A diffusion model for estimating adoption patterns of a one-way carsharing system in its initial years

Oneway carsharing service operators must assess the importance of each station relative to overall demand if resources are limited. We propose a variation of an innovation diffusion model designed to estimate new “hesitant” and “fast” adopters for different stations of a one-way carsharing system to understand system adoption dynamics better over time and to derive policy implications. We forecast the number of monthly new adopters and potential market of stations considering their synergistic effects. We further reflect the spatially diverse adoption dynamics during the initial years of a carsharing service. Stations are classified into four groups based on their location and demand pattern. The models are estimated using data from the Ha:mo RIDE carsharing system in Toyota, Japan. We observe two peaks in the new user curve that our model can explain. We propose that the initial peak is caused by information diffusion, whereas the later peak is due to market saturation. Policy relevant implications are that we observe a low degree of follower effect and that new stations in strategic locations are essential for continued demand growth. More specifically, we suggest that carsharing stations in residential areas experience demand stagnation fast, whereas continued demand growth can be expected and quantified for carsharing stations around transit hubs and public facilities. Therefore carsharing operators need to be aware that assessing the importance of a station for the overall system requires time as well as consideration of synergy effect with other service points.