Predicting Human Mobility Using Sina Weibo Check-in Data

Xia, Tianyu; Shen, Junhao; Yu, Xiaoqing

doi:10.1109/icalip.2018.8455627

Cited by 4 publications

(7 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While employing the model attention should be given to the parameters that are used to determine the state transition, the initial condition setting that may influence the result, and the model assumption on the initial state. In addition, when the input data is sparse the model is not able to provide accurate results [12,35,41]. Instead, machine learning (ML) techniques, both supervised and unsupervised, are commonly used in next-place prediction as they can be trained to identify patterns and relationships.…”

Section: Methodsmentioning

confidence: 99%

“…The purposes of the studies can be divided between individual mobility prediction, which captures regularities, and tendencies of individual's mobility behaviours using mobility data, and population mobility prediction, which captures mobility behaviours at a population/group of individual level, capturing aggregated trends. The first predictions are carried out mainly by means of statistic or machine learning techniques according to the data availability [8][9][10][11][12][13][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36], while the latter can also exploit data mining techniques or agent-based modelling [6,11,24,[37][38][39][40][41][42][43][44][45][46]. The identified purposes can be further segmented from a spatial perspective by varying the unit of analysis, resulting in three prediction outcomes per purpose, i.e., trajectory recognition, next location prediction, and next trip prediction.…”

Section: Objectives and Predictionsmentioning

confidence: 99%

“…Instead, next location prediction, relying more on statistical analysis, focuses on estimating the most likely destination that a person will visit after the visited one, exploiting historical trajectories and contextual information. By exploiting past behaviours, possible areas where an individual will be in the future can be identified [11,15,23,24,33,37,39,41]. Finally, next trip prediction, similarly to next place prediction, by exploiting historical data, aims to predict the whole trip performed, identifying starting, intermediate and final points [10,12,16,21,35].…”

Section: Objectives and Predictionsmentioning

confidence: 99%

See 2 more Smart Citations

Next Place Prediction Model: A Literature Review

Garola,

Siragusa,

Seghezzi

et al. 2024

Advances in Transdisciplinary Engineering

View full text Add to dashboard Cite

Given the rising attention towards the understanding of people’s mobility, this paper focuses on the study of next-place prediction models for mobility demand and path estimation. A systematic literature review was conducted to classify the existing methodologies from a quantitative and qualitative perspective. The findings highlight the availability of several models to study next-place prediction which varies according to the objectives of the study and on the data used, with no specific dominant approach. In conclusion, the study proposes a first attempt of classification, by developing a conceptual framework, that on the one hand explains the relationship between the models’ characteristics and on the other hand guides its selection according to the users’ needs.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Objectives and Predictionsmentioning

confidence: 99%

Section: Objectives and Predictionsmentioning

confidence: 99%

See 1 more Smart Citation

Next Place Prediction Model: A Literature Review

Garola,

Siragusa,

Seghezzi

et al. 2024

Advances in Transdisciplinary Engineering

View full text Add to dashboard Cite

show abstract

“…In [21], the authors proposed to find the geographical points related to a special folk belief. In [22], the author utilized the Markov Chain model with their proposed activity detection method to predict the activity category of the user's next check-in location. In [23], an urban tourism check algorithm is proposed.…”

Section: Related Workmentioning

confidence: 99%

A GNSS-Based Crowd-Sensing Strategy for Specific Geographical Areas

Lin

Hung

Hsu

et al. 2020

Sensors

View full text Add to dashboard Cite

Infectious diseases, such as COVID-19, SARS, MERS, etc., have seriously endangered human safety, economy, and education. During the spread of epidemics, restricting the range of activities of personnel is one of the options for the prevention and treatment of infectious diseases. A global navigation satellite system (GNSS), it can provide accurate coordinates of latitude and longitude to targets with GNSS receivers. However, it is not common to use GNSS coordinates to represent positions in social life. For epidemic management, it is important to know the locations (and addresses) of targets, especially in social life. When there are many targets, it is not easy to efficiently map these coordinates to locations. Therefore, we propose a GNSS-based crowd-sensing strategy for specific geographical areas that can be used to calculate how many targets are in specific geographical areas or whether a target is in a specific area. This strategy is based on the coordinates of latitude and longitude provided by GNSS to find the locations of these coordinates. As simulated data, the data records containing latitude and longitude in a well-known social networking service platform are used. The strategy is also available for mining hot spots or hot areas.

show abstract

“…This dataset consists of 182 users' locations and GPS trajectories in a period of over five years. In [9], the authors proposed an Markov Chain predictive model for predicting the next location of an individual based on its recent locations and mobility behavior over a period of time. In their simulation part, the GeoLife dataset was used to evaluate the proposed algorithm.…”

Section: Related Workmentioning

confidence: 99%

A Predictive On-Demand Placement of UAV Base Stations Using Echo State Network

Peng

Chen

Lai

et al. 2019

2019 IEEE/CIC International Conference on Communications in China (ICCC)

View full text Add to dashboard Cite

The unmanned aerial vehicles base stations (UAV-BSs) have great potential in being widely used in many dynamic application scenarios. In those scenarios, the movements of served user equipments (UEs) are inevitable, so the UAV-BSs needs to be re-positioned dynamically for providing seamless services. In this paper, we propose a system framework consisting of UEs clustering, UAV-BS placement, UEs trajectories prediction, and UAV-BS reposition matching scheme, to serve the UEs seamlessly as well as minimize the energy cost of UAV-BSs' reposition trajectories. An Echo State Network (ESN) based algorithm for predicting the future trajectories of UEs and a Kuhn-Munkres-based algorithm for finding the energy-efficient reposition trajectories of UAV-BSs is designed, respectively. We conduct a simulation using a real open dataset for performance validation. The simulation results indicate that the proposed framework achieves high prediction accuracy and provides the energy-efficient matching scheme.

show abstract

Predicting Human Mobility Using Sina Weibo Check-in Data

Cited by 4 publications

References 9 publications

Next Place Prediction Model: A Literature Review

Next Place Prediction Model: A Literature Review

A GNSS-Based Crowd-Sensing Strategy for Specific Geographical Areas

A Predictive On-Demand Placement of UAV Base Stations Using Echo State Network

Contact Info

Product

Resources

About