Where and what: Using smartphones to predict next locations and applications in daily life

Tri, Trinh Minh; Gática-Pérez, Daniel

doi:10.1016/j.pmcj.2013.03.006

Cited by 115 publications

(47 citation statements)

References 38 publications

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“…Location data recorded from the mobile phone devices reflects up-to-date travel patterns on a significantly large sample of the population, making the data a natural candidate for the analysis of mobility phenomena (e.g. Do & Gatica-Pereza, 2013;Schneider et al, 2013). In addition, the data collection is a by-product of the mobile phone companies for billing and operational purposes that generates neither extra expenses nor respondent burden.…”

Section: Problem Statementmentioning

confidence: 99%

“…The applications do not only include travel behavior and transportation modeling related research, such as mobility pattern discovery (Do & Gatica-Pereza, 2013;Schneider et al, 2013), transportation modelling and traffic analysis (Angelakis et al, 2013;Berlingerio et al, 2013;Calabrese et al, 2011), and urban planning (Becker et al, 2011;Jiang et al, 2012), but they also cover context-awareness services where user-centric assistance is provided based on users' specific location and activity context (García-Sánchez el al., 2013; Lee & Cho, 2013), and location tracking systems where knowledge of individuals' real-time locations and related routine activities is used in tools that provide support for industry, childcare, elderly health care and emergency rescue (Horng et al, 2011;Zhang et al, 2013;Zhou et al, 2014). Despite the multitude of possible applications, there are also challenges that are pertinent to the data, as acknowledged by some of the existing studies (e.g.…”

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confidence: 99%

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Building a validation measure for activity-based transportation models based on mobile phone data

Liu

Janssens

Cui

et al. 2014

Expert Systems with Applications

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Activity-based micro-simulation transportation models typically predict 24-hour activitytravel sequences for each individual in a study area. These sequences serve as a key input for travel demand analysis and forecasting in the region. However, despite their importance, the lack of a reliable benchmark to evaluate the generated sequences has hampered further development and application of the models. With the wide deployment of mobile phone devices today, we explore the possibility of using the travel behavioral information derived from mobile phone data to build such a validation measure. Our investigation consists of three steps. First, the daily trajectory of locations, where a user performed activities, is constructed from the mobile phone records. To account for the discrepancy between the stops revealed by the call data and the real location traces that the user has made, the daily trajectories are then transformed into actual travel sequences. Finally, all the derived sequences are classified into typical activity-travel patterns which, in combination with their relative frequencies, define an activity-travel profile. The established profile characterizes the current activity-travel behavior in the study area, and can thus be used as a benchmark for the assessment of the activity-based transportation models. By comparing the activity-travel profiles derived from the call data with statistics that stem from traditional activity-travel surveys, the validation potential is demonstrated. In addition, a sensitivity analysis is carried out to assess how the results are affected by the different parameter settings defined in the profiling process.

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Section: Problem Statementmentioning

confidence: 99%

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confidence: 99%

Building a validation measure for activity-based transportation models based on mobile phone data

Liu

Janssens

Cui

et al. 2014

Expert Systems with Applications

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“…The service has four components, Data Handler, Pattern Recognizer, Rule Learner, and Recommender. There are various work show why and where the next application prediction is important [16], [12]. The recommender systems, adaptive services, and contextaware applications are examples that use next user action's prediction [14].…”

Section: Application Recommendation Service (Next-app)mentioning

confidence: 99%

A Next Application Prediction Service Using the BaranC Framework

Hashemi

Herbert²

2016

2016 IEEE 40th Annual Computer Software and Applications Conference (COMPSAC)

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“…With the growing availability of aerial imagery, inexpensive GPS tracking and smartphone usage data, the last few years saw a substantial number of studies that focussed on the use of such data to predict trajectories, waypoints and trip destinations [e.g., 1,2,3,4,5,6,7]. Most work to date analysed data sets of logged trajectories to develop models of habitual movement patterns that can subsequently be employed to predict an individual's movements.…”

Section: Introductionmentioning

confidence: 99%

Predicting Individual Trip Destinations with Artificial Potential Fields

Zonta

Smit²,

Haasdijk

2017

Smart Cities

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Abstract. This paper presents a method to model the intended destination of a subject in real time, based on a trace of position information and prior knowledge of possible destinations. In contrast to most work in this field, it does so without the need for prior analysis of habitual travel patterns. The method models the certainty of each POI by means of a virtual charge, resulting in an artificial potential field that reflects the current estimate of the subject's intentions. The virtual charges are updated as new information about the subject's position arrives. We experimentally compare a number of update rules with various parameter settings, showing that it is important to take the distance to a potential destination into account when updating the charge.

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Where and what: Using smartphones to predict next locations and applications in daily life

Cited by 115 publications

References 38 publications

Building a validation measure for activity-based transportation models based on mobile phone data

Building a validation measure for activity-based transportation models based on mobile phone data

A Next Application Prediction Service Using the BaranC Framework

Predicting Individual Trip Destinations with Artificial Potential Fields

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