Selecting Individual and Population Models for Predicting Human Mobility

Baumann, Paul; Koehler, Christian; Dey, Anind K.; Santini, Silvia

doi:10.1109/tmc.2018.2797937

Cited by 15 publications

(5 citation statements)

References 43 publications

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“…Despite the fact that this model depicts humans as solitary beings with independent mobility, research [7], [8] indicates that human movement is not entirely random because human characteristics themselves exhibit an implication. Humans tend to move in groups or crowds due to their social nature [9], [10]. Lindorfer and Hossen's studies also confirm that although humans move in groups, they still possess individualistic characteristics, so it is possible that they will occasionally leave the group [11], [12].…”

Section: Introductionmentioning

confidence: 93%

Implementation of Group-Based Human Movement Model in Opportunistic Network

Ayu

Soelistijanto

2022

Indonesian J. of Inf. Syst.

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As an instance of a distributed computing system, opportunistic networks facilitate message dissemination in a store-carry-forward manner. In this setting, the mobile devices are communicating in opportunistic contacts as they move across the network areas. However, the movement of these mobile devices is exclusively reliant on the mobility of their human owner, thereby limiting the likelihood of contact. The current state of the art typically simulates human movement based on randomness, which is unsuitable for representing how people move in groups. Therefore, this paper proposes an implementation of a group-based human mobility model to simulate device-to-device communication in opportunistic networks. In this model, individuals are able to move as a set within a group and have the ability to join and leave the group dynamically We built the model in BonnMotion and subsequently implemented it in an opportunistic environment simulator, ONE Simulator. To evaluate the proposed model, we compared them to the random-based model as a benchmark. Subsequently, we assess the impact of the movement model on two major areas of network performance: message delivery performance and resource utilization, such as nodes’ energy consumption. We are concerned about these aspects since the mobile agents have limited resources yet are expected to achieve a high rate of message delivery as well. The simulation results show that our model outperformed the random-based model in terms of the number of successfully delivered messages and average delay. However, the number of message replications and the energy consumption is fairly higher than those of the benchmarks.

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Section: Introductionmentioning

confidence: 93%

Implementation of Group-Based Human Movement Model in Opportunistic Network

Ayu

Soelistijanto

2022

Indonesian J. of Inf. Syst.

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“…In addition, movement patterns aid to develop a scenario where mobile users play a significant role in their movement behaviours through most visited places. Embracing the world in the 5G era, challenges in understanding traffic patterns and human mobility predictions is limited, which has been experienced by urbanised cellular towers [4,7,8]. Extra diligent intelligence is required to fully automate the network that would be a hypothetical remedy against current complexities involved in predicting the traffic flows in the underground train environment [9,10].…”

Section: Related Workmentioning

confidence: 99%

Mobility Prediction-Based Optimisation and Encryption of Passenger Traffic-Flows Using Machine Learning

Asad

Ahmad

Hussain

et al. 2020

Sensors

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Information and Communication Technology (ICT) enabled optimisation of train’s passenger traffic flows is a key consideration of transportation under Smart City planning (SCP). Traditional mobility prediction based optimisation and encryption approaches are reactive in nature; however, Artificial Intelligence (AI) driven proactive solutions are required for near real-time optimisation. Leveraging the historical passenger data recorded via Radio Frequency Identification (RFID) sensors installed at the train stations, mobility prediction models can be developed to support and improve the railway operational performance vis-a-vis 5G and beyond. In this paper we have analysed the passenger traffic flows based on an Access, Egress and Interchange (AEI) framework to support train infrastructure against congestion, accidents, overloading carriages and maintenance. This paper predominantly focuses on developing passenger flow predictions using Machine Learning (ML) along with a novel encryption model that is capable of handling the heavy passenger traffic flow in real-time. We have compared and reported the performance of various ML driven flow prediction models using real-world passenger flow data obtained from London Underground and Overground (LUO). Extensive spatio-temporal simulations leveraging realistic mobility prediction models show that an AEI framework can achieve 91.17% prediction accuracy along with secure and light-weight encryption capabilities. Security parameters such as correlation coefficient (<0.01), entropy (>7.70), number of pixel change rate (>99%), unified average change intensity (>33), contrast (>10), homogeneity (<0.3) and energy (<0.01) prove the efficacy of the proposed encryption scheme.

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“…A widespread topic is to predict human mobility with the smartphone usage contextual information, e.g., temporal information, application usage, call logs, WiFi status, Cell ID, etc. In [2] and [13] for instance, the researchers applied various machine learning techniques to accomplish prediction tasks such as next-time slot location prediction and nextplace prediction. In particular, they exploited how different combinations of contextual features related to smartphone usage can affect prediction accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…Previous research in this filed mainly only focus on discovering the significant places or predicting the transition among the significant places [1], [2], [3]. However, these research neglect the data sampled at the places where one stay for a relatively short time, for instance, in the middle of transitions.…”

Section: Introductionmentioning

confidence: 99%

A Probabilistic Approach for Discovering Daily Human Mobility Patterns with Mobile Data

Qian¹,

Lauri²,

Gechter³

2019

Preprint

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Discovering human mobility patterns with geolocation data collected from smartphone users has been a hot research topic in recent years. In this paper, we attempt to discover daily mobile patterns based on GPS data. We view this problem from a probabilistic perspective in order to explore more information from the original GPS data compared to other conventional methods. A non-parameter Bayesian modeling method, Infinite Gaussian Mixture Model, is used to estimate the probability density for the daily mobility. Then, we use Kullback-Leibler divergence as the metrics to measure the similarity of different probability distributions. And combining Infinite Gaussian Mixture Model and Kullback-Leibler divergence, we derived an automatic clustering algorithm to discover mobility patterns for each individual user without setting the number of clusters in advance. In the experiments, the effectiveness of our method is validated on the real user data collected from different users. The results show that the IGMM-based algorithm outperforms the GMM-based algorithm. We also test our methods on the dataset with different lengths to discover the minimum data length for discovering mobility patterns.

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Selecting Individual and Population Models for Predicting Human Mobility

Cited by 15 publications

References 43 publications

Implementation of Group-Based Human Movement Model in Opportunistic Network

Implementation of Group-Based Human Movement Model in Opportunistic Network

Mobility Prediction-Based Optimisation and Encryption of Passenger Traffic-Flows Using Machine Learning

A Probabilistic Approach for Discovering Daily Human Mobility Patterns with Mobile Data

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