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Activity-Aware Map: Identifying Human Daily Activity Pattern Using Mobile Phone Data
TLDR
This paper characterize mobility in a profile-based space (activity-aware map) that describes most probable activity associated with a specific area of space and finds a strong correlation in daily activity patterns within the group of people who share a common work area's profile. Expand
Socio-Geography of Human Mobility: A Study Using Longitudinal Mobile Phone Data
TLDR
A relationship between people’s mobility and their social networks is presented based on an analysis of calling and mobility traces for one year of anonymized call detail records of over one million mobile phone users in Portugal, finding that people are geographically closer to their weak ties than strong ties. Expand
Urban mobility study using taxi traces
In this work, we analyze taxi-GPS traces collected in Lisbon, Portugal. We perform an exploratory analysis to visualize the spatiotemporal variation of taxi services; explore the relationshipsExpand
Exploratory Study of Urban Flow using Taxi Traces
The analysis of vehicle’s GPS traces such as taxis can help better understand urban mobility and flow. In this paper w e present a spatiotemporal analysis of taxis GPS traces collected in Lisbon, PExpand
Weather Effects on the Patterns of People's Everyday Activities: A Study Using GPS Traces of Mobile Phone Users
TLDR
It is found that people were more likely to stay longer at eateries or food outlets, and (to a lesser degree) at retail or shopping areas when the weather is very cold or when conditions are calm (non-windy), and how mobile phone data can be used to investigate the influence of environmental factors on urban dynamics is shed. Expand
Group Recommendation System for Facebook
TLDR
This work believes that Facebook SN groups can be identified based on their members' profiles and introduces group recommendation system (GRS) using combination of hierarchical clustering technique and decision tree. Expand
Behavior analysis of spam botnets
TLDR
This study shows that the relationship among spammers demonstrates highly clustering structures based on features such as content length, time of arrival, frequency of email, active time, inter-arrival time, and content type. Expand
Taxi-Aware Map: Identifying and Predicting Vacant Taxis in the City
TLDR
This paper presents a predictive model for the number of vacant taxis in a given area based on time of the day, day of the week, and weather condition, and the history is used to build the prior probability distributions for the inference engine. Expand
Behavior-based adaptive call predictor
TLDR
A Call Predictor (CP) that offers two new advanced features for the next-generation phones namely “Incoming Call Forecast” and “Intelligent Address Book" and it is shown that the recent trend of the caller/user's calling pattern has higher correlation to the future pattern than the pattern derived from the entire historical data. Expand
Inferring Passenger Travel Demand to Improve Urban Mobility in Developing Countries Using Cell Phone Data: A Case Study of Senegal
TLDR
A methodology to estimate passenger demand for public transport services using cell phone data is presented and significant origins and destinations of inhabitants are extracted and used to build origin-destination matrices that resemble travel demand. Expand
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