Mobility in modern cities: looking for physical laws (original) (raw)
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Individual Mobility Patterns in Urban Environment
Proceedings of the 1st International Conference on Complex Information Systems, 2016
The understanding and the characterization of individual mobility patterns in urban environments is important in order to improve liveability and planning of big cities. In relatively recent times, the availability of data regarding human movements have fostered the emergence of a new branch of social studies, with the aim to unveil and study those patterns thanks to data collected by means of geolocalization technologies. In this paper we analyze a large dataset of GPS tracks of cars collected in Rome (Italy). Dividing the drivers in classes according to the number of trips they perform in a day, we show that the sequence of the traveled space connecting two consecutive stops shows a precise behavior so that the shortest trips are performed at the middle of the sequence, when the longest occur at the beginning and at the end when drivers head back home. We show that this behavior is consistent with the idea of an optimization process in which the total travel time is minimized, under the effect of spatial constraints so that the starting points is on the border of the space in which the dynamics takes place.
2017
Nowadays we witness a rapid increase of people mobility as the world population has become more interconnected and is relying on faster transportation methods, simplified connections and shorter commuting times. Unveiling and understanding human mobility patterns have become a crucial issue to support decisions and prediction activities when managing the complexity of the today’s social organization. The strict connections between human mobility patterns, the planning, deployment and management of a variety of public and commercial services have fueled the rise of a vast research activity. Throughout this work, we are more interested and mainly focusing on urban mobility because here most of the human interactions take place and mobility has the greatest impact on management and optimization of public and commercial services. In this thesis, we provided a general framework for dealing with the modeling importance of locations from a per-user perspective and identified a few novel pr...
Statistical laws in urban mobility from microscopic GPS data in the area of Florence
Journal of Statistical Mechanics: Theory and Experiment, 2010
The application of Statistical Physics to social systems is mainly related to the search for macroscopic laws, that can be derived from experimental data averaged in time or space,assuming the system in a steady state. One of the major goals would be to find a connection between the statistical laws to the microscopic properties: for example to understand the nature of the microscopic interactions or to point out the existence of interaction networks. The probability theory suggests the existence of few classes of stationary distributions in the thermodynamics limit, so that the question is if a statistical physics approach could be able to enroll the complex nature of the social systems. We have analyzed a large GPS data base for single vehicle mobility in the Florence urban area, obtaining statistical laws for path lengths, for activity downtimes and for activity degrees. We show also that simple generic assumptions on the microscopic behavior could explain the existence of stationary macroscopic laws, with an universal function describing the distribution. Our conclusion is that understanding the system complexity requires dynamical data-base for the microscopic evolution, that allow to solve both small space and time scales in order to study the transients.
Spatiotemporal Patterns of Urban Human Mobility
Journal of Statistical Physics, 2012
The modeling of human mobility is adopting new directions due to the increasing availability of big data sources from human activity. These sources enclose digital information about daily visited locations of a large number of individuals. Examples of these data include: mobile phone calls, credit card transactions, bank notes dispersal, check-ins in internet applications, among several others. In this study, we consider the data obtained from smart subway fare card transactions to characterize and model urban mobility patterns. We present a simple mobility model for predicting peoples' visited locations using the popularity of places in the city as an interaction parameter between different individuals. This ingredient is sufficient to reproduce several characteristics of the observed travel behavior such as: the number of trips between different locations in the city, the exploration of new places and the frequency of individual visits of a particular location. Moreover, we indicate the limitations of the proposed model and discuss open questions in the current state of the art statistical models of human mobility.
A tale of many cities: universal patterns in human urban mobility
2011
The advent of geographic online social networks such as Foursquare, where users voluntarily signal their current location, opens the door to powerful studies on human movement. In particular the fine granularity of the location data, with GPS accuracy down to 10 meters, and the worldwide scale of Foursquare adoption are unprecedented. In this paper we study urban mobility patterns of people in several metropolitan cities around the globe by analyzing a large set of Foursquare users.
Scaling laws for the movement of people between locations in a large city
Physical Review E, 2003
Large scale simulations of the movements of people in a ''virtual'' city and their analyses are used to generate insights into understanding the dynamic processes that depend on the interactions between people. Models, based on these interactions, can be used in optimizing traffic flow, slowing the spread of infectious diseases, or predicting the change in cell phone usage in a disaster. We analyzed cumulative and aggregated data generated from the simulated movements of 1.6ϫ10 6 individuals in a computer ͑pseudo-agent-based͒ model during a typical day in Portland, Oregon. This city is mapped into a graph with 181 206 nodes representing physical locations such as buildings. Connecting edges model individual's flow between nodes. Edge weights are constructed from the daily traffic of individuals moving between locations. The number of edges leaving a node ͑out-degree͒, the edge weights ͑out-traffic͒, and the edge weights per location ͑total out-traffic͒ are fitted well by power-law distributions. The power-law distributions also fit subgraphs based on work, school, and social/recreational activities. The resulting weighted graph is a ''small world'' and has scaling laws consistent with an underlying hierarchical structure. We also explore the time evolution of the largest connected component and the distribution of the component sizes. We observe a strong linear correlation between the out-degree and total out-traffic distributions and significant levels of clustering. We discuss how these network features can be used to characterize social networks and their relationship to dynamic processes.
Where, when and how people move in large-scale urban networks: the Grenoble saga
HAL (Le Centre pour la Communication Scientifique Directe), 2022
This work studies the mobility of people in the metropolitan city Grenoble in France, building a supply-demand based mobility model which captures the daily movement of people between residences and places of interests called destinations, using time schedules and gating profiles which also accommodate the possibility of imposing restrictions on mobility. The goal of this paper is to build the mobility network in Grenoble answering the three main questions (WWH): Where does mobility happen? When does it happen? How many people move? We provide methods to compute the time gating functions and different parameters for this network. Further, we identify and address the issues encountered during discretization of the continuous-time mobility model. As an application, we also study how different restrictions on the mobility effects the epidemic spread in Grenoble. Finally, we describe the online GTL-covid demonstrator which is being developed by implementing the results of this paper. It is an academic platform which can be used to simulate different scenarios of mobility and visualise its effect on epidemic spread in Grenoble area.
Characterizing the human mobility pattern in a large street network
Physical Review E, 2009
Previous studies demonstrated empirically that human mobility exhibits Lévy flight behaviour. However, our knowledge of the mechanisms governing this Lévy flight behaviour remains limited. Here we analyze over 72 000 people's moving trajectories, obtained from 50 taxicabs during a six-month period in a large street network, and illustrate that the human mobility pattern, or the Lévy flight behaviour, is mainly attributed to the underlying street network. In other words, the goal-directed nature of human movement has little effect on the overall traffic distribution. We further simulate the mobility of a large number of random walkers, and find that (1) the simulated random walkers can reproduce the same human mobility pattern, and (2) the simulated mobility rate of the random walkers correlates pretty well (an R square up to 0.87) with the observed human mobility rate.
Intra-urban human mobility patterns: An urban morphology perspective
2012
This paper provides a new perspective on human motion with an investigation of whether and how patterns of human mobility inside cities are affected by two urban morphological characteristics: compactness and size. Mobile phone data have been collected in eight cities in Northeast China and used to extract individuals' movement trajectories. The massive mobile phone data provides a wide coverage and detailed depiction of individuals' movement in space and time.
Entropic measures of individual mobility patterns
Journal of Statistical Mechanics: Theory and Experiment, 2013
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.