Bibliographies thématiques / Free Floating Car Sharing

Littérature scientifique sur le sujet « Free Floating Car Sharing »

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Publié le 14 mars 2023

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Articles de revues sur le sujet "Free Floating Car Sharing"

Cocca, Michele, Danilo Giordano, Marco Mellia et Luca Vassio. « Free floating electric car sharing design : Data driven optimisation ». Pervasive and Mobile Computing 55 (avril 2019) : 59–75. http://dx.doi.org/10.1016/j.pmcj.2019.02.007.

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Kypriadis, Damianos, Grammati Pantziou, Charalampos Konstantopoulos et Damianos Gavalas. « Optimizing Relocation Cost in Free-Floating Car-Sharing Systems ». IEEE Transactions on Intelligent Transportation Systems 21, n^o 9 (septembre 2020) : 4017–30. http://dx.doi.org/10.1109/tits.2020.2995197.

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Kolleck, Aaron. « Does Car-Sharing Reduce Car Ownership ? Empirical Evidence from Germany ». Sustainability 13, n^o 13 (1 juillet 2021) : 7384. http://dx.doi.org/10.3390/su13137384.

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The sharing economy is making its way into our everyday lives. One of its business models, car-sharing, has become highly popular. Can it help us increase our sustainability? Besides emissions and vehicle miles traveled, one key aspect in the assessment regards the effect of car-sharing on car ownership. Previous studies investigating this effect have relied almost exclusively on surveys and come to very heterogeneous results, partly suggesting spectacular substitution rates between shared and private cars. This study empirically explores the impact of car-sharing on noncorporate car ownership and car markets in 35 large German cities. The analysis draws on publicly available data for the years 2012, 2013, 2015, and 2017, including, among others, the number of shared cars per operating mode (free-floating and station-based) and the number of cars owned and registered by private individuals (i.e., excluding company cars). We find that one additional station-based car is associated with a reduction of about nine private cars. We do not find a statistically significant relation between car ownership and free-floating car-sharing. Neither type of car-sharing appears to impact the markets for used and new cars significantly. Given the measurable impacts on car ownership levels, this result is surprising and invites future research to study car-sharing’s impact on the dynamics of car markets.

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Takahira, Kentaro, et Shigeo Matsubara. « Contract-based Inter-user Usage Coordination in Free-floating Car Sharing ». Proceedings of the AAAI Conference on Artificial Intelligence 35, n^o 13 (18 mai 2021) : 11361–68. http://dx.doi.org/10.1609/aaai.v35i13.17354.

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We propose a novel distributed user-car matching method based on a contract between users to mitigate the imbalance problem between vehicle distribution and demand in free-floating car sharing. Previous regulation methods involved an incentive system based on the predictions of origin-destination (OD) demand obtained from past usage history. However, the difficulty these methods have in obtaining accurate data limits their applicability. To overcome this drawback, we introduce contract-based coordination among drop-off and pick-up users in which an auction is conducted for drop-off users' intended drop-off locations. We theoretically analyze the proposed method regarding the upper bound of its efficiency. We also compare it with a baseline method and non-regulation scenario on a free-floating car-sharing simulator. The experimental results show that the proposed method achieves a higher social surplus than the existing method.

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Weikl, Simone, et Klaus Bogenberger. « Relocation Strategies and Algorithms for Free-Floating Car Sharing Systems ». IEEE Intelligent Transportation Systems Magazine 5, n^o 4 (2013) : 100–111. http://dx.doi.org/10.1109/mits.2013.2267810.

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Zhou, Wei, Haixia Wang, Victor Shi et Xiding Chen. « A Decision Model for Free-Floating Car-Sharing Providers for Sustainable and Resilient Supply Chains ». Sustainability 14, n^o 13 (4 juillet 2022) : 8159. http://dx.doi.org/10.3390/su14138159.

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For green and sustainable supply chains, transportation resilience is a critical issue. Car Sharing is an effective way to improve transportation resilience. The emerging car-sharing industry continues to attract a lot of investment, but few companies in the industry are profitable. Indeed, numerical experiments based on dynamic models in this paper showed that it was challenging for a car-sharing company to be profitable. As the numerical experiments followed the fractional factorial designs, from the factor analysis, it is suggested that a new car-sharing business first study the external business environment. Even if the external environment is sound, the company still needs to pay attention to internal operations management. Moreover, when the company decides the number of cars it owns and the fleet size, it should consider factors including variable daily expenses, maintenance costs, salvage value, and commission.

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Giordano, Danilo, Luca Vassio et Luca Cagliero. « A multi-faceted characterization of free-floating car sharing service usage ». Transportation Research Part C : Emerging Technologies 125 (avril 2021) : 102966. http://dx.doi.org/10.1016/j.trc.2021.102966.

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Daraio, Elena, Luca Cagliero, Silvia Chiusano, Paolo Garza et Danilo Giordano. « Predicting Car Availability in Free Floating Car Sharing Systems : Leveraging Machine Learning in Challenging Contexts ». Electronics 9, n^o 8 (16 août 2020) : 1322. http://dx.doi.org/10.3390/electronics9081322.

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Free-Floating Car Sharing (FFCS) services are currently available in tens of cities and countries spread all over the worlds. Depending on citizens’ habits, service policies, and road conditions, car usage profiles are rather variable and often hardly predictable. Even within the same city, different usage trends emerge in different districts and in various time slots and weekdays. Therefore, modeling car availability in FFCS systems is particularly challenging. For these reasons, the research community has started to investigate the applicability of Machine Learning models to analyze FFCS usage data. This paper addresses the problem of predicting the short-term level of availability of the FFCS service in the short term. Specifically, it investigates the applicability of Machine Learning models to forecast the number of available car within a restricted urban area. It seeks the spatial and temporal contexts in which nonlinear ML models, trained on past usage data, are necessary to accurately predict car availability. Leveraging ML has shown to be particularly effective while considering highly dynamic urban contexts, where FFCS service usage is likely to suddenly and unexpectedly change. To tailor predictive models to the real FFCS data, we study also the influence of ML algorithm, prediction horizon, and characteristics of the neighborhood of the target area. The empirical outcomes allow us to provide system managers with practical guidelines to setup and tune ML models.

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Cocca, Michele, Danilo Giordano, Marco Mellia et Luca Vassio. « Free Floating Electric Car Sharing : A Data Driven Approach for System Design ». IEEE Transactions on Intelligent Transportation Systems 20, n^o 12 (décembre 2019) : 4691–703. http://dx.doi.org/10.1109/tits.2019.2932809.

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Becker, Henrik, Francesco Ciari et Kay W. Axhausen. « Measuring the car ownership impact of free-floating car-sharing – A case study in Basel, Switzerland ». Transportation Research Part D : Transport and Environment 65 (décembre 2018) : 51–62. http://dx.doi.org/10.1016/j.trd.2018.08.003.

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Thèses sur le sujet "Free Floating Car Sharing"

Müller, Daniel. « Ride Pooling in Free Floating Car Sharing ». Thesis, KTH, Optimeringslära och systemteori, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-209928.

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Even though car sharing is already a widespread business concept and part of the shared mobility approach shaping the future of the automotive industry to some extent, the incorporation of ride pooling within the car sharing framework is not yet a fixed component of the existing models. Hence, the purpose of this thesis is to investigate the potential of including the possibility of ride sharing in terms of fleet size, cost and profit optimization for a free floating car sharing model. This is done by building three different mathematical models and extending them by certain cost parameters. One of the three mentioned models is the Ride Share Matching approach, which focuses more on the actual ride sharing process than on a real business case with respect to a company. The latter is then covered by the other two approaches, namely the Dial-a-Ride-Problem and a modification of it with better running time called the Task Graph model. Test runs on those three models with randomly generated instances show that the potential of ride sharing is undoubtedly existent.
Även om samåkning redan är en utbredd affärsidé och en del av Shared-Mobility strategin som bland annat formar bilindustrins framtid, är införandet av samåkning inom Carsharing strukturen fortfarande inte en del av de befintliga modellerna. Därför är syftet med denna studie att undersöka möjligheten att inkludera samåkning i termer av att optimera med av seende på bilpoolens storlek, kostnad och vinst. Detta uppnås genom att skapa tre olika matematiska modeller och utöka dem med vissa kostnadsparametrar. En av de tre modellerna som nämns är Ride-Share-Matching strategin som fokuserar mer på själva processen att göra en bilpool än på ett riktigt affärsscenario baserat på ett företag. Den senare täcks av de övriga två modellerna, nämligen Dial-a-Ride problemet och en modifiering med kortare körtid som vi kallar Task-Graph modell. Tester med dessa tre modeller på slumpmässigt generade instanser visar att möjligheten för samåkning otvivelaktigt existerar.
Obwohl Carsharing bereits ein weitverbreitetes Geschäftskonzept und Teil des Shared-Mobility-Ansatzes ist, der unter anderem die Zukunft der heutigen Automobilindustrie prägt, ist das Einbeziehen von Fahrgemeinschaften innerhalb des Carsharing-Gefüges noch kein fester Bestandteil der existierenden Modelle. Daher ist die Zielsetzung dieser Arbeit darauf ausgerichtet, das Potential der Möglichkeit zur Einbeziehung von Fahrgemeinschaften in Sachen Flottengrößen-, Kosten- und Gewinnoptimierung zu untersuchen. Dies wird durch das Erstellen von drei verschiedenen mathematischen Modellen und deren Erweiterung durch bestimmte Kostenparameter erreicht. Eins der drei erwähnten Modelle ist der Ride-Share- Matching-Ansatz, der sich mehr auf den tatsächlichen Vorgang des Fahrgemeinschaftbildens fokussiert als auf ein reales Geschäftsszenario anhand eines Unternehmens. Letzteres wird dann durch die anderen beiden Ansätze abgedeckt, nämlich durch das Dial-a-Ride-Problem und eine Abänderung von diesem mit besserer Laufzeit, das wir Task-Graph-Modell nennen. Testläufe mit diesen drei Modellen auf zufällig generierten Instanzen zeigen, dass das Potential von Fahrgemeinschaften zweifelsohne existiert.

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COCCA, MICHELE. « Electric Revolution in Free Floating Car Sharing : a data driven methodology for system design ». Doctoral thesis, Politecnico di Torino, 2021. http://hdl.handle.net/11583/2872345.

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Strand, Elliot, et Viktor Sandell. « The Key Value Components of a Customer Value Proposition for Free-Floating Car Sharing Services in the Nordics ». Thesis, Jönköping University, IHH, Företagsekonomi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-52829.

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A well-crafted, locally adapted customer value proposition (CVP) can aid businesses in attaining loyal customers. The main purpose of this research is to determine the key value components that should be considered for the development of a CVP, for free-floating car sharing services in the Nordic region. This is done by establishing the relationship between deductively identified value components, perceived value, satisfaction, trust, and loyalty. A research framework is proposed, where the relationships between the different constructs are hypothesised. Quantitative data is collected from existing car sharing users in the Nordic countries, through a self-administered online questionnaire, distributed through a non-probability sampling method. The empirical data is analysed through multiple regression analysis using the software SPSS, and the extension “PROCESS”, as well as additional analysis techniques to ensure data quality. The research findings indicate that perceived convenience, need fit, and a low service price positively impact both perceived value, as well as satisfaction. Satisfaction shows a stronger, positive effect on loyalty than that of perceived value, yet, loyalty is better explained when both constructs are accounted for. Additionally, trust shows to carry a mediating effect between both satisfaction and loyalty, as well as between perceived value and loyalty. Therefore, firms operating within this context should emphasise the customer needs to provide a service which is perceived as affordable and convenient. Finally, efforts should be taken to reduce uncertainty, and promote trust between the service providers, and their users.

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Pal, Aritra. « Improving Service Level of Free-Floating Bike Sharing Systems ». Scholar Commons, 2017. https://scholarcommons.usf.edu/etd/7433.

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Bike Sharing is a sustainable mode of urban mobility, not only for regular commuters but also for casual users and tourists. Free-floating bike sharing (FFBS) is an innovative bike sharing model, which saves on start-up cost, prevents bike theft, and offers significant opportunities for smart management by tracking bikes in real-time with built-in GPS. Efficient management of a FFBS requires: 1) analyzing its mobility patterns and spatio-temporal imbalance of supply and demand of bikes, 2) developing strategies to mitigate such imbalances, and 3) understanding the causes of a bike getting damaged and developing strategies to minimize them. All of these operational management problems are successfully addressed in this dissertation, using tools from Operations Research, Statistical and Machine Learning and using Share-A-Bull Bike FFBS and Divvy station-based bike sharing system as case studies.

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Reiss, Svenja [Verfasser], Klaus [Akademischer Betreuer] Bogenberger, Ralph [Akademischer Betreuer] Buehler, Klaus [Gutachter] Bogenberger et Ralph [Gutachter] Buehler. « Demand Modeling and Relocation Strategies for Free-floating Bicycle Sharing Systems / Svenja Reiss ; Gutachter : Klaus Bogenberger, Ralph Buehler ; Akademische Betreuer : Klaus Bogenberger, Ralph Buehler ; Universität der Bundeswehr München, Fakultät für Bauingenieurwesen und Umweltwissenschaften ». Neubiberg : Universitätsbibliothek der Universität der Bundeswehr München, 2017. http://d-nb.info/116553858X/34.

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Reiss, Svenja [Verfasser], Klaus Akademischer Betreuer] Bogenberger, Ralph [Akademischer Betreuer] [Buehler, Klaus [Gutachter] Bogenberger et Ralph [Gutachter] Buehler. « Demand Modeling and Relocation Strategies for Free-floating Bicycle Sharing Systems / Svenja Reiss ; Gutachter : Klaus Bogenberger, Ralph Buehler ; Akademische Betreuer : Klaus Bogenberger, Ralph Buehler ; Universität der Bundeswehr München, Fakultät für Bauingenieurwesen und Umweltwissenschaften ». Neubiberg : Universitätsbibliothek der Universität der Bundeswehr München, 2017. http://d-nb.info/116553858X/34.

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Cocca, Michele. « Electric Revolution and Free Floating Car Sharing : A Data Driven Methodology for System Design ». Doctoral thesis, 2021. http://hdl.handle.net/11583/2870025.

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Nowadays, the increase in traffic congestions, land consumption, and pollution emission due to private car ownership makes the rise of shared mobility possible. One of the most spread implementations of shared mobility is Free Floating Car Sharing (FFCS). It is a car rental model where the users can pick and release the car everywhere within an operative area. The customers can reserve (and return) the vehicle using a web-based application. With just a simple tap, the users can unlock and lock the smart vehicle. Usually, the provider bills the users only for the time spend driving, with time-minute based fares. All the other costs, like petrol, insurance, and maintenance, are in charge of the provider. This service’s flexibility fills the urban mobility gap between public transport’s relative cheapness and the comfort and capillarity of private car ownership. Indeed, FFCS allows people to travel and commute faster than the standard public bus but avoiding all the fixed and variable costs related to private car ownership. Given the recent electric cars market increase and all the benefits those vehicles carry, replacing FFCS fleet with electric-powered cars may still improve urban centers’ quality of life. The setup and management of an electric FFCS require ingenuity to minimize the users’ discomfort due to car plugging procedures. In my thesis, I present a methodology to address, in different cases of studies, all the challenges related to the conversion of combustion engine cars to electric vehicles in FFCS. In particular, my research’s main driver is to propose a methodology to build a profitable and technically sustainable system setup, able to guarantee a flexible and appealing mobility service to an increasing customer audience. In the first part of my thesis, I describe the software I developed to scrape from the web real combustion engine FFCS, from two providers: car2go and Enjoy. The car2go data collection lasted from December 2016 to January 2018, collecting more than 27 million users’ bookings spread in 23 cities. The Enjoy data collection phase started in May 2017 and lasted until June 2019, recording about 6 million bookings in 6 cities. Then, I characterize both datasets in Turin, one of the cities in which both FFCS providers work. I detect the outliers, filter them out from the dataset, and extract geotemporal users’ travel patterns. After that, I compare the car2go customer’s pattern with the one-way and two-way car-sharing system. The results show how users prefer more flexible services like FFCS or one-way car sharing. Once the data are consolidated, I develop: A methodology to place a charging station in a city by looking at users’ patterns. System policies to manage the fleet when the vehicle state of charge may not guarantee a trip. Via an event-based trace-driven simulator able to replicate the recorded trips in an electrified scenario evaluating each configuration’s feasibility. Via accurate simulation in Berlin, Milan, Turin, and Vancouver, I study different electric FFCS setup. By placing the charging station in the most frequented areas, by offering an incentive to the users to plug the car when the battery state of charge is below a safety threshold, and balancing the spread of poles, it is possible to obtain a sustainable system covering with charging station only the 8-10 % of zones. To reduce the number of charging stations to have a sustainable electric FFCS, I compare several optimization algorithms. The results show how a Genetic Algorithm can find a better solution to shrink the minimum amount of resources to sustain the same mobility demand. After that, I move my attention to the users’ rentals’ demand predictability. The main goal is to understand how different open-data sources could impact the recorded FFCS users’ rental. Initially, I compare several time-series forecasts to predict the users’ demand in the short and medium-term. Random Forest regression produces better accuracy and results in terms of interpretability. Then I correlate the socio-economics features characterizing each city neighborhood to FFCS demand, and again, the Random Forest regression outperforms other algorithms. Finally, I question the system scalability figuring out several scenarios having increasing demand. I use a model to synthesize users’ demand by looking only at the geospatial users’ rentals. By varying the electric FFCS setup and simulating the new scenario, I point out how a linear increase in the demand intensity requires a fleet sublinear increase. Finally, I project those considerations in euros, proofing how electric FFCS has room for economic growth.

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Kapourani, Eleftheria-Eleni. « Urban mobility in transition : the impact of free-floating car sharing- exemplified by the case of driveNow in Lisbon ». Master's thesis, 2018. http://hdl.handle.net/10362/39620.

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Car sharing is introduced as an innovative, technology-driven mobility service to Lisbon’s existing mobility mix, thus providing a mechanism to the growing transportation challenges the city is facing today. Urbanization and dominating motorized travel have come at economic, environmental, and social costs that car sharing aims to compensate and even reverse. The present thesis develops a theoretical framework – building on the concepts of urban mobility, automotive disruption as well as environmental pressures – to assist in gaining and improving the understanding of car sharing. Further, it discusses its implications including car ownership reduction and accompanying side effects and ultimately, necessary measures are recommended to accelerate the adoption rate of car sharing among consumers, and with that, its expected benefits.

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CHIANESE, YURI MARIA. « Optimization of profits in one-way free-floating car-sharing services, with a user-based relocation strategy that apply dynamic pricing and urban area demand defined gathering real vehicle-sensor data ». Doctoral thesis, 2019. http://hdl.handle.net/11573/1298567.

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Rapid growing in urbanization and miles driven in the city will triple urban mobility by 2050. This explosion in demand requires switching to Mobility-as-a-Service (MaaS) models, such as Car-sharing. However, a critical issue for Car-sharing one-way free-floating services is the imbalance problem that requires to solve the conflict between the positioning of vehicles “at the right place and time” and the freedom for customers to return vehicles where and when they want. To better understand the impact of the imbalance problem, we propose to use a grid partition of the served city into zones with different demand potentials. To this aim as first step of the research real data related to vehicle positions of three Car-sharing services have been collected for approximately three months in the cities of Rome, Milan, Turin and Florence (Italy). In the experimental results data of the city of Rome have been used. This part of the research focuses on analysing user behaviour by using the number of stops in selected city zones (Stop Density) and the duration of any stop (Average Stop Duration); in fact, all the stops of each vehicle belonging to any car-sharing operator, are uniquely associated and mapped to exactly one cell of the city grid representing the Urban Areas, also tracking stop start/end time and trip start/end time. This spatial association is used to calculate Stop Density and Average Stop Duration of each urban area and to map stops to specific time-slots. Consequently, in each urban area, the Urban Area Value is calculated as a function of Stop Density and Average Stop Duration belonging to the urban area; the results of this research confirm that Urban Area Value is high where high values of Stop Density and low value of Average Stop Duration occurs. Urban Areas are ranked using the Urban Area Value calculated by considering all Car-sharing services operating in the eco-system; a spatial analysis with a thermographic map of Urban Area Value allows to visualize the existence of city zones with crucial different demand potentials. The analysis derived from such Urban Area Value and from a time-slot dynamic of the Urban Areas Values themselves, that suggested to split the standard operating day in five hourly ranges, is then used to construct a flexible and dynamic pricing mathematical programming model that has been used to derive an optimal setting of tariffs and to perform a validation phase. In this model the trip fare is defined, based on a trip planning trigger, applying a bonus/malus mechanism to a basic tariff, which considers vehicle service cost, staff relocation saving and the difference of demand value between origin and destination Urban Areas. If the user desired destination is planned in an urban area which is adjoining urban areas with higher values, alternatives with lower fees are proposed. This approach is applicable, in the reality, to several Car-sharing operators and mobility-sharing aggregators such as Urbi. The model and the outcomes of Urban Area Values have been validated in a study based on real data collected in the city of Rome (Italy) during an observation period of 49 days from April 28th to June 16th, in 2016, and where 287.975 stops observation referring to 1.271 distinct vehicles have been collected. All the stops have been observed in the city of Rome whose grid representation has been partitioned in 636 cells. These results have been presented to the 2017 COMPSAC Conference, July 7th, 2017 in the Workshop “Smart Sharing Mobility in Smart Cities” 1. These data have been used to construct an integer linear programming model where only a grid of 25 cells has been considered over the same period of 49 days. The resulting model (which has 84.500 variables and 87.750 constraints) has been solved using AMPL/CPLEX and validated by simulating a trip demand over an observed period. The result of this pricing scheme seems to produce interesting results with a business applicability in urban car–sharing market. The thesis is organized as follows. Chapter 1 is focused on the analysis of main challenges of urban mobility, and the role that car-sharing systems can play. Chapters 2, 3, 4 are devoted to the introduction and a systematic review of the literature. In Chapter 5 the data collection and cleaning are described and the final Data set is presented. Chapter 6 includes the grid partition of a city and the procedure to evaluate the Urban Area Value. Chapter 7 presents a review of the up-to-date pricing models for Car sharing that are used for defining some parameters in the optimization model presented in Chapter 8. Finally, in Chapter 9 the results obtained on the available Data set for the city of Rome are presented.

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Liu, Zhu-Ming, et 劉祝銘. « A User-Based Relocation System for Free Floating Bike Sharing System ». Thesis, 2019. http://ndltd.ncl.edu.tw/handle/2k52d4.

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碩士
國立臺灣大學
土木工程學研究所
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In recent years, with people''s increased awareness of protecting the environment, more and more people choose to use public bike travel within city or as a connecting tool for other public transit modes. Since the first generation public bicycle system has been developed in 1965, the fourth generation which called Free Floating Bike Sharing (FFBS) System has been developed and exploded in Beijing in 2015 to solve the problems caused by the location and station capacity of the third generation station-based system. The emergence of so many bikes in a short time recent years can cause many problems. For instance, the free floating system allows the user to rent and return the bike almost from anywhere within the operating area, thus resulting in an imbalance distribution of bikes. In addition, the tidal flow caused by daily commuting trip can cause bikes to be concentrated and lower the turnover rate, and there have some origin or destination type regions will cause imbalance distribution of bikes as well and resulting in a low service level; therefore, a relocation system is necessary. The existing operator-based relocation system for public bike system mostly uses the operator to drive a truck to carry the bicycle to those insufficient stations. However, the characteristics of free floating makes if we only use the traditional operator-based relocation strategy, it will become economically less attractive. In response to this situation, this research attempts to alleviate the imbalance distribution of bikes by giving users an incentive bonus to “employ” users to return the bike to the near area that lack or will lack bikes. In order to construct the above-mentioned dispatching system, based on the historical trip data of Mobike which is one of the biggest free floating bike sharing company in China, this study first analyzed the temporal and spatial characteristics of the bike usage to quantify the imbalance distribution. Then, based on GPS data, spatial clustering was used to construct transit Origin-Destination matrix. Additionally, in order to predict upcoming Origin-Destination traffic volume at certain district, depending on different temporal factors, a demand model was built using back-propagation neural network (BPNN). The prediction result was used to compute the redundant bike that could be dispatched and the insufficiency at each district. Then, combined with a user participation pattern that established based on an online survey, a dispatching system that considers minimizing both the incentives payout and the total bike shortfall developed and evaluated. In the end, a case study of free floating bike sharing system was performed to show the effect of the proposed system within 3 different types of case regions. Although the result shows that the relocation effect varies by region. However, the relocation system in each region can achieve significant improvement under an affordable dispatching cost. The result proves the practicality of the user-based relocation system, and this research is also a valuable reference for the free floating bike sharing related company to optimize their bike relocation strategies.

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Chapitres de livres sur le sujet "Free Floating Car Sharing"

Rawiel, Paul. « Positioning of Pedelecs for a Pedelec Sharing System with Free-Floating Bikes ». Dans iCity. Transformative Research for the Livable, Intelligent, and Sustainable City, 51–64. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-92096-8_5.

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AbstractFor intelligent mobility concepts in growing urban environments, positioning of transportation vehicles and generally moving objects is a fundamental prerequisite. Global Navigation Satellite Systems (GNSS) are commonly used for this purpose, but especially in urban environments under certain conditions, they offer limited accuracy due to buildings, tunnels, etc. that can deviate or mask the satellite signals. The use of existing built-in sensors of the vehicle and the installation of additional sensors can be utilized to describe the movement of the vehicle independently of GNSS. This conforms to the concept of dead reckoning (DR). Both systems (GNSS and DR) can be integrated and prepared to work together since they compensate their respective weaknesses efficiently. In this study, a method to integrate different inertial sensors (gyroscope and accelerometer) and GNSS is investigated. Pedelecs usually do not have many inbuilt additional sensors like it is the case in cars; therefore, additional low-cost sensors have to be used. An extended Kalman filter (EKF) is the base of calculations to perform data integration. Driving tests are realized to check the performance of the integration model. The results show that positioning in situations where GNSS data is not available can be done through dead reckoning for a short period of time. The weak point hereby is the calibration of the accelerometer. Inaccurate accelerometer data cause increasing inaccuracy of the position due to the double integration of the acceleration over time.

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Herrmann, Sascha, Frederik Schulte et Stefan Voß. « Increasing Acceptance of Free-Floating Car Sharing Systems Using Smart Relocation Strategies : A Survey Based Study of car2go Hamburg ». Dans Lecture Notes in Computer Science, 151–62. Cham : Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11421-7_10.

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Heitz, Christoph, Roman Etschmann, Raoul Stoeckle, Thomas Bachmann et Matthias Templ. « User-Based Redistribution in Free-Floating Bike Sharing Systems ». Dans Operations Research Proceedings, 555–62. Cham : Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18500-8_69.

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Sun, Shouheng, et Myriam Ertz. « Environmental Impact of Free-Floating Bike Sharing : From Life Cycle Perspective ». Dans Handbook of Solid Waste Management, 2255–80. Singapore : Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-4230-2_88.

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Sun, Shouheng, et Myriam Ertz. « Environmental Impact of Free-Floating Bike Sharing : From Life Cycle Perspective ». Dans Handbook of Solid Waste Management, 1–26. Singapore : Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-7525-9_88-1.

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Caggiani, Leonardo, Michele Ottomanelli, Rosalia Camporeale et Mario Binetti. « Spatio-temporal Clustering and Forecasting Method for Free-Floating Bike Sharing Systems ». Dans Advances in Intelligent Systems and Computing, 244–54. Cham : Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48944-5_23.

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Heitz, Christoph, Marc Blume, Corinne Scherrer, Raoul Stöckle et Thomas Bachmann. « Designing Value Co-creation for a Free-Floating e-Bike-Sharing System ». Dans Smart Service Systems, Operations Management, and Analytics, 113–25. Cham : Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30967-1_11.

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Chen, Tzu-Hao, Chit-Jie Chew, Ying-Chin Chen et Jung-San Lee. « Preserving Collusion-Free and Traceability in Car-Sharing System Based on Blockchain ». Dans Communications in Computer and Information Science, 613–24. Singapore : Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-9582-8_54.

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Liang, Wenjia, Jianru Hao et Liguo Zhang. « Travel Behavior Analysis for Free-Floating Bike Sharing Systems Based on Markov-Chain Models ». Dans Positive Systems, 127–45. Cham : Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-04327-8_11.

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Neijmeijer, Nout, Frederik Schulte, Kevin Tierney, Henk Polinder et Rudy R. Negenborn. « Dynamic Pricing for User-Based Rebalancing in Free-Floating Vehicle Sharing : A Real-World Case ». Dans Lecture Notes in Computer Science, 443–56. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59747-4_29.

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Actes de conférences sur le sujet "Free Floating Car Sharing"

Dmitrienko, Alexandra, et Christian Plappert. « Secure Free-Floating Car Sharing for Offline Cars ». Dans CODASPY '17 : Seventh ACM Conference on Data and Application Security and Privacy. New York, NY, USA : ACM, 2017. http://dx.doi.org/10.1145/3029806.3029807.

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Weikl, S., et K. Bogenberger. « Relocation strategies and algorithms for free-floating Car Sharing Systems ». Dans 2012 15th International IEEE Conference on Intelligent Transportation Systems - (ITSC 2012). IEEE, 2012. http://dx.doi.org/10.1109/itsc.2012.6338869.

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Martin, Gregory, Matthieu Donain, Elisa Fromont, Tias Guns, Laurence Roze et Alexandre Termier. « Prediction-Based Fleet Relocation for Free Floating Car Sharing Services ». Dans 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2021. http://dx.doi.org/10.1109/ictai52525.2021.00187.

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Iwata, Yuto, et Shigeo Matsubara. « Usage Coordination Utilizing Flexible Contracts in Free-floating Car Sharing ». Dans 2021 IEEE International Conference on Agents (ICA). IEEE, 2021. http://dx.doi.org/10.1109/ica54137.2021.00010.

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Kypriadis, Damianos, Charalampos Konstantopoulos, Grammati Pantziou et Damianos Gavalas. « An Efficient Scheme for Dynamic Car Relocation in Free-Floating Car-Sharing Systems ». Dans 2019 IEEE International Smart Cities Conference (ISC2). IEEE, 2019. http://dx.doi.org/10.1109/isc246665.2019.9071681.

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Kypriadis, Damianos, Grammati Pantziou, Charalampos Konstantopoulos et Damianos Gavalas. « Minimum Walking Static Repositioning in Free-Floating Electric Car-Sharing Systems ». Dans 2018 21st International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2018. http://dx.doi.org/10.1109/itsc.2018.8569912.

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Ollora Zaballa, Eder. « Exploiting free-floating car sharing rewards to support a free year of daily commute ». Dans 2020 IEEE 20th Mediterranean Electrotechnical Conference ( MELECON). IEEE, 2020. http://dx.doi.org/10.1109/melecon48756.2020.9140671.

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Cocca, Michele, Danilo Giordano, Marco Mellia et Luca Vassio. « Free Floating Electric Car Sharing in Smart Cities : Data Driven System Dimensioning ». Dans 2018 IEEE International Conference on Smart Computing (SMARTCOMP). IEEE, 2018. http://dx.doi.org/10.1109/smartcomp.2018.00088.

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Formentin, Simone, Andrea G. Bianchessi et Sergio M. Savaresi. « On the prediction of future vehicle locations in free-floating car sharing systems ». Dans 2015 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2015. http://dx.doi.org/10.1109/ivs.2015.7225816.

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Cocca, Michele, Danilo Giordano, Marco Mellia et Luca Vassio. « Data Driven Optimization of Charging Station Placement for EV Free Floating Car Sharing ». Dans 2018 21st International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2018. http://dx.doi.org/10.1109/itsc.2018.8569256.

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