Crowd location forecasting at points of interest

نویسندگان

  • Jorge Alvarez-Lozano
  • J. Antonio García-Macías
  • Edgar Chávez
چکیده

Predicting the location of a mobile user in the near future can be used for a large number of user-centered ubiquitous applications. This can be extended to crowdcentered applications if a large number of users is included. In this paper we present a spatio-temporal prediction approach to forecast user location in a medium-term period. Our approach is based on the hypothesis that users exhibit a different mobility pattern for each day of the week. Once factored out this weekly pattern, user mobility among points of interest is postulated to be markovian. We trained a hidden Markov model to forecast user mobility and evaluated our approach using a public dataset. The experimental results show that our approach is effective considering a time period of up to seven hours. We obtained an accuracy of up to 81.75 % for a period of 30 minutes, and 66.25 % considering 7 hours.

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Visual Geographical Representation for Large-Scale Crowd Simulation and Reaction to Events

Abstract: The goal of the project was to create a visual repThe goal of the project was to create a visual representation for simulated crowd behavior on a massive scale (many city blocks) as driven by cultural and other demographic considerations. Specifically, the representation was to take place in real-time and on top of existing geographical data. We wanted to observe the crowd behavior as...

متن کامل

A Review of Spatial Factor Modeling Techniques in Recommending Point of Interest Using Location-based Social Network Information

The rapid growth of mobile phone technology and its combination with various technologies like GPS has added location context to social networks and has led to the formation of location-based social networks. In social networking sites, recommender systems are used to recommend points of interest (POIs) to users. Traditional recommender systems, such as film and book recommendations, have a lon...

متن کامل

Centroidal particles for interactive crowd simulation

Real-time crowd simulation is a challenging task that demands a careful consideration of the classic trade-off between accuracy and efficiency. Existing particle-based methods have seen success in simulating crowd scenarios for various applications in the architecture, military, urban planning, robotics, and entertainment (film and gaming) industries. In this paper we focus on local dynamics an...

متن کامل

Improving Location Reliability in Mobile Crowd Sensing

People-centric sensing with smart phones can be used for large scale sensing of the physical world at low cost by leveraging the available sensors on the phones. However, the sensed data submitted by participants is not always reliable as they can submit false data to earn money without executing the actual task at the desired location. To address this problem, the authors propose ILR, a scheme...

متن کامل

Integrating OpenStreetMap with Google Street View using image processing and machine learning

OpenStreetMap is rapidly expanding crowd-sourced data base of world map. This data, if integrated with visual information from Google Street View would be an immense store of information without manual crowd collection. This paper proposes a method of probabilistically evaluating values of heading or camera angle of Google Street View which points directly to the entity of interest whose latitu...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

عنوان ژورنال:
  • IJAHUC

دوره 18  شماره 

صفحات  -

تاریخ انتشار 2015