No abstract

Free-floating car-sharing schemes operate without fixed car-sharing stations, ahead reservations or return-trip requirements. Providing fast and convenient motorization, they attract both public transportation users and (former) car-owners. However, given their highly flexible nature and different pricing structures, previous findings on user groups and environmental impact of station-based car-sharing may not be easily transferable. Therefore, this research uses survey data to directly compare user groups and usage patterns of a free-floating and station-based carsharing service both operating in the city of Basel. The findings suggest, that the schemes indeed attract different user groups and are also used differently. Moreover, it is shown, that car-sharing membership is governed by other factors than car-sharing activity. Given the observed structural differences, the environmental impact of free-floating car-sharing is yet to be determined.

Autonomous vehicles are expected to offer a higher comfort of traveling at lower prices and at the same time to increase road capacity -a pattern recalling the rise of the private car and later of motorway construction. Using the Swiss national transport model, this research simulates the impact of autonomous vehicles on accessibility of the Swiss municipalities. The results show that autonomous vehicles could cause another quantum leap in accessibility. Moreover, the spatial distribution of the accessibility impacts implies that autonomous vehicles favor urban sprawl and may render public transport superfluous except for dense urban areas.

This paper describes development and testing of a passive GPS tracking smartphone application and corresponding data analysis methodology designed to increase the quality of travel behavior information collected in long-term travel surveys. The new approach is intended to replace the pencil-and-paper travel diaries and prompted recall methods that require more user involvement due to requirements for manual data entry and/or high battery usage. Reducing the burden placed on users enables researchers to collect data over longer periods, thus improving the quality of travel behavior research. To reduce battery use the smartphone-based application collects GPS data less frequently than other methods. Therefore, new algorithms were developed to identify trips and activities, transport mode, and even the specific vehicle used by the traveler. An important finding was the significant advantage of using users past data to improve mode detection results. The system was tested successfully in Zürich and Basel (Switzerland).

Mobility as a Service (MaaS) aims to allow less biased mode choice decisions by overcoming market segmentation. To this end, all available modes are offered at their respective marginal cost for each trip. Such a setting favors shared modes, where fixed costs can be apportioned among a large number of users. In turn, car-sharing, bike-sharing or ridehailing may themselves become an efficient alternative of public transport. Although early field studies confirm the expected changes in behaviour, impacts have not been studied for larger transport systems yet. This research conducts a first joint simulation of car-sharing, bike-sharing and ride-hailing for a city-scale transport system using MAT-Sim. Results show that in Zurich, through less biased mode choice decisions, transportrelated energy consumption can be reduced by 25 %. In addition, introduction of shared modes may increase transport system efficiency by up to 7 %. Efficiency gains may reach 11 % if shared modes were used as a substitute for public transport in lower-density areas. Hence, a MaaS scheme with shared mobility allows to increase system efficiency (travel times & cost), while substantially reducing energy consumption.

Free-floating car-sharing schemes operate without fixed car-sharing stations, ahead reservations or return-trip requirements. Providing fast and convenient motorization, they attract both public transport users and (former) car-owners. Thus, their impact on individual travel behavior depends on the user type. Estimating the travel behavior impact of these systems therefore requires quantitative data. Using a two-wave survey approach (shortly after launch of the scheme plus one year later) including travel diaries, this research indicates that (due to their membership) 6 % of the free-floating car-sharing customers reduce their private vehicle ownership. Moreover, the results suggest that freefloating car-sharing both complements and competes with station-based car-sharing.

Free-floating car-sharing has been one of the latest innovations in the car-sharing market. It allows its customers to locate available vehicles via a smartphone app and reserve them for a short time prior to their rental. Because it is available for point-to-point trips, free-floating car-sharing is not only an alternative to private cars, but also to public transportation. Using spatial regression and conditional logit analysis of original transaction data of a free-floating carsharing scheme in Switzerland, this research shows that free-floating car-sharing is mainly used for discretionary trips, for which only substantially inferior public transportation alternatives are available. In contrast to station-based car-sharing, it does not rely on high-quality local public transportation access, but bridges gaps in the existing public transportation network.

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.