19 Transportation Research Record: Journal of the Transportation Research Board, No. 2536, Transportation Research Board, Washington, D.C., 2015, pp. 19–27. DOI: 10.3141/2536-03 This paper presents an integrated relocation model for free-floating car- sharing (FFCS) systems. A historical data analysis conducted monthly generates demand indicators. A zone categorization identifies zones with a historical vehicle shortage or surplus and enables the estimation of the optimal vehicle distribution for the target time period. Given the current vehicle positions, the model identifies vehicle imbalances. Five macroscopic and microscopic steps using optimization and rule-based methods recommend relocations and service trips on an individual vehicle level. This process results in detailed staff operation plans. To assess the impact of relocations on the system’s operation, the model was applied to an FFCS system in Munich, Germany. Three real-world field tests conducted at stages of the model’s development exhibited different degrees of automation. The evaluation showed promising results. All tests had positive impacts on the main measures of success. The earnings per vehicle were increased by up to 18%. The mean idle time per trip end of all vehicles in the fleet was decreased by up to 18%. The difference in idle times between the relocated vehicles and neighboring vehicles that were not relocated was decreased by up to 31%. Most impor- tant, the profit of the operator was between 4.7% and 5.8% higher than without relocations. The automation of the whole model in the last field test led to slightly lower but still very positive impacts while facilitating the whole relocation process for the operator. During the past 30 years, carsharing (CS) has found a solid posi- tion in the mobility landscape. It contributes to solving problems in transportation, land use, environment, and society (1). The traditional CS systems are station-based, with vehicles distributed throughout a network of stations that have different capacities. The automobiles are used with time-dependent and distance-dependent fees after reservation (1). In most cases, the user must return the vehicle to its home station. This ensures that stations do not overflow with vehicles or run out of them. Some oper- ators allow one-way trips to other stations, which leads to imbalances in vehicle stocks at stations. This phenomenon has already forced Honda’s Diracc system in Singapore to stop operation (2). During the past decade, flexible or free-floating CS systems (FFCS) sup- plemented the traditional station-based systems. These systems are more flexible because a user is not required to make a reservation. The vehicle can be parked at any parking spot within an operating area, and the fee is primarily time-dependent. As in station-based, one- way systems, imbalances between supply and demand may occur. The problem of station capacity is not relevant, but the prediction of system behavior is more complicated because customers access the vehicles spontaneously and do not specify their destinations in advance. Using data of a system in Munich, Germany, Weikl and Bogenberger (3) showed that vehicle imbalances occur for FFCS systems. Vehicles conglomerate in the southern zones of the oper- ating area during the weekends and are in short supply in the cen- tral zones on Monday mornings. The southern zones, on average, on Mondays with a significantly low number of bookings, have a vehicle surplus of between 1% and 6%; whereas, the northern zones have a shortage of between 4% and 7%. This is caused by different usage patterns on weekends and workdays. A study of the same sys- tem showed that on Sundays more customers use vehicles to drive home, for leisure activities, to bring someone, or to pick someone up. Compared with workdays, shopping trips and trips to work play a minor role (4). The challenging logistic problems of FFCS systems have to be solved (5). Serious vehicle imbalances have to be eliminated by dynamically relocating vehicles from oversupplied to undersupplied regions. Jorge and Correia (6) provided an overview of the existing literature on CS systems, with a focus on demand modeling and the vehicle imbalance problem of one-way CS systems. In the past, the problem of vehicle imbalances has been considered mainly for station-based, one-way vehicle sharing (VS) systems. For bikesharing (BS) systems, Morency et al. (7 ) noted that the degree to which relocations are necessary is related to the stations’ locations. Approaches trying to reduce vehicle stock imbalances in station- based BS systems by optimal system design can be found, among others, in Barth and Todd (8), Correia et al. (9), and García-Palomares et al. (10). Other researchers focused more intensely on how to eliminate existing imbalances during operation. The studies distinguished between operator-based relocation strategies, where the operator moves the vehicles between stations, and user-based relocation strategies, where the relocation task is shifted to users. Relevant papers are, for instance, Barth et al. (11), Clemente et al. (12), Di Febbraro et al. (13), Fan (14), Jorge et al. (15), Miller-Hooks and Nair (16), Nair et al. (17 ), and Sayarshad et al. (18). However, the problem of how operator-based vehicle movements would be most efficiently conducted by the relocation workers was not solved. Among others, Bruglieri et al. (19), Chemla et al. (20), and Kek et al. (21) focused on this aspect of efficient operator-based vehicle movement. Integrated Relocation Model for Free-Floating Carsharing Systems Field Trial Results Simone Weikl and Klaus Bogenberger Department of Civil Engineering and Environmental Sciences, Munich University of the Federal Armed Forces, Werner-Heisenberg-Weg 39, 85577 Neubiberg, Germany. Corresponding author: S. Weikl, Simone.Weikl@unibw.de.