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

Abstract

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.

Electric Free-Floating Carsharing for Sustainable Cities: Characterization of Frequent Trip Profiles Using Acquired Rental Data

“…Data processing techniques were applied subsequently with the necessary criteria to identify the records corresponding to real user trips. This research shows that reconstructing trips from booking data by using big data platforms is not an automatic task, as stressed previously Müller et al [11], and more research is needed in this area. It would also be convenient to reproduce this study in other cities to make the analysis more robust and check the correct performance of the platform.…”

“…Their objective was to describe carsharing usage and to obtain a spatial distribution of flows, identifying the socio-economic factors that influence FFCS demand. Using booking data provided by DriveNow also in the city of Berlin, Müller et al [11] studied, a few years later, the influence of land use and census data on FFCS demand through a negative binomial model. Areas' centrality, districts with citizens having a frequent use of ICTs, and parking availability were revealed as the main determinants which lead to higher demand (higher number of bookings).…”

“…On the other hand, the itinerary of a FFCS trip is not known (although FFCS travel time can be calculated) and, in most studies, it is estimated as a geodistance, namely, the straight-line distance between origin and destination. Müller et al [11], after working with FFCS flows data provided by companies, raised an important issue concerning the complexity of using these methodologies to obtain real flow data by reading an operator's API. Booking data should be analyzed with caution, and this analysis cannot be automatized in order to avoid errors.…”

“…These web-based methods work with the current position of available FFCS cars (at least when parked and not booked) and focus on various hypotheses to reconstruct the profile and the spatial distribution of trips. Reconstructing trips from booking data by using big data platforms is not an automatic task as stressed by Müller et al [11], and more research is needed in this area, where there are only a few previous studies in the literature [12][13][14].This paper contributes to the existing literature with a detailed analysis of FFCS web-based data obtained for Madrid, paying special attention to the socioeconomic variables of the most demanding districts, the main destinations of the flows, and the parking availability. This is a pioneering experience in Madrid, a city with one of the highest FFCS utilization rates in the world [15] and a long tradition of public transport, although the implementation of FFCS systems only took place recently in 2015.…”

“…These web-based methods work with the current position of available FFCS cars (at least when parked and not booked) and focus on various hypotheses to reconstruct the profile and the spatial distribution of trips. Reconstructing trips from booking data by using big data platforms is not an automatic task as stressed by Müller et al [11], and more research is needed in this area, where there are only a few previous studies in the literature [12][13][14].…”