Abstract: Check-in locations on social media provide information about an individual’s location. The millions of units of data generated from these sites provide knowledge for human activity. In this research, we used a geolocation service and users’ texts posted on Twitter social media to analyze human mobility. Our research will answer the questions; what are the movement patterns of a citizen? And, how far do people travel in the city? We explore the people trajectory of 201,118 check-ins and 22,318 users over a period of one month in Makassar city, Indonesia. To accommodate individual mobility, the authors only analyze the users with check-in activity greater than 30 times. We used sampling method with a systematic sampling approach to assign the research sample. The study found that the individual movement shows a high degree of regularity and intensity in certain places. The other finding found that the average distance an urban inhabitant can travel per day is as far as 9.6 km.
Abstract: With the gigantic increment in portable applications use and the spread of positioning and location-aware technologies that we are seeing today, new procedures and methodologies for location-based strategies are required. Location recommendation is one of the highly demanded location-aware applications uniquely with the wide accessibility of social network applications that are location-aware including Facebook check-ins, Foursquare, and others. In this paper, we aim to present a new methodology for location recommendation. The proposed approach coordinates customary spatial traits alongside other essential components including shortest distance, and user interests. We also present another idea namely, "activity trajectory" that represents trajectory that fulfills the set of activities that the user is intrigued to do. The approach dispatched acquaints the related distance value to select trajectory(ies) with minimum cost value (distance) and spatial-area to prune unneeded directions. The proposed calculation utilizes the idea of movement direction to prescribe most comparable N-trajectory(ies) that matches the client's required action design with least voyaging separation. To upgrade the execution of the proposed approach, parallel handling is applied through the employment of a MapReduce based approach. Experiments taking into account genuine information sets were built up and tested for assessing the proposed approach. The exhibited tests indicate how the proposed approach beets different strategies giving better precision and run time.
Abstract: The check-in area of airport terminal is one of the
busiest sections at airports at certain periods. The passengers are
subjected to queues and delays during the check-in process. These
delays and queues are due to constraints in the capacity of service
facilities. In this project, the airport terminal is decomposed into
several check-in areas. The airport check-in scheduling problem
requires both a deterministic (integer programming) and stochastic
(simulation) approach. Integer programming formulations are
provided to minimize the total number of counters in each check-in
area under the realistic constraint that counters for one and the same
flight should be adjacent and the desired number of counters
remaining in each area should be fixed during check-in operations.
By using simulation, the airport system can be modeled to study the
effects of various parameters such as number of passengers on a
flight and check-in counter opening and closing time.
Abstract: Currently, there is excessively growing information
about places on Facebook, which is the largest social network but
such information is not explicitly organized and ranked. Therefore
users cannot exploit such data to recommend places conveniently and
quickly. This paper proposes a Facebook application and an Android
application that recommend places based on the number of check-ins
of those places, the distance of those places from the current location,
the number of people who like Facebook page of those places, and
the number of talking about of those places. Related Facebook data is
gathered via Facebook API requests. The experimental results of the
developed applications show that the applications can recommend
places and rank interesting places from the most to the least. We have
found that the average satisfied score of the proposed Facebook
application is 4.8 out of 5. The users’ satisfaction can increase by
adding the app features that support personalization in terms of
interests and preferences.