Towards a Persuasive Recommender for Bike Sharing Systems: A Defeasible Argumentation Approach

Abstract

This work proposes a persuasion model based on argumentation theory and users’ characteristics for improving the use of resources in bike sharing systems, fostering the use of the bicycles and thus contributing to greater energy sustainability by reducing the use of carbon-based fuels. More specifically, it aims to achieve a balanced network of pick-up and drop-off stations in urban areas with the help of the users, thus reducing the dedicated management trucks that redistribute bikes among stations. The proposal aims to persuade users to choose different routes from the shortest route between a start and an end location. This persuasion is carried out when it is not possible to park the bike in the desired station due to the lack of parking slots, or when the user is highly influenceable. Differently to other works, instead of employing a single criteria to recommend alternative stations, the proposed system can incorporate a variety of criteria. This result is achieved by providing a defeasible logic-based persuasion engine that is capable of aggregating the results from multiple recommendation rules. The proposed framework is showcased with an example scenario of a bike sharing system.

Guidelines for the Analysis and Design of Argumentation-Based Recommendation Systems

“…Early work includes [1], and [7,2] present an application in the domain of film recommendation, stressing the importance of considering both qualitative and quantitative aspects; furthermore, explanations that support the recommendations are generated in natural language. Other recent efforts leveraging argumentation-based tools are [8,9]. Finally, other relevant work involves applying data-driven approaches, as we will discuss below [10,11].…”

Are Personal Electric Vehicles Sustainable? A Hybrid E-Bike Case Study

“…The lowest prices were obtained for the hybrid storage implementations (battery-FC and SC-FC) and the highest for the FC implementation. * According to estimations and average values, 1 according to reference [69], 2 according to reference [25], 3 according to reference [17], for the tested pedelec, 4 according to reference [33], 5 according to reference [39], for the tested models, 6 according to reference [70], 7 according to reference [49], 8 according to reference [71], 9 according to reference [22], 10 according to reference [72], 11 according to reference [73], 12 according to reference [21], 13 according to reference [74], 14 according to reference [75], 15 according to reference [76], 16 according to reference [77], 17 according to reference [23], 18 according to reference [58], 19 according to reference [60], 20 according to reference [78], 21 according to reference [79], 22 according to reference [67]. Table 9.…”

“…Unfortunately, the current development of vehicles can have a damaging effect on the environment [16]. Air pollution caused by different classes of vehicles is discussed in references [17][18][19][20].…”

“…E-bikes and other EVs present several limitations and issues such as lack of infrastructure and unitary regulations in safety, speed, and power; reduced autonomy and life span. But, crowded cities still require solutions that should imply green and sustainable smart transportation systems [16] that are also beneficial to human health and fitness, or at least not damaging. Of course, the traditional bike is also green and does not consume any energy except for human effort.…”

A Rebalancing Strategy for the Imbalance Problem in Bike-Sharing Systems

“…The proposed Hierarchical Reinforcement Pricing (HRP) algorithm shows great performance on a dataset from Mobike, a major Chinese dockless bike sharing system. More recently, Diez et al [26] proposed a persuasion model to recommend the optimal station and the shortest riding route between two locations. The model combines the argumentation theory and the characteristics of customers.…”