Early Destination Prediction with Spatio-temporal User Behavior Patterns

Abstract

Predicting user behavior makes it possible to provide personalized services. Destination prediction (e.g. predicting a future location) can be applied to various practical applications. An example of destination prediction is personalized GIS services, which are expected to provide alternate routes to enable users to avoid congested roads. However, the destination prediction problem requires critical trade-offs between timing and accuracy. In this paper, we focus on early destination prediction as the central issue, as early recognition in destination prediction has not been fully explored. As an alternative to the traditional two basic approaches with trajectory tracking that narrow down the candidates with respect to the trip progress, and Next Place Prediction (NPP) that infers the future location of a user from user habits, we propose a new probabilistic model based on both conventional models. The advantage of our model is that it drastically narrows down the destination candidates efficiently at the early stage of a trip, owing to the staying information derived from the NPP approach. In other words, our approach achieves high prediction accuracy by considering both approaches at the same time. To implement our model, we employ SubSynE for state-of-the-art prediction based on trajectory tracking as well as a multi-class logistic regression based on user contexts. Despite the simplicity of our model, the proposed method provides improved performance compared to conventional approaches based on the experimental results using the GPS logs of 1,646 actual users from the commercial services.

When Do Drivers Interact with In-Vehicle Well-being Interventions?

“…3 To understand how different factor groups influence model prediction, we built three types of models, (a) using just the affective states of the participant, (b) using just the extrinsic trip characteristics, and (c) using a combination of affective states and extrinsic trip characteristics. The reasoning is that the approximation of trip characteristics such as route or traffic can be achieved by navigational systems or analysis of past travel patterns [26]; detecting the affective state, however, is a more challenging task and is an area of active research [55].…”

Perfilado inteligente de movilidad de usuarios

“…Por ejemplo, conociendo los lugares que el usuario visitó en el pasado es posible inferir sus intereses [15] [16], lo cual es extremadamente útil en sistemas de recomendación. Uno de los tópicos más importantes en el estudio de movilidad de los usuarios, es la predicción de destinos [17]. Este tópico consiste en, una vez identificados los lugares que visita el usuario, aprender sus rutinas de movilidad y predecir cuándo y dónde serán sus próximas visitas.…”

“…Por último, en cuarto lugar, al combinar distintas técnicas de predicción normalmente se consiguen mejores precisiones. Algunos autores han notado que distintas técnicas pueden funcionar mejor o peor para distintos usuarios, por lo que han propuesto combinar varias técnicas de predicción [121] [122] [114] [117] [128] [17]. La forma de combinar varía en cada trabajo.…”

Understanding User Behavior in Car Sharing Services Through The Lens of Mobility

“…There is a rich history of UbiComp/IMWUT research concentrating on user behavior analysis [63]. In recent years, with the increasing importance of user experiences in system design, we are witnessing an increasing number of research studies in the IMWUT community [2,24,26,27,33,37,40,45,47,63,64] to understand user behavior. Zhang et al [63] tried to understand user behavior in group event decisions based on data collected from a mobile application, and they finally provided detailed novel insights in the event scheduling process of social groups.…”