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Автор: Grafiati

Опубліковано: 14 березня 2023

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1

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

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2

Kypriadis, Damianos, Grammati Pantziou, Charalampos Konstantopoulos, and Damianos Gavalas. "Optimizing Relocation Cost in Free-Floating Car-Sharing Systems." IEEE Transactions on Intelligent Transportation Systems 21, no. 9 (September 2020): 4017–30. http://dx.doi.org/10.1109/tits.2020.2995197.

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3

Kolleck, Aaron. "Does Car-Sharing Reduce Car Ownership? Empirical Evidence from Germany." Sustainability 13, no. 13 (July 1, 2021): 7384. http://dx.doi.org/10.3390/su13137384.

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Анотація:

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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4

Takahira, Kentaro, and Shigeo Matsubara. "Contract-based Inter-user Usage Coordination in Free-floating Car Sharing." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 13 (May 18, 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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5

Weikl, Simone, and Klaus Bogenberger. "Relocation Strategies and Algorithms for Free-Floating Car Sharing Systems." IEEE Intelligent Transportation Systems Magazine 5, no. 4 (2013): 100–111. http://dx.doi.org/10.1109/mits.2013.2267810.

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6

Zhou, Wei, Haixia Wang, Victor Shi, and Xiding Chen. "A Decision Model for Free-Floating Car-Sharing Providers for Sustainable and Resilient Supply Chains." Sustainability 14, no. 13 (July 4, 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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7

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

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8

Daraio, Elena, Luca Cagliero, Silvia Chiusano, Paolo Garza, and Danilo Giordano. "Predicting Car Availability in Free Floating Car Sharing Systems: Leveraging Machine Learning in Challenging Contexts." Electronics 9, no. 8 (August 16, 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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9

Cocca, Michele, Danilo Giordano, Marco Mellia, and Luca Vassio. "Free Floating Electric Car Sharing: A Data Driven Approach for System Design." IEEE Transactions on Intelligent Transportation Systems 20, no. 12 (December 2019): 4691–703. http://dx.doi.org/10.1109/tits.2019.2932809.

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10

Becker, Henrik, Francesco Ciari, and 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 (December 2018): 51–62. http://dx.doi.org/10.1016/j.trd.2018.08.003.

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11

Acheampong, Ransford A., and Alhassan Siiba. "Modelling the determinants of car-sharing adoption intentions among young adults: the role of attitude, perceived benefits, travel expectations and socio-demographic factors." Transportation 47, no. 5 (July 12, 2019): 2557–80. http://dx.doi.org/10.1007/s11116-019-10029-3.

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Abstract Major cities in developing countries are increasingly becoming motorized. Thus, effective solutions to address the negative impacts that come with rising car-ownership are needed as part of an overall travel demand management strategy. In developed and emerging economies, shared-mobility in the form of car-sharing is becoming popular as potentially low-cost and environmentally sustainable alternative to car-ownership. Yet, our understanding of car-sharing adoption and diffusion factors in developing countries is limited. In this study, we fill this gap by examining car-sharing adoption intentions among young adults aged between 18 and 35 years in Ghana, Sub-Saharan Africa. Using structural equation modelling, we model car-sharing adoption intentions based on a framework that integrates individuals’ perception of the benefits of car-sharing, attitudes towards the environment and technology, trust of stewardship in car-sharing, perception of innovativeness of car-sharing, travel expectations and socio-demographic factors. We found that pro-technology and pro-environmental attitudes correlate positively with perceived benefits of car-sharing. Perceived benefits of car-sharing, in turn, has the largest predictive effect on intentions to car-share. Other factors, including individuals’ previous experience using Uber on-demand taxi services, gender, education, driver’s licensure and expectation of comfortable and fast travel options, all predict car-sharing adoption intentions. While there exists an interest in both station-based and free-floating car-sharing services, more of the would-be users favour the latter than the former. Also, majority of the potential adopters (62%) would join a car-sharing service within the first 1 year of its introduction. An important finding is that dissatisfaction with existing public transit services underpins car-sharing intentions, implying that relying on car-sharing alone to meet travel needs, without a holistic strategy of providing quality and affordable public transit services, could lead to unsustainable outcomes.

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12

Schiffer, Maximilian, Gerhard Hiermann, Fabian Rüdel, and Grit Walther. "A polynomial-time algorithm for user-based relocation in free-floating car sharing systems." Transportation Research Part B: Methodological 143 (January 2021): 65–85. http://dx.doi.org/10.1016/j.trb.2020.11.001.

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13

Guirao, Begoña, Rafael Molina-Sánchez, Armando Ortuño, and Daniel Gálvez-Pérez. "Integration of Free Floating Car Sharing Systems in Rail Stations: A Web Based Data Analysis." Future Transportation 1, no. 1 (April 9, 2021): 38–53. http://dx.doi.org/10.3390/futuretransp1010004.

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In the last decades, car sharing has been a tool for city planners to reduce private car traffic and pollution in big urban areas. The emergence of the ICTs (Information and Communication Technologies), together with the development of the collaborative economy, has allowed for the birth of the new Free-Floating Carsharing (FFCS): A more flexible type of carsharing, in which electric cars can be used. Little research has been devoted using real FFCS flows data, to the FFCS impacts on user behavior and even on the public transport system thus far. Furthermore, in big metropolitan areas, central rail stations should promote modal interchanges, including new modes of electric FFCS systems. The aim of this paper is to design a web-based platform to collect and analyze FFCS demand on the surrounding areas of rail stations and makes a proposal to provide these systems with electrical recharging energy obtained from the regenerative braking of high-speed trains. This case study includes Atocha and Chamartín Central Stations in Madrid (Spain). Scientific evidence shows a high demand of FFCS cars at central rail stations and a trip profile with a short time duration linked to the closest districts of rail stations.

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14

Zhang, Chen, Jie He, Ziyang Liu, Lu Xing, and Yinhai Wang. "Travel demand and distance analysis for free-floating car sharing based on deep learning method." PLOS ONE 14, no. 10 (October 16, 2019): e0223973. http://dx.doi.org/10.1371/journal.pone.0223973.

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15

Becker, Henrik, Francesco Ciari, and Kay W. Axhausen. "Modeling free-floating car-sharing use in Switzerland: A spatial regression and conditional logit approach." Transportation Research Part C: Emerging Technologies 81 (August 2017): 286–99. http://dx.doi.org/10.1016/j.trc.2017.06.008.

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16

Kopp, Johanna, Regine Gerike, and Kay W. Axhausen. "Do sharing people behave differently? An empirical evaluation of the distinctive mobility patterns of free-floating car-sharing members." Transportation 42, no. 3 (April 4, 2015): 449–69. http://dx.doi.org/10.1007/s11116-015-9606-1.

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17

Cocca, Michele, Douglas Teixeira, Luca Vassio, Marco Mellia, Jussara M. Almeida, and Ana Paula Couto da Silva. "On Car-Sharing Usage Prediction with Open Socio-Demographic Data." Electronics 9, no. 1 (January 1, 2020): 72. http://dx.doi.org/10.3390/electronics9010072.

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Анотація:

Free-Floating Car-Sharing (FFCS) services are a flexible alternative to car ownership. These transportation services show highly dynamic usage both over different hours of the day, and across different city areas. In this work, we study the problem of predicting FFCS demand patterns—a problem of great importance to the adequate provisioning of the service. We tackle both the prediction of the demand (i) over time and (ii) over space. We rely on months of real FFCS rides in Vancouver, which constitute our ground truth. We enrich this data with detailed socio-demographic information obtained from large open-data repositories to predict usage patterns. Our aim is to offer a thorough comparison of several machine-learning algorithms in terms of accuracy and ease of training, and to assess the effectiveness of current state-of-the-art approaches to address the prediction problem. Our results show that it is possible to predict the future usage with relative errors down to 10%, while the spatial prediction can be estimated with relative errors of about 40%. Our study also uncovers the socio-demographic features that most strongly correlate with FFCS usage, providing interesting insights for providers interested in offering services in new regions.

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18

Schulte, Frederik, and Stefan Voß. "Decision Support for Environmental-friendly Vehicle Relocations in Free- Floating Car Sharing Systems: The Case of Car2go." Procedia CIRP 30 (2015): 275–80. http://dx.doi.org/10.1016/j.procir.2015.02.090.

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19

Li, Qing, Feixiong Liao, Harry J. P. Timmermans, Haijun Huang, and Jing Zhou. "Incorporating free-floating car-sharing into an activity-based dynamic user equilibrium model: A demand-side model." Transportation Research Part B: Methodological 107 (January 2018): 102–23. http://dx.doi.org/10.1016/j.trb.2017.11.011.

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20

Mattia, Giovanni, Roberta Guglielmetti Mugion, and Ludovica Principato. "Shared mobility as a driver for sustainable consumptions: The intention to re-use free-floating car sharing." Journal of Cleaner Production 237 (November 2019): 117404. http://dx.doi.org/10.1016/j.jclepro.2019.06.235.

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21

Fauser, J., N. Sigle, and D. Hertweck. "DATA-BASED APPLICATION SCENARIOS FOR E-SCOOTERS." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences VIII-4/W1-2021 (September 3, 2021): 41–47. http://dx.doi.org/10.5194/isprs-annals-viii-4-w1-2021-41-2021.

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Abstract. In various German cities free-floating e-scooter sharing is an upcoming trend in e-mobility. Trends such as climate change, urbanization, demographic change, amongst others are arising and forces the society to develop new mobility solutions. Contrasting the more scientifically explored car sharing, the usage patterns and behaviors of e-scooter sharing customers still need to be analyzed. This presumably enables a better addressing of customers as well as adaptions of the business model to increase scooter utilization and therefore the profit of the e-scooter providers. The customer journey is digitally traceable from registration to scooter reservation and the ride itself. These data enable to identifies customer needs and motivations. We analyzed a dataset from 2017 to 2019 of an e-scooter sharing provider operating in a big German city. Based on the datasets we propose a customer clustering that identifies three different customer segments, enabling to draw multiple conclusions for the business development and improving the problem-solution fit of the e-scooter sharing model.

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22

Haustein, Sonja. "What role does free-floating car sharing play for changes in car ownership? Evidence from longitudinal survey data and population segments in Copenhagen." Travel Behaviour and Society 24 (July 2021): 181–94. http://dx.doi.org/10.1016/j.tbs.2021.04.003.

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23

Guyon, Olivier, Dominique Millet, Julien Garcia, Manuele Margni, Sophie Richet, and Nicolas Tchertchian. "Prioritisation of modelling parameters of a free-floating car sharing system according to their sensitivity to the environmental impacts." Journal of Cleaner Production 296 (May 2021): 126081. http://dx.doi.org/10.1016/j.jclepro.2021.126081.

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24

Borghetti, Fabio, Simona Briancesco, Michela Longo, Roberto Maja, and Dario Zaninelli. "From Traditional to Electric Free-Floating Car Sharing: Application and Case Study in the City of Milan in Italy." Transportation Research Procedia 60 (2022): 456–63. http://dx.doi.org/10.1016/j.trpro.2021.12.059.

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25

Firnkorn, Jörg, and Martin Müller. "What will be the environmental effects of new free-floating car-sharing systems? The case of car2go in Ulm." Ecological Economics 70, no. 8 (June 2011): 1519–28. http://dx.doi.org/10.1016/j.ecolecon.2011.03.014.

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26

Xu, Dandan, Yang Bian, and Shinan Shu. "Research on the Psychological Model of Free-floating Bike-Sharing Using Behavior: A Case Study of Beijing." Sustainability 12, no. 7 (April 8, 2020): 2977. http://dx.doi.org/10.3390/su12072977.

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As a clean, sustainable transport tool, bicycles have significant advantages in short-distance travel. Despite many efforts assumed in Beijing to improve the cycling environment, the popularity of cycling remains relatively low. However, the advent of the free-floating bike-sharing (FFBS) system has engendered an unexpected cycling enthusiasm in Beijing. Therefore, it is of great importance to delve into why travelers prefer FFBS as a transportation form from a psychological perspective. In this paper, 352 valid questionnaires were collected from an online survey, and an extended theory of planned behavior (TPB) was adopted to examine the psychological determinants of intention and actual behavior to use FFBS. The results showed that men and car-owners prefer vehicles and show a lower willingness to use FFBS. In contrast, residents under the age of 60, residents with FFBS riding experience, and residents skilled in cycling are inclined to use FFBS; the economic convenience of FFBS is the most important attractant for FFBS, while bad weather is the biggest hindrance factor for residents to use FFBS; however, imperfection in infrastructure has no significant impact on reducing residents’ willingness to use FFBS. These results have important implications for planners to better understand the FFBS use behavior, and several suggestions are proposed to support the policymaking about FFBS.

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27

Sprei, Frances, Shiva Habibi, Cristofer Englund, Stefan Pettersson, Alex Voronov, and Johan Wedlin. "Free-floating car-sharing electrification and mode displacement: Travel time and usage patterns from 12 cities in Europe and the United States." Transportation Research Part D: Transport and Environment 71 (June 2019): 127–40. http://dx.doi.org/10.1016/j.trd.2018.12.018.

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28

Oldenbroek, Vincent, Gilbert Smink, Tijmen Salet, and Ad J. M. van Wijk. "Fuel Cell Electric Vehicle as a Power Plant: Techno-Economic Scenario Analysis of a Renewable Integrated Transportation and Energy System for Smart Cities in Two Climates." Applied Sciences 10, no. 1 (December 23, 2019): 143. http://dx.doi.org/10.3390/app10010143.

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Renewable, reliable, and affordable future power, heat, and transportation systems require efficient and versatile energy storage and distribution systems. If solar and wind electricity are the only renewable energy sources, what role can hydrogen and fuel cell electric vehicles (FCEVs) have in providing year-round 100% renewable, reliable, and affordable energy for power, heat, and transportation for smart urban areas in European climates? The designed system for smart urban areas uses hydrogen production and FCEVs through vehicle-to-grid (FCEV2G) for balancing electricity demand and supply. A techno-economic analysis was done for two technology development scenarios and two different European climates. Electricity and hydrogen supply is fully renewable and guaranteed at all times. Combining the output of thousands of grid-connected FCEVs results in large overcapacities being able to balance large deficits. Self-driving, connecting, and free-floating car-sharing fleets could facilitate vehicle scheduling. Extreme peaks in balancing never exceed more than 50% of the available FCEV2G capacity. A simple comparison shows that the cost of energy for an average household in the Mid Century scenario is affordable: 520–770 €/year (without taxes and levies), which is 65% less compared to the present fossil situation. The system levelized costs in the Mid Century scenario are 71–104 €/MWh for electricity and 2.6–3.0 €/kg for hydrogen—and we expect that further cost reductions are possible.

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29

Lenfers, Ulfia Annette, Nima Ahmady-Moghaddam, Daniel Glake, Florian Ocker, Jonathan Ströbele, and Thomas Clemen. "Incorporating Multi-Modal Travel Planning into an Agent-Based Model: A Case Study at the Train Station Kellinghusenstraße in Hamburg." Land 10, no. 11 (November 3, 2021): 1179. http://dx.doi.org/10.3390/land10111179.

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Models can provide valuable decision support in the ongoing effort to create a sustainable and effective modality mix in urban settings. Modern transportation infrastructures must meaningfully combine public transport with other mobility initiatives such as shared and on-demand systems. The increase of options and possibilities in multi-modal travel implies an increase in complexity when planning and implementing such an infrastructure. Multi-agent systems are well-suited for addressing questions that require an understanding of movement patterns and decision processes at the individual level. Such models should feature intelligent software agents with flexible internal logic and accurately represent the core functionalities of new modalities. We present a model in which agents can choose between owned modalities, station-based bike sharing modalities, and free-floating car sharing modalities as they exit the public transportation system and seek to finish their personal multi-modal trip. Agents move on a multi-modal road network where dynamic constraints in route planning are evaluated based on an agent’s query. Modality switch points (MSPs) along the route indicate the locations at which an agent can switch from one modality to the next (e.g., a bike rental station to return a used rental bike and continue on foot). The technical implementation of MSPs within the road network was a central focus in this work. To test their efficacy in a controlled experimental setting, agents optimized only the travel time of their multi-modal routes. However, the functionalities of the model enable the implementation of different optimization criteria (e.g., financial considerations or climate neutrality) and unique agent preferences as well. Our findings show that the implemented MSPs enable agents to switch between modalities at any time, allowing for the kind of versatile, individual, and spontaneous travel that is common in modern multi-modal settings.

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30

Niels, Tanja, and Klaus Bogenberger. "Booking Behavior of Free-Floating Carsharing Users." Transportation Research Record: Journal of the Transportation Research Board 2650, no. 1 (January 2017): 123–32. http://dx.doi.org/10.3141/2650-15.

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In recent years, the services of free-floating carsharing (FFCS) systems have gained popularity, especially in urban areas. Some FFCS operators are now including battery electric vehicles (BEVs) in their fleets. This addition is especially beneficial for cities and their inhabitants, as BEVs are locally emission free and move more quietly. However, how FFCS customers react to the new technology has not yet been empirically analyzed. By combining the app call and booking data of an FFCS operator in Munich, Germany, this study assessed the preferences of customers when booking a specific car and evaluated the attractiveness of the fleet’s BEVs. With the consideration of the spatial availability encountered when the mobile phone app was opening, the study found that the distance to the nearest available car was the most important decision criterion for customers. If a car had been available within 200 m, the probability that customers booked a car would have been twice as great compared with the case that the nearest vehicle had been 500 m away (100 m ≈ 0.06 mi). In addition, the majority of the customers booked the nearest available car. Nevertheless, certain qualities of vehicles made users walk to a car that was farther away. The study identified popular vehicle models and found that customers preferred to use the fleet’s BEVs, independent of the battery level. BEVs were used for almost the same distances as internal combustion engine vehicles and were suitable for most use cases: more than 80% of the FFCS trips covered a distance of at most 20 km (≈12.4 mi).

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31

Duz, Alessandra, and Matteo Corno. "Flexible Pricing Strategies in Electric Free-Floating Bicycle Sharing." IEEE Access 9 (2021): 152972–83. http://dx.doi.org/10.1109/access.2021.3127568.

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32

Zhang, Wenbin, Xiaolei Niu, Guangyong Zhang, and Lixin Tian. "Dynamic Rebalancing of the Free-Floating Bike-Sharing System." Sustainability 14, no. 20 (October 19, 2022): 13521. http://dx.doi.org/10.3390/su142013521.

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In the paper, we propose a novel method to analyze the rebalancing of the free-floating bike-sharing system. First, we construct a visualization method to analyze the rebalancing of the system. Then, for the first time, we set up a coarse-grained way to study dynamics rebalancing during rush hours. Finally, we complete the empirical analysis with the real-time cycling data of the Nanjing Mobike Sharing-bike Company. The results show that: static rebalancing is weak, and dynamic rebalancing during rush hours is serious. Therefore, increasing the number of shared bikes in parking spots can ease the rebalancing. At the same time, we find that commuting to and from work is not the main factor that constitutes the rebalancing of the free-floating bike-sharing system, though the rebalancing is proportional to travel frequency.

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33

Sun, Xiaojie, Wansheng Tang, Jing Chen, and Jianxiong Zhang. "Optimal investment strategy of a free-floating sharing platform." Transportation Research Part E: Logistics and Transportation Review 138 (June 2020): 101958. http://dx.doi.org/10.1016/j.tre.2020.101958.

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34

Roblek, Vasja, Maja Meško, and Iztok Podbregar. "Impact of Car Sharing on Urban Sustainability." Sustainability 13, no. 2 (January 18, 2021): 905. http://dx.doi.org/10.3390/su13020905.

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The article gives us an insight into the key issues of car sharing and its impact on urban sustainability. A selection of 314 articles published in peer-reviewed journals from the Scopus database were analysed using Leximancer 5.0 for Automated Content analysis. A total of seven themes were identified explaining the researched topic of the car sharing situation in Europe, which are sharing, economy, model, systems, electrical car sharing, policy and travel. There are two ways of sharing owned cars in Europe; access to cars from the fleet of private organisations and P2P car sharing. Sustainable environmental solutions in the context of the electrification of cars are used. Car sharing usually takes place online and can be free or for a fee as defined by The European Economic and Social Committee. The article provides an overview of understanding the concept of urban car sharing in Europe.

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Cai, Yutong, Ghim Ping Ong, and Qiang Meng. "Bicycle sharing station planning: From free-floating to geo-fencing." Transportation Research Part C: Emerging Technologies 147 (February 2023): 103990. http://dx.doi.org/10.1016/j.trc.2022.103990.

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De Bartolomeo, Simona, Leonardo Caggiani, and Michele Ottomanelli. "An equity indicator for free-floating electric vehicle-sharing systems." Transportation Research Procedia 69 (2023): 115–22. http://dx.doi.org/10.1016/j.trpro.2023.02.152.

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Arbeláez Vélez, Ana María, and Andrius Plepys. "Car Sharing as a Strategy to Address GHG Emissions in the Transport System: Evaluation of Effects of Car Sharing in Amsterdam." Sustainability 13, no. 4 (February 23, 2021): 2418. http://dx.doi.org/10.3390/su13042418.

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Shared mobility options, such as car sharing, are often claimed to be more sustainable, although evidence at an individual or city level may contradict these claims. This study aims to improve understanding of the effects of car sharing on transport-related emissions at an individual and city level. This is done by quantifying the greenhouse gas (GHG) emissions of the travel habits of individuals before and after engaging with car sharing. The analysis uses a well-to-wheel (WTW) approach, including both business-to-consumer (B2C) and peer-to-peer (P2P) car-sharing fleets. Changes in GHG emissions after engaging in car sharing vary among individuals. Transport-related GHG emissions caused by car-free individuals tend to increase after they engage in car sharing, while emissions caused by previous car owners tend to fall. At the city level, GHG emissions savings can be achieved by using more efficient cars in sharing systems and by implementing greener mobility policies. Changes in travel habits might help to reduce GHG emissions, providing individuals migrate to low-carbon transport modes. The findings can be used to support the development and implementation of transport policies that deter car ownership and support shared mobility solutions that are integrated in city transport systems.

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Wielinski, Grzegorz, Martin Trépanier, and Catherine Morency. "Electric and hybrid car use in a free-floating carsharing system." International Journal of Sustainable Transportation 11, no. 3 (August 12, 2016): 161–69. http://dx.doi.org/10.1080/15568318.2016.1220653.

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Kubik, Andrzej, Katarzyna Turoń, Piotr Folęga, and Feng Chen. "CO2 Emissions—Evidence from Internal Combustion and Electric Engine Vehicles from Car-Sharing Systems." Energies 16, no. 5 (February 24, 2023): 2185. http://dx.doi.org/10.3390/en16052185.

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Car-sharing services are developing at an ever-increasing pace. Taking into account the reduction of carbon dioxide emissions and pursuit of the sustainable development of transport, implementing electric cars in car-sharing fleets is being proposed. On the one hand, these types of vehicles are referred to as emission-free, but on the other hand, their environmental friendliness is questionable due to the emission of carbon dioxide during the production of energy to power them. Although many scientific papers are devoted to the issue of reducing emissions through car sharing, there is a research gap concerning the real production of carbon dioxide by car-sharing vehicles during car-sharing trips. To fill this research gap, the objective of the article was to analyze the actual level of carbon dioxide emissions from combustion and electric vehicles from car-sharing systems produced when renting rides. The test results showed that the electric car turned out to be significantly less emitting. The use of electric vehicles in car-sharing fleets can reduce carbon dioxide emissions from 14% to 65% compared to using cars with internal combustion engines. However, the key role during car-sharing trips is played by the driving style of the drivers, which has been omitted from the literature to date. This should be properly regulated by service providers and focus on the proper use of energy from electric vehicle batteries, especially at low temperatures. The article provides support for operators planning to modernize their fleet of vehicles and fills the research gap concerning car-sharing emissions.

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40

Sun, Shouheng, and Myriam Ertz. "Contribution of bike-sharing to urban resource conservation: The case of free-floating bike-sharing." Journal of Cleaner Production 280 (January 2021): 124416. http://dx.doi.org/10.1016/j.jclepro.2020.124416.

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Zhao, De, and Ghim Ping Ong. "Geo-fenced parking spaces identification for free-floating bicycle sharing system." Transportation Research Part A: Policy and Practice 148 (June 2021): 49–63. http://dx.doi.org/10.1016/j.tra.2021.03.007.

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Poliziani, Cristian, Joerg Schweizer, and Federico Rupi. "Supply and Demand Analysis of a Free Floating Bike Sharing System." Communications - Scientific letters of the University of Zilina 24, no. 2 (April 1, 2022): A53—A65. http://dx.doi.org/10.26552/com.c.2022.2.a53-a65.

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43

Cheng, Xi, and Yang Gao. "The Optimal Monthly Strategy Pricing of Free-Floating Bike Sharing Platform." Modern Economy 09, no. 02 (2018): 318–38. http://dx.doi.org/10.4236/me.2018.92021.

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Li, Xuefeng, Yong Zhang, Li Sun, and Qiyang Liu. "Free-Floating Bike Sharing in Jiangsu: Users’ Behaviors and Influencing Factors." Energies 11, no. 7 (June 26, 2018): 1664. http://dx.doi.org/10.3390/en11071664.

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45

Binetti, Mario, Leonardo Caggiani, Rosalia Camporeale, and Michele Ottomanelli. "A Sustainable Crowdsourced Delivery System to Foster Free-Floating Bike-Sharing." Sustainability 11, no. 10 (May 15, 2019): 2772. http://dx.doi.org/10.3390/su11102772.

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Since bicycles and bike-sharing systems are becoming increasingly important in modern transportation contexts, we suggest in this paper an alternative method to incorporate cycling among the freight transport alternatives within urban areas. We propose pursuing a sustainable initiative of crowdsourced delivery where some of the urban good deliveries may be voluntarily undertaken by users of the free-floating bike-sharing systems while following their prefixed route in exchange for some kind of reward. We believe that a network design model that allows properly allocating the resources of the bike-shared mobility service could improve the potential of crowdshipping, making it a viable support and supplement for the local postal services, and more easily accepted and adopted in urban contexts. An application to a case study has been embodied to show the effectiveness and advantages of our proposal.

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46

Guidon, Sergio, Henrik Becker, Horace Dediu, and Kay W. Axhausen. "Electric Bicycle-Sharing: A New Competitor in the Urban Transportation Market? An Empirical Analysis of Transaction Data." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 4 (March 24, 2019): 15–26. http://dx.doi.org/10.1177/0361198119836762.

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Electric bicycles (e-bikes) are a new addition to bicycle-sharing and may improve its competitiveness. E-bikes allow for higher speeds at a higher level of comfort than conventional bicycles and, compared with traditional bicycle-sharing, e-bike-sharing is better positioned to complement or compete with existing public transportation, or to even challenge established taxi services. In this paper, eight months of transaction data from a free-floating e-bike-sharing system in Zürich, Switzerland, were used to study the market position of e-bike sharing and drivers of demand. The results of the analysis indicate that a large proportion of the trips are commuting, and that the distance range of e-bike-sharing trips overlaps with the distance ranges of traditional public transportation and taxi services. Intensity of use is sensitive to precipitation. Spatial regression modeling indicates that economic and social activity, public transportation service quality, and the availability of bicycle infrastructure are key drivers of demand for free-floating e-bike-sharing. Given the substantial differences in the service compared with traditional bicycle-sharing, a new fifth generation of bicycle-sharing schemes is proposed.

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An, Jingrui, Linghao Zhang, Rui Li, and Yingjie Victor Chen. "Collaborative governance in the sharing economy. A case of free-floating bicycle sharing with visualized analyzation." Design Journal 22, sup1 (April 1, 2019): 777–88. http://dx.doi.org/10.1080/14606925.2019.1595399.

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Pal, Aritra, and Yu Zhang. "Free-floating bike sharing: Solving real-life large-scale static rebalancing problems." Transportation Research Part C: Emerging Technologies 80 (July 2017): 92–116. http://dx.doi.org/10.1016/j.trc.2017.03.016.

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Cheng, Long, Junjian Yang, Xuewu Chen, Mengqiu Cao, Hang Zhou, and Yu Sun. "How could the station-based bike sharing system and the free-floating bike sharing system be coordinated?" Journal of Transport Geography 89 (December 2020): 102896. http://dx.doi.org/10.1016/j.jtrangeo.2020.102896.

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Ma, Yuge, Jing Lan, Thomas Thornton, Diana Mangalagiu, and Dajian Zhu. "Challenges of collaborative governance in the sharing economy: The case of free-floating bike sharing in Shanghai." Journal of Cleaner Production 197 (October 2018): 356–65. http://dx.doi.org/10.1016/j.jclepro.2018.06.213.

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