Identifying Users and Use of (Electric-) Free-Floating Carsharing in Berlin and Munich

Mueller, Johannes; Schmoeller, S; Giesel, F.

doi:10.1109/itsc.2015.413

Cited by 8 publications

(5 citation statements)

References 10 publications

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“…As has been said already, the authors see the study from Seign as a reason for transferring socio-demographic characteristics of the district to the users. This conclusion is confirmed by the findings of Mueller et al [31] who present the results of surveys from onboard units of the vehicles in which users were asked to tell the purpose of their trip: Most of the costumers use carsharing for their trip back home.…”

Section: Interpretation Of the Variables' Effectsupporting

confidence: 65%

An Explanatory Model Approach for the Spatial Distribution of Free-Floating Carsharing Bookings: A Case-Study of German Cities

2017

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Abstract:When the first free-floating carsharing operators launched their business, they did not know if it would be profitable. They often started in highly populated cities without performing extensive target group analysis, and were less concerned about fleet management. Usually, there are two main datasets that can be used to find areas that would have a high demand for free-floating carsharing: booking data, for measuring the actual demand; and land use and census data for describing the activities performed in different areas in a city. In this paper, we aim to use this information to help predict the demand of free-floating carsharing systems. We use booking data provided by DriveNow for Berlin in 2014 and contextual information about the type of activity each neighborhood has. Using Berlin as a case study, we apply a negative binomial statistical model to explain the number of bookings. From the results, we conclude that free-floating carsharing is predominantly successful in areas with more affluent citizens who are open to trying new and sustainable technologies. Other important determinants that result in a high number of carsharing bookings are the area's centrality and parking lot availability. The statistical model for Berlin was then transferred to Munich and Cologne, two other cities in Germany with similar population sizes. A comparison between the estimated demand categories and actual bookings shows satisfying results, but also non-negligible local conditions influencing the spatial demand for bookings.

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Section: Interpretation Of the Variables' Effectsupporting

confidence: 65%

An Explanatory Model Approach for the Spatial Distribution of Free-Floating Carsharing Bookings: A Case-Study of German Cities

2017

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“…For example, in 2008, the bike sharing system Vélib' in Paris launched a discount pricing strategy [31] to motivate users to return bikes to uphill stations. A pricing strategy was developed by Chemla et al [32] to incentivize users to return bikes to the least loaded stations nearby, and a dynamic online pricing incentive strategy was proposed by Pfrommer et al [33] to motivate users to choose alternative locations to pick up or return bikes. However, depending on users' participation, user-based rebalancing may not be sufficient to achieve the system level self-rebalancing [30].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Hence, user-based rebalancing strategies are often used as a supplement to operator-based ones. These two types of strategies can be combined to help reduce rebalancing costs [33]. Currently, most existing bike-sharing systems (e.g., Mobike and Ofo in China) employ operator-based rebalancing strategies [8].…”

Section: Literature Reviewmentioning

confidence: 99%

Station-Free Bike Rebalancing Analysis: Scale, Modeling, and Computational Challenges

Jin

Tong

2020

IJGI

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In the past few years, station-free bike sharing systems (SFBSSs) have been adopted in many cities worldwide. Different from conventional station-based bike sharing systems (SBBSSs) that rely upon fixed bike stations, SFBSSs allow users the flexibility to locate a bike nearby and park it at any appropriate site after use. With no fixed bike stations, the spatial extent/scale used to evaluate bike shortage/surplus in an SFBSS has been rather arbitrary in existing studies. On the one hand, a balanced status using large areas may contain multiple local bike shortage/surplus sites, leading to a less effective rebalancing design. On the other hand, an imbalance evaluation conducted in small areas may not be meaningful or necessary, while significantly increasing the computational complexity. In this study, we examine the impacts of analysis scale on the SFBSS imbalance evaluation and the associated rebalancing design. In particular, we develop a spatial optimization model to strategically optimize bike rebalancing in an SFBSS. We also propose a region decomposition method to solve large-sized bike rebalancing problems that are constructed based on fine analysis scales. We apply the approach to study the SFBSS in downtown Beijing. The empirical study shows that imbalance evaluation results and optimal rebalancing design can vary substantially with analysis scale. According to the optimal rebalancing results, bike repositioning tends to take place among neighboring areas. Based on the empirical study, we would recommend 800 m and 100/200 m as the suitable scale for designing operator-based and user-based rebalancing plans, respectively. Computational results show that the region decomposition method can be used to solve problems that cannot be handled by existing commercial optimization software. This study provides important insights into effective bike-share rebalancing strategies and urban bike transportation planning.

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“…To approximate the probability of using carsharing, we analyze the profiles of current users of carsharing, as well as characteristics of trips which are currently done by carsharing. We use data from a survey with a random sample of 1071 users of flexible carsharing in Berlin for socio-demography data and an in-car survey with 2850 recorded trips for trip characteristics ("baseline weighting") [36].…”

Section: Carsharing Usersmentioning

confidence: 99%

Estimating Public Charging Demand of Electric Vehicles

Hardinghaus

Seidel²,

Anderson

2019

Sustainability

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Electric vehicles require sufficient public charging infrastructure. This in turn necessitates detailed information on charging demand. In this paper we present a four-step approach to estimating public charging demand of electric vehicles. Previous methods are limited in their ability to provide differentiated results and adapt to future developments. Therefore, we account for user groups (private, carsharing, commercial), technical developments (vehicles, infrastructure), infrastructure availability, and carsharing development (operational area, business models, autonomous vehicles). Our approach also considers the interactions between these factors and allows for scenario analysis yielding the quantity and spatial distribution of public charging demand. We demonstrate our approach for Berlin, Germany. We find that the majority of public charging demand results from carsharing. This demand is concentrated in the city center, even when carsharing is available citywide. Public charging demand for commercial users is relatively low and located outside the city center. For private users, public charging demand shifts to the city center with an increasing market penetration of electric vehicles and technological advancements (increased range, charging speed). Public demand from private users increases dramatically when private infrastructure is absent. Finally, public charging demand shifts to the city center when private users do not have private infrastructure.

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Identifying Users and Use of (Electric-) Free-Floating Carsharing in Berlin and Munich

Cited by 8 publications

References 10 publications

An Explanatory Model Approach for the Spatial Distribution of Free-Floating Carsharing Bookings: A Case-Study of German Cities

An Explanatory Model Approach for the Spatial Distribution of Free-Floating Carsharing Bookings: A Case-Study of German Cities

Station-Free Bike Rebalancing Analysis: Scale, Modeling, and Computational Challenges

Estimating Public Charging Demand of Electric Vehicles

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