Search Results (56)

This paper proposes a general workflow for collecting and analyzing open data from Bicycle Sharing Systems (BSSs) that was developed using data from the Valenbisi system, operated in Valencia by the French company JCDecaux; however, the stages of the proposed workflow are service-independent and can be applied broadly. Cycling has become an increasingly popular mode of transportation, leading to the emergence of BSSs in modern cities. Parallel to this, Smart City solutions have been implemented using Internet of Things (IoT) technologies, such as embedded sensors and GPS-based communication systems, which have become essential to everyday life. When public transportation services or bicycle sharing systems are used, real-time information about the services is provided to customers, including vehicle tracking based on GPS technology and the availability of bikes via sensors installed at bike rental stations. The bike stations were examined from two different perspectives: first, their daily usage, and second, the types of facilities located in their surroundings. Based on these two approaches, the overlap between the clustering results was analyzed—specifically, the similarity in how stations could be grouped and the correlation between their usage and locations. To enhance the raw data retrieved from the service provider’s official API, the stations were annotated based on OpenStreetMap and Overpass API data. Data visualization was created using Tableau from Salesforce. Based on the results, an agreement of 62% was found between the results of the two different clustering approaches. Full article

Background/Objectives: Using healthcare vouchers mitigates the financial burdens of low-income individuals, therefore enhancing mothers’ satisfaction and encouraging service utilization. In Yemen, reducing financial barriers results in marked improvement in reproductive health services utilization for mothers and their newborns. Such financial strain can be addressed through reproductive health vouchers, which reduce out-of-pocket expenses of family planning, pregnancy, birth, postnatal care and neonatal care. This study compares the Safe Motherhood and Family Planning Voucher Program in the Lahj governorate to the non-voucher program in the Abyan governorate in terms of enhancement of reproductive healthcare accessibility and use. Methods: This facility-based, quantitative, comparative, cross-sectional study was conducted in the Lahj governorate, which supports the Safe Motherhood and Family Planning Voucher Program, and the Abyan governorate, which does not. Results: The voucher-supported program has greatly improved mothers’ satisfaction, access, and use of all reproductive health services by covering transportation, covering lodging during hospitalization, and providing free reproductive treatments. Compared to Abyan mothers, Lahj governorate mothers more frequently used rental vehicles (paid for by the voucher program) and free reproductive health services (p-value < 0.001). Lahj governorate mothers (who used the vouchers) used family planning, prenatal care, facility-based delivery, home delivery by competent birth staff, cesarean section, postnatal care, and neonatal care more frequently than Abyan governorate mothers. A health institution which supported the Safe Motherhood and Family Planning Voucher Program (SMHFPVP) provided prenatal care (98.5%), competent birth services (99.0%), and modern contraceptive use (92.3%)—oral contraceptive pills, implants, injectables, contraceptive patches, vaginal rings, and intrauterine devices—for mothers who were interviewed and attended the targeted HFs in the Lahj governorate, compared with (77.6%), (80.3%), and (67.8%), respectively, for mothers in Abyan governorate who were not supported by the SMHFPVP. This study demonstrates substantially higher satisfaction levels among voucher-using mothers in the Lahj governorate compared to those in the Abyan governorate, across all satisfaction domains and overall satisfaction scores. Conclusions: This study found that women without access to maternal health vouchers experienced worse prenatal, natal, and postnatal care and were less satisfied with healthcare services compared with women who used vouchers. Full article

Regarding the goals for achieving carbon neutrality by 2025, the transportation sector is one of the main causes of various environmental burdens, such as greenhouse gas (GHG) emissions and resource depletion, so reducing the environmental impact of the automobile industry is important. Although many countries are conducting numerous studies on the environmental impact of electric vehicles, they are limited to each country’s vehicles and models, and are limited to the production and process stages. In this study, we compared and analyzed the carbon reductions in electric and internal combustion engine vehicles during the operation stage for the most commonly used mid-sized rental vehicles in South Korea. The research results confirmed a reduction effect of approximately 1 MtCO₂-eq per year based on approximately 570,000 vehicles, and, if applied to all passenger vehicles nationwide, an average annual reduction effect of approximately 36 MtCO₂ can be expected. This figure corresponds to a reduction of approximately 30% in domestic transportation sector carbon emissions in 2024. This study is expected to have potential as a strategic indicator to start with, tailorable to the characteristics of each country’s transportation sector’s decarbonization processes. Full article

This study demonstrates that small-scale liquefied natural gas (SS LNG) is a viable and cost-effective alternative to High-Speed Diesel (HSD) for power generation in remote areas of Indonesia. An integrated supply chain model is developed to optimize total costs based on LNG inventory levels. The model minimizes transportation costs from supply depots to demand points and handling costs at receiving terminals, which utilize Floating Storage Regasification Units (FSRUs). LNG distribution is optimized using a Multi-Depot Capacitated Vehicle Routing Problem (MDCVRP), formulated as a Mixed Integer Linear Programming (MILP) problem to reduce fuel consumption, CO₂ emissions, and vessel rental expenses. The novelty of this research lies in its integrated cost optimization, combining transportation and handling within a model specifically adapted to Indonesia’s complex geography and infrastructure. The simulation involves four LNG plant supply nodes and 50 demand locations, serving a total demand of 15,528 m³/day across four clusters. The analysis estimates a total investment of USD 685.3 million, with a plant-gate LNG price of 10.35 to 11.28 USD/MMBTU at a 10 percent discount rate, representing a 55 to 60 percent cost reduction compared to HSD. These findings support the strategic deployment of SS LNG to expand affordable electricity access in remote and underserved regions. Full article

Under California’s Clean Miles Standard (or SB 1014), transportation network companies (TNCs) must transition to zero-emission vehicles by 2030. One significant hurdle for TNC drivers is the electric vehicle (EV) acquisition and operating costs versus an internal combustion engine (ICE) vehicle. This study therefore evaluates net TNC driving earnings through EV acquisition pathways—financing, leasing, and renting—along with EV-favoring policy options. Key metrics assessed include (1) total TNC income when considering service fees, fuel costs, monthly vehicle payments, etc., and (2) the time EVs take to reach parity with their ICE counterparts. Monthly comparisons illustrate the earning differentials between new/used EVs and gas-powered vehicles. Our analyses employing TNC data from 2019 to 2020 suggest that EV leasing is optimal for short-term low-mileage drivers; EV financing is more feasible for those planning to drive for TNCs for over two years; EV rentals are only optimal for higher mileages, and they are not an economical pathway for longer-term driving. Sensitivity analyses further indicate that EV charging price discounts are effective in shortening the time for EVs to reach cost parity over ICEs. Drivers may experience a total asset gain when reselling their TNC vehicle after two to three years. Full article

The car rental sector is a dynamic and rapidly growing business sector, which is important for both the development of the automotive industry and consumer mobility needs. In the rental car market, risk management becomes an essential factor determining the success and long-term growth of business operations. Risk factors inherent in this sector, such as the technical condition of cars, customer behavior, economic conditions and the legal environment, require a structured and efficient management that would allow companies to make the most of the available opportunities and to minimize threats. The purpose of this article is to examine the risk management of car rental and evaluate the impact of a 5–10-year-old car rental model with reverse logistics on risk reduction and business efficiency. The article examines the methods of the risk management process, presenting the methods of risk identification, assessment and reduction, as well as the possibilities of applying reverse logistics in the car rental sector. The results of the applied expert method of quantitative research and the conducted questionnaire survey revealed the factors that are most important for a company that renews its fleet of vehicles. These are the price, operating costs, sustainability, and environmental friendliness, with sustainability being the top priority. Companies can benefit from the research results when making decisions about the renewal and optimization of their vehicle fleet. It was concluded that implementing reverse logistics in the car rental sector will increase company profits and reduce pollution. Full article

The integration of connected autonomous vehicles (CAVs) has significantly enhanced driving convenience, but it has also raised serious privacy concerns, particularly regarding the personal identifiable information (PII) stored on infotainment systems. Recent advances in connected and autonomous vehicle control, such as multi-agent system (MAS)-based hierarchical architectures and privacy-preserving strategies for mixed-autonomy platoon control, underscore the increasing complexity of privacy management within these environments. Rental cars with infotainment systems pose substantial challenges, as renters often fail to delete their data, leaving it accessible to subsequent renters. This study investigates the risks associated with PII in connected vehicles and emphasizes the necessity of automated solutions to ensure data privacy. We introduce the Vehicle Inactive Profile Remover (VIPR), an innovative automated solution designed to identify and delete PII left on infotainment systems. The efficacy of VIPR is evaluated through surveys, hands-on experiments with rental vehicles, and a controlled laboratory environment. VIPR achieved a 99.5% success rate in removing user profiles, with an average deletion time of 4.8 s or less, demonstrating its effectiveness in mitigating privacy risks. This solution highlights VIPR as a critical tool for enhancing privacy in connected vehicle environments, promoting a safer, more responsible use of connected vehicle technology in society. Full article

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. Full article

Rapid growth rate indicates that the free-floating electric vehicle sharing (FFEVS) system leads to a new carsharing idea. Like other carsharing systems, the FFEVS system faces significant regional demand fluctuations. In such a situation, the rental stations and charging stations should be constructed in high-demand areas to reduce the scheduling costs. However, the planning of the FFEVS system includes a series of aspects of rental stations and charging stations, such as the location, size, and number, which interact with each other. In this paper, we first provide a method for forecasting the demand for car sharing based on the land characteristics of Beijing FFEVS station catchment areas. Then, the multi-objective MILP model for planning FFEVS systems is developed, which considers the requirements of vehicle relocation and electric vehicle charging. Afterward, the capabilities of the proposed models are demonstrated by the real data obtained from Beijing, China. Finally, the sensitivity analysis of the model is made based on varying demand and subsidy levels. From the results, the proposed model can provide decision-makers with useful insights about the planning of FFEVS systems, which bring great benefits to formulating more rational policies. Full article

In recent years, the implementation of shared electric micro-mobility services (SEMMS) enables short rentals of light electric vehicles for short-distance travel. The fast expansion of SEMMS worldwide, promoted as a green mobility service, has raised a debate about its role in urban mobility, especially in terms of environmental impacts such as climate change. This article presents a systematic review of the current knowledge on the environmental impacts of SEMMS, with a special focus on the use of life-cycle assessment (LCA) methods. The study offers a detailed analysis of the global warming potential of SEMMS and its critical phases. It is found that shared e-scooters have the greatest greenhouse-gas emissions during their life cycle, while emissions from shared e-mopeds and shared e-bikes tend to be lower. The literature reveals that the materials and manufacturing phase is the most important one for the environmental impact of shared e-scooters, followed by the daily collection of vehicles for charging. The article also identifies influential factors in the sensitivity analysis and the potential for net-impact reduction accounted for mode substitution. Finally, the article identifies further research areas aimed at contributing to the adoption of environmentally responsible practices in the rapidly expanding field of shared services in cities. Full article

In modern times, people predominantly use personal vehicles as a means of transportation, and, as this trend has developed, services that enable consumers to rent vehicles instead of buying their own have emerged. These services have grown into an industry, and the demand for predicting rental prices has arisen with the number of consumers. This study addresses the challenge in accurately predicting rental prices using big data with numerous features, and presents the experiments conducted and results obtained by applying various machine learning (ML) algorithms to enhance the prediction accuracy. Our experiment was conducted in two parts: single- and multi-step forecasting. In the single-step forecasting experiment, we employed random forest regression (RFR), multilayer perceptron (MLP), 1D convolutional neural network (1D-CNN), long short-term memory (LSTM), and the autoregressive integrated moving average (ARIMA) model to predict car rental prices and compared the results of each model. In the multi-step forecasting experiment, rental prices after 7, 14, 21 and 30 days were predicted using the algorithms applied in single-step forecasting. The prediction performance was improved by applying Bayesian optimization hyperband. The experimental results demonstrate that the LSTM and ARIMA models were effective in predicting car rental prices. Based on these results, useful information could be provided to both rental car companies and consumers. Full article

To determine a vertical cooperation strategy and address the optimal pricing problem of the electric vehicle (EV) supply chain, a supply chain system consisting of two competing EV manufacturers (M₁ and M₂) and a battery supplier is studied. Firstly, three cooperation strategy models were constructed for the battery supplier and the EV manufacturers, namely: Strategy N (neither the battery supplier nor the two manufacturers cooperate with each other); Strategy I (M₁ cooperates with the battery supplier); and Strategy II (M₂ cooperates with the battery supplier). Then, the Stackelberg solution method was used to obtain the optimal equilibrium decisions under the three strategic models. Finally, the effect of the preference coefficient of consumers for leasing EVs per unit on the optimal equilibrium decision was analyzed. We found that: (1) The wholesale price of batteries provided by the battery supplier to M₁ is always greater than to M₂. (2) Strategies I and II prompt M₁ and M₂ to reduce the unit and fixed rental prices of EVs to some extent, while intensifying the competition between the two manufacturers in terms of EV lease prices. (3) When the consumer preference coefficient (θ) for leasing EVs per unit provided by manufacturer M₁ is relatively small, the cooperation alliance S₂ and the supply chain achieve the maximum profit under Strategy II; however, while θ is large, M₁, cooperative alliance S₁, and the entire supply chain could benefit the most under Strategy I. Full article

How to reduce fuel consumption to mitigate CO₂ emissions to the atmosphere and improve road safety is one of the priorities to be addressed in the field of transport in the European Union. Considering the trend towards more frequent car rentals, it seems important to encourage drivers to change their driving style to a more ecological and economic one. This can be achieved by a system (built of a sensor located in the car, analytical software in the cloud and a mobile application for displaying results) that analyzes driving style and tells the driver how to drive better. Solutions such as the car bus PCB, GSM/GPS modem and 3D sensors were used in the development of the sensor. The validation of the sensor and the development of the analytical system are based on tests carried out in road conditions and in a closed area. Graphical methods (box-plot charts), correlation analysis and testing statistical hypotheses using the Mann–Whitney method were used in the analysis of the test results. The developed sensor and the analytical system allow for identifying the driving style of drivers. This system, through the use of a sensor that allows for downloading data not only from the car’s CAN bus but also the forces acting on the vehicle, permits the checking of 14 driving parameters used to interpret the driver’s driving style. Full article

In the context of actively and steadily implementing the “dual carbon” strategy, two competing electric vehicle manufacturers (manufacturers m₁ and m₂) were selected as research objects to construct two different leasing strategy models for electric vehicle manufacturers, namely, m₁ provided a unit rental electric vehicle strategy and m₂ provided a fixed rental electric vehicle strategy. We studied the optimal car rental strategy and pricing of the two manufacturers under the situation of m₂ providing and not providing rental service efforts, and the influence of relevant factors on the optimal decision are explored. It shows that the price of electric vehicles rented by consumers per unit increases with the combined effect of the coefficient of rental service effort and the marginal cost of the rental service effort, while the price of fixed rental electric vehicles decreases with the combined effect of both. When the unit rental preference coefficient is large, the unit rental of electric vehicles will give m₁ maximum profit. When the rental service effort coefficient is high, m₂ is the most profitable. The efforts to provide leasing services of m₂ increase their own interests to a certain extent. The greater the effort coefficient of leasing services, the smaller the marginal cost of leasing services, and the optimal social welfare reaches the maximum. The conclusion of the article can provide relevant leasing insights for electric vehicle manufacturers and also provide certain theoretical guidance for promoting electric vehicle leasing service strategies. Full article

This study investigates the understudied area of motivational factors influencing the rental intention of electric vehicles (EVs) within the context of their integration into urban transportation to combat air pollution and reduce carbon footprints and explores the critical factors influencing consumer behavior towards EV rental, focusing on hedonic motivation, service level, consumer habits, and willingness to pay. Utilizing multiple linear regression analysis on 302 valid samples from Texas, USA, the research identifies the significant impact of these factors on rental intention. Notably, the service level emerges as the most influential predictor while emphasizing the unique and less studied role of hedonic and personal characteristics as essential antecedents of rental intention. The findings, supplemented by a Monte Carlo simulation, reveal that these personal and motivational characteristics are pivotal in shaping rental intentions, accounting for approximately 47.2% of the variance in rental intention. The study contributes valuable insights into the EV rental market, offering theoretical implications for the EV literature and practical strategies for car rental enterprises to tap into consumer patterns effectively. Full article