Carsharing, in any form, is still growing around the world. One of the effects is the increasing number of cities in which multiple carsharing operators are competing. The carsharing industry has never been as competitive as it is now: the present is a good time for researchers to invest efforts in providing tools for the assessment and planning of carsharing programs. Nevertheless, efforts in this direction are still scarce, in particular for some of the newest forms in which carsharing has been implemented, such as free-floating carsharing. This paper reports on a study that made use of MATSim, an agent-based simulation software that had already been used to model station-based carsharing, to evaluate different carsharing scenarios for the city of Berlin. The main findings are the existing high potential to extend carsharing services further in Berlin and the apparent complementarity of station-based and free-floating carsharing. On the methodological level, the work introduces a new tool for the modeling of free-floating carsharing along with improvements of the previously existing station-based carsharing model.

Carsharing is a system that allows individuals to use a car from a fleet on an hourly basis. The growing popularity of carsharing is reflected by a continuous increase in the number of users worldwide. However, the estimation of travel demand for this mode has only sporadically been addressed by researchers and not in a completely satisfactory way. The work reported in this article introduces a new methodology to estimate travel demand for carsharing: activity-based microsimulation. An existing open source software, MATSim, has been enhanced to allow modeling carsharing. This article reports the modeling approach and describes the implementation of the carsharing mode in the system. Some results, based on a simulation scenario with about 160,000 agents representing the urban area of Zurich, Switzerland, are presented and validated against customer data of Mobility Switzerland, the dominant carsharing operator active in the study area.

This study investigates the effects of climate change on tourist mobility in mountain areas, distinguishing between infrastructure, transport operation and travel demand. We examine change in tourist travel demand by proposing a two-step approach to forecast its future development. A multi-origin, multi-destination model for tourism demand quantifies the variation in overnight stays within a given region, and a linear, deterministic model determines the traffic-related implications. The method, tested on the Autonomous Province of South Tyrol (Italy), exhibits expected variations in winter and summer travel demand up to 2080 under different scenarios. Results reveal that average summer traffic can be more than twice as intense as average winter traffic, contributing to significantly increasing the peak days of congestion along the Provincial road network. Despite this evidence, all stakeholders seem to be at an early stage in incorporating this information into their strategic planning. The need for adequate transport policies and measures is considered essential to obtain the optimal balance of transport modes that will be required in the near future.