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The article presents a first hypothesis of a business model for an intelligent LBS for personal mobility. This business model has been developed inside FP7 i-Tour project. i-Tour stands for “intelligent Transport system for Optimized URban trips” and it is a so-called Collaborative Focused Research Project presented to the call on Surface Sustainab...
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... Previous smartphone user segmentation studies have typically focused on demographics and reported usage instead of on usage patterns. This approach does not take full advantage of a number of useful features of smartphone data, as smartphones have the capability of collecting various types of information, including user communications, social networks, and lifestyle indicators, that can serve as effective sources of user segmentation [8]; in this respect, a log of application use can be a powerful resource for user segmentation because it contains meaningful information regarding the user's preferences, behaviors, and interests [16]. ...
User segmentation is an essential element of marketing and product development that considers customers’ needs and recognizes the heterogeneity of those needs. In a key study of smartphone user segmentation, Lee et al. analyzed app usage sequencing using seq2seq architecture. However, despite achieving meaningful results, their approach could not provide a robust interpretation of user segmentation because the seq2seq architecture represented users in a continuous vector space generated from a black box model. In this paper, we propose an interpretable user representation method that combines app clustering with a novel segmentation method. The user representation clusters characteristically similar apps into common clusters, with each user represented by their frequencies of app use within their respective clusters. Two novel techniques are also applied to normalize the value of user representation based on the relative degrees of importance between app clusters and the membership strengths of individual apps within a cluster. Furthermore, to address the limitations of existing segmentation methods, in which the most closely located users are assigned to specific clusters, the proposed method segments represented users using a novel segmentation approach based on relative similarity. Experimental results demonstrate that the proposed method provides an intuitive interpretation for each user’s representation and segmentation results. Furthermore, we effectively show the similarities between the results produced by our method and ground truth and demonstrate that it outperforms existing user segmentation methods.
Providing appropriate help is important in smartphone development as smartphones have become increasingly complex owing to their large number of features. To determine the appropriate help content, numerous studies on contextual help systems have been conducted; however, few studies have been concerned with user manual content. Thus, to provide effective user manuals, we focused on content prioritization, considering the usage pattern. Specifically, we calculated the vector representation of each element of the usage pattern and adopted a heterogeneous embedding approach. Moreover, we embedded the entire usage pattern using RNN-SVAE to calculate a user modeling value for representing user interests. Additionally, we trained InfoGAN (a generative adversarial network) to predict the usage of the user manual, and we prioritized and re-organized its content accordingly. Experiments demonstrated that, compared with existing benchmark methods, the proposed method can achieve better content-usage prediction and more effective prioritization of the top-k contents.
There are various help systems embedded in smartphones that are intended to provide assistance to users. These systems should be conveniently accessible in locations where users may need assistance. Moreover, when help contents are provided without regard to the users interest, it makes it difficult for the user to find relevant content. Thus, the present study provides a new method of re-organizing help content by considering each users interests and preferences using their app usage sequence. Based on the user specification derived from the app usage sequence, help contents usage prediction is generated for each of them with conditional generative adversarial network (GAN) architecture in a new way. Further, another method to pre-process data in applying conditional GAN, originally devised to generate image data, is proposed in our problem. The experiment result showed a higher absolute performance level of help contents usage prediction and better performance of effectiveness in re-organization of top-k contents compared to the existing benchmark method. Thus, the proposed method reflects the users interest and provides appropriate help contents for each user effectively.
Die Nutzung und das Verständnis von Mobilität haben sich gewandelt. Immer mehr wird Mobilität als ein on‐demand Service (Mobility on‐demand – Mod) verstanden [1]. Vor allem junge Menschen wollen und besitzen keine eignen Autos mehr, sondern verwenden stattdessen eine Kombination aus verschiedenen Mobilitätsangeboten [2]. Diese steigende Nachfrage nach flexiblen Mobilitätsangeboten wird flankiert durch den Trend der zunehmenden Urbanisierung.
Tourism development is a useful strategy in historic area renewal, especially for Recreational Business District (RBD) development. However, there is also a challenge in maintaining a balance between protection and development. This study aims to put forward a method to identify the boundary of RBD based on Internet big data in a famous historic area in China. To reconcile heritage conservation with people's behavior, the authors applied a space syntax method to obtain an understanding of space in this historic area and explored the relationship between spatial structure and RBD development based on the axial map in a series of time periods: 1950, 1990, 2002 and 2016. The authors found that RBD has spatial advantages for aggregating people flows and subsequent business in the historic area. Additionally, a positive influence between spatial structure and RBD development was found. Finally, some proposals for balancing between historic preservation and RBD development were put forth.
The sustainability of mobility services, such as car sharing, is undeniable. Therefore, it is important to research and improve mobility services, thereby revealing their full environmental potential. As modern mobility services rely on sophisticated IS and modern technology, research on IS in the context of sustainable mobility services should be an important focus in the field. Accordingly, this systematic literature review provides an overview of current research regarding mobility services and the IS employed therein. We analyze 58 publications using a concept matrix and develop a research framework. The framework builds on the IS success model by DeLone and McLean (2003) as we argue that IS quality and usage should be considered to unfold sustainable impacts as net benefits. We further outline three major research fields for future IS research in the context of sustainable mobility services: IS solutions, IS qualities and usage, and sustainable benefits.
