number of operative (usable) and damaged (unusable) bikes that will be moved (loading instructions/loading policy) between stations and/or the depot. The objective is to minimize the weighted sum of the deviation from the target number of bikes for each station, the number of damaged bikes not removed, and the total time used by vehicles. To solve this problem, we propose a matheuristic based on a variable neighborhood search combined with several improving algorithms, including an integer linear programming model to optimize loading instructions. The algorithm was tested in instances based on real-world data and could find good solutions in reasonable computing times.

Fleet vehicle replacement strategy in the Korean market under phase-out of conventional vehicle

With the recent global requirements to phase out internal combustion engine vehicles (ICEVs), fleet owners (FOs) must consider battery electric vehicles (BEVs) as replacements, introducing regulatory risk due to policy changes. Many countries have legal retirement age limit for fleet vehicles (FVs), considered as policy constraint necessitating eventual replacement. Additionally, uncertainties like fuel price of ICEV and battery price affect the replacement decision. This study proposes an optimal replacement strategy using real option approach for annual decision-making under multiple risk factors. Using data from the Korean FV market, we provide managerial implications. Despite optimistic battery price projections, voluntary BEV switching is not expected to exceed 40% of total FVs. Sensitivity analysis reveals that adjusting the legal retirement age is more effective than changing the BGVSP implementation year. Also, A flexible subsidy based on fuel prices and the extension of the legal retirement age limit through safety inspections should be recommended.

Advancing sustainable EV charging infrastructure: A hybrid solar-wind fast charging station with demand response

This study aims to design an efficient hybrid solar-wind fast charging station with an energy storage system (ESS) to maximize station efficiency and reduce grid dependence. The research employs Monte Carlo simulation to capture renewable energy source (RES) uncertainties, models stochastic electric vehicles (EVs) driver behavior and fleet diversity, and utilizes an Erlang B queuing model for EV load demand estimation. A hybrid optimization approach combining the Binary-Gravitational Search Optimization Algorithm (B-GOA) and the Non-dominated Crowding Sort Optimization Algorithm (NCSOA) is implemented for efficient optimization in a combined binary-continuous solution space. Results demonstrate superior performance of the proposed approach compared to existing methods, with high-RES penetration significantly reducing grid reliance. This study contributes to advancing sustainable EV charging infrastructure development and enhancing overall grid stability through improved load flexibility and demand response management. The analysis reveals Scenario IV as the most economically viable, with the highest Net Present Value of 1,025,895.32€. Most scenarios favor 5 chargers (44–46 kW) and 4 Type 3 wind generators. Battery capacity varies widely (108–372 kWh), as does grid connection (0–292 kW). Despite varying initial investments, Scenarios II-VII show a consistent 4-year Internal Rate of Return, indicating good economic potential across different configurations.