## 1 Introduction  
A fundamental ingredient for optimizing the locations of service points in mobility applications, such as charging stations for electric vehicles or pickup and drop-off stations for car/bike sharing systems, is the distribution of existing customer demand to be potentially fulfilled in the considered geographical area. While there exists a vast amount of literature regarding setting up service points for mobility applications, such as vehicle sharing systems [1, 2, 3, 4] or charging stations for electric vehicles [5, 6, 7, 8], estimations of the existing demand distribution are usually obtained upfront by performing customer surveys, considering demographic data, information on the street network and public transport, and not that seldom including human intuition and political motives. However, such estimations are frequently imprecise and a system built on such assumptions might not perform as effectively as it was originally hoped for. For example, in Reference [9], GPS-based travel survey data of fossil fueled cars is used for setting up charging stations for electric vehicles. However, as pointed out by [PERSON] et al. [10], it cannot be assumed that the driving behavior of customers remains unchanged when switching from fossil fueled carsto electric vehicles. Furthermore, [PERSON] et al. [10] present a survey of 119 publications for locating charging stations for electric vehicles in which they also discuss further problems with the above mentioned demand estimation methods. A more frequent usage of a service system by a customer will in general depend not only on the construction of a single service point on a particular location but more globally on non-trivial relationships of the customer's necessities and preferences in conjunction with larger parts of the whole service system. For example, in the case of bike/car sharing systems, a well placed rental station close to the origin of a trip might be worthless if there does not also exist a suitable location near the destination for returning the vehicle. Furthermore, some customers might use multiple modes of transport for a single trip [11]. Consequently, some more distant service station for returning the vehicle might be acceptable if this place is well connected by public transport used for an additional last leg [12]. Thus, there also might be alternatives for fulfilling demand that cannot all be exactly specified by potential users. The example with an additional leg by public transport also illustrates that geographical closeness is not always the deciding factor. To address these issues, [PERSON] et al. [13] proposed the concept of a _Cooperative Optimization Algorithm_ (COA) which, instead of estimating customer demand upfront, directly incorporates potential users in the location optimization process by iteratively confronting them with location scenarios and asking for evaluation. Based on the user feedback, a machine learning model is trained which is then used as surrogate objective function to evaluate solutions in an optimization component. User interaction, a corresponding refinement of the surrogate objective function, and the optimization are iterated. Expected benefits of such a cooperative approach are a faster and easier data acquisition [14], the direct integration of users into the whole location planning process, a possibly stronger emotional link of the users to the product, and ultimately better understood [15] and more accepted optimization results [14]. Potential customers further know local conditions and their particular properties, including also special aspects that are not all easily foreseen in a classical demand acquisition approach. To the best of our knowledge, such an approach has not yet been considered in the area of locating service points for mobility applications.