2,197 research outputs found
Entropic measures of individual mobility patterns
Understanding human mobility from a microscopic point of view may represent a
fundamental breakthrough for the development of a statistical physics for
cognitive systems and it can shed light on the applicability of macroscopic
statistical laws for social systems. Even if the complexity of individual
behaviors prevents a true microscopic approach, the introduction of mesoscopic
models allows the study of the dynamical properties for the non-stationary
states of the considered system. We propose to compute various entropy measures
of the individual mobility patterns obtained from GPS data that record the
movements of private vehicles in the Florence district, in order to point out
new features of human mobility related to the use of time and space and to
define the dynamical properties of a stochastic model that could generate
similar patterns. Moreover, we can relate the predictability properties of
human mobility to the distribution of time passed between two successive trips.
Our analysis suggests the existence of a hierarchical structure in the mobility
patterns which divides the performed activities into three different
categories, according to the time cost, with different information contents. We
show that a Markov process defined by using the individual mobility network is
not able to reproduce this hierarchy, which seems the consequence of different
strategies in the activity choice. Our results could contribute to the
development of governance policies for a sustainable mobility in modern cities
Modeling Human Mobility Entropy as a Function of Spatial and Temporal Quantizations
The knowledge of human mobility is an integral component of several different branches of research and planning, including delay tolerant network routing, cellular network planning, disease prevention, and urban planning. The uncertainty associated with a person's movement plays a central role in movement predictability studies. The uncertainty can be quantified in a succinct manner using entropy rate, which is based on the information theoretic entropy. The entropy rate is usually calculated from past mobility traces. While the uncertainty, and therefore, the entropy rate depend on the human behavior, the entropy rate is not invariant to spatial resolution and sampling interval employed to collect mobility traces. The entropy rate of a person is a manifestation of the observable features in the person's mobility traces. Like entropy rate, these features are also dependent on spatio-temporal quantization. Different mobility studies are carried out using different spatio-temporal quantization, which can obscure the behavioral differences of the study populations. But these behavioral differences are important for population-specific planning. The goal of dissertation is to develop a theoretical model that will address this shortcoming of mobility studies by separating parameters pertaining to human behavior from the spatial and temporal parameters
Potential destination discovery for low predictability individuals based on knowledge graph
Travelers may travel to locations they have never visited, which we call
potential destinations of them. Especially under a very limited observation,
travelers tend to show random movement patterns and usually have a large number
of potential destinations, which make them difficult to handle for mobility
prediction (e.g., destination prediction). In this paper, we develop a new
knowledge graph-based framework (PDPFKG) for potential destination discovery of
low predictability travelers by considering trip association relationships
between them. We first construct a trip knowledge graph (TKG) to model the trip
scenario by entities (e.g., travelers, destinations and time information) and
their relationships, in which we introduce the concept of private relationship
for complexity reduction. Then a modified knowledge graph embedding algorithm
is implemented to optimize the overall graph representation. Based on the trip
knowledge graph embedding model (TKGEM), the possible ranking of individuals'
unobserved destinations to be chosen in the future can be obtained by
calculating triples' distance. Empirically. PDPFKG is tested using an anonymous
vehicular dataset from 138 intersections equipped with video-based vehicle
detection systems in Xuancheng city, China. The results show that (i) the
proposed method significantly outperforms baseline methods, and (ii) the
results show strong consistency with traveler behavior in choosing potential
destinations. Finally, we provide a comprehensive discussion of the innovative
points of the methodology
On the performance of social-based and location-aware forwarding strategies in urban vehicular networks
High vehicular mobility in urban scenarios originates inter-vehicles communication discontinuities, a highly important factor when designing a forwarding strategy for vehicular networks. Store, carry and forward mechanisms enable the usage of vehicular networks in a large set of applications, such as sensor data collection in IoT, contributing to smart city platforms. This work evaluates the performance of several location-based and social-aware forwarding schemes through emulations and in a real scenario. Gateway Location Awareness (GLA), a location-aware ranking classification, makes use of velocity, heading angle and distance to the gateway, to select the vehicles with higher chance to deliver the information in a shorter period of time, thus differentiating nodes through their movement patterns. Aging Social-Aware Ranking (ASAR) exploits the social behavior of each vehicle, where nodes are ranked based on a historical contact table, differentiating vehicles with a high number of contacts from those who barely contact with other vehicles. To merge both location and social aforementioned algorithms, a HYBRID approach emerges, thus generating a more intelligent mechanism. For each strategy, we evaluate the influence of several parameters in the network performance, as well as we comparatively evaluate the strategies in different scenarios. Experiment results, obtained both in emulated (with real traces of both mobility and vehicular connectivity from a real city-scale urban vehicular network) and real scenarios, show the performance of GLA, ASAR and HYBRID schemes, and their results are compared to lower- and upper-bounds. The obtained results show that these strategies are a good tradeoff to maximize data delivery ratio and minimize network overhead, while making use of mobile networks as a smart city network infrastructure.publishe
- …