Human Mobility has attracted attentions from dierent elds of studies such as epidemic modeling, trac engineering, trac prediction and urban planning. In this survey we review major characteristics of human mobility studies including from trajectory-based studies to studies using graph and network theory. In trajectory-based studies statistical measures such as jump length distribution and radius of gyration are analyzed in order to investigate how people move in their daily life, and if it is possible to model this individual movements and make prediction based on them. Using graph in mobility studies, helps to investigate the dynamic behavior of the system, such as diusion and ow in the network and makes it easier to estimate how much one part of the network inuences another by using metrics like centrality measures. We aim to study population ow in transportation networks using mobility data to derive models and patterns, and to develop new applications in predicting phenomena such as congestion. Human Mobility studies with the new generation of mobility data provided by cellular phone networks, arise new challenges such as data storing, data representation, data analysis and computation complexity. A comparative review of dierent data types used in current tools and applications of Human Mobility studies leads us to new approaches for dealing with mentioned challenges.
 This study used a sample of 100,000 mobile phone users whose trajectory was tracked for six months to study human mobility patterns. Displacements across all users suggest behaviour close to the Levy-flight-like pattern observed previously based on the motion of marked dollar bills, but with a cutoff in the distribution. The origin of the Levy patterns observed in the aggregate data appears to be population heterogeneity and not Levy patterns at the level of the individual. Whilst being hailed as the remedy to the world’s ills, cities will need to adapt in the 21st century. In particular, the role of public transport is likely to increase significantly, and new methods and technics to better plan transit systems are in dire need. This paper examines one fundamental aspect of transit: network centrality. By applying the notion of betweenness centrality to 28 worldwide metro systems, the main goal of this paper is to study the emergence of global trends in the evolution of centrality with network size and examine several individual systems in more detail. Betweenness was notably found to consistently become more evenly distributed with size (i.e. no “winner takes all”) unlike other complex network properties. Two distinct regimes were also observed that are representative of their structure. Moreover, the share of betweenness was found to decrease in a power law with size (with exponent 1 for the average node), but the share of most central nodes decreases much slower than least central nodes (0.87 vs. 2.48). Finally the betweenness of individual stations in several systems were examined, which can be useful to locate stations where passengers can be redistributed to relieve pressure from overcrowded stations. Overall, this study offers significant insights that can help planners in their task to design the systems of tomorrow, and similar undertakings can easily be imagined to other urban infrastructure systems (e.g., electricity grid, water wastewater system, etc.) to develop more sustainable cities. We study fifteen months of human mobility data for one and a half million individuals and find that human mobility traces are highly unique. In fact, in a dataset where the location of an individual is specified hourly, and with a spatial resolution equal to that given by the carrier's antennas, four spatio-temporal points are enough to uniquely identify 95 of the individuals. We coarsen the data spatially and temporally to find a formula for the uniqueness of human mobility traces given their resolution and the available outside information. This formula shows that the uniqueness of mobility traces decays approximately as the 1 10 power of their resolution. Hence, even coarse datasets provide little anonymity. These findings represent fundamental constraints to an individual's privacy and have important implications for the design of frameworks and institutions dedicated to protect the privacy of individuals. The spread of infectious disease epidemics is mediated by human travel. Yet human mobility patterns vary substantially between countries and regions. Quantifying the frequency of travel and length of journeys in well-defined population is therefore critical for predicting the likely speed and pattern of spread of emerging infectious diseases, such as a new influenza pandemic. Here we present the results of a large population survey undertaken in 2007 in two areas of China: Shenzhen city in Guangdong province, and Huangshan city in Anhui province. In each area, 10,000 randomly selected individuals were interviewed, and data on regular and occasional journeys collected. Travel behaviour was examined as a function of age, sex, economic status and home location. Women and children were generally found to travel shorter distances than men. Travel patterns in the economically developed Shenzhen region are shown to resemble those in developed and economically advanced middle income countries with a significant fraction of the population commuting over distances in excess of 50 km. Conversely, in the less developed rural region of Anhui, travel was much more local, with very few journeys over 30 km. Travel patterns in both populations were well-fitted by a gravity model with a lognormal kernel function. The results provide the first quantitative information on human travel patterns in modern China, and suggest that a pandemic emerging in a less developed area of rural China might spread geographically sufficiently slowly for containment to be feasible, while spatial spread in the more economically developed areas might be expected to be much more rapid, making containment more difficult. We propose a simplified human regular mobility model to simulate an individual's daily travel with three sequential activities: commuting to workplace, going to do leisure activities and returning home. With the assumption that the individual has a constant travel speed and inferior limit of time at home and in work, we prove that the daily moving area of an individual is an ellipse, and finally obtain an exact solution of the gyration radius. The analytical solution captures the empirical observation well. Traffic delays and congestion are a major source of inefficiency, wasted fuel, and commuter frustration. Measuring and localizing these delays, and routing users around them, is an important step towards reducing the time people spend stuck in traffic. As others have noted, the proliferation of commodity smartphones that can provide location estimates using a variety of sensors---GPS, WiFi, and or cellular triangulation---opens up the attractive possibility of using position samples from drivers' phones to monitor traffic delays at a fine spatiotemporal granularity. This paper presents VTrack, a system for travel time estimation using this sensor data that addresses two key challenges: energy consumption and sensor unreliability. While GPS provides highly accurate location estimates, it has several limitations: some phones don't have GPS at all, the GPS sensor doesn't work in "urban canyons" (tall buildings and tunnels) or when the phone is inside a pocket, and the GPS on many phones is power-hungry and drains the battery quickly. In these cases, VTrack can use alternative, less energy-hungry but noisier sensors like WiFi to estimate both a user's trajectory and travel time along the route. VTrack uses a hidden Markov model (HMM)-based map matching scheme and travel time estimation method that interpolates sparse data to identify the most probable road segments driven by the user and to attribute travel times to those segments. We present experimental results from real drive data and WiFi access point sightings gathered from a deployment on several cars. We show that VTrack can tolerate significant noise and outages in these location estimates, and still successfully identify delay-prone segments, and provide accurate enough delays for delay-aware routing algorithms. We also study the best sampling strategies for WiFi and GPS sensors for different energy cost regimes.  We report on our experience scaling up the Mobile Millennium traffic information system using cloud computing and the Spark cluster computing framework. Mobile Millennium uses machine learning to infer traffic conditions for large metropolitan areas from crowdsourced data, and Spark was specifically designed to support such applications. Many studies of cloud computing frameworks have demonstrated scalability and performance improvements for simple machine learning algorithms. Our experience implementing a real-world machine learning-based application corroborates such benefits, but we also encountered several challenges that have not been widely reported. These include: managing large parameter vectors, using memory efficiently, and integrating with the application's existing storage infrastructure. This paper describes these challenges and the changes they required in both the Spark framework and the Mobile Millennium software. While we focus on a system for traffic estimation, we believe that the lessons learned are applicable to other machine learning-based applications. A parameter-free model predicts patterns of commuting, phone calls and trade using only population density at all intermediate points.  In this paper, we combine the most complete record of daily mobility, based on large-scale mobile phone data, with detailed Geographic Information System (GIS) data, uncovering previously hidden patterns in urban road usage. We find that the major usage of each road segment can be traced to its own - surprisingly few - driver sources. Based on this finding we propose a network of road usage by defining a bipartite network framework, demonstrating that in contrast to traditional approaches, which define road importance solely by topological measures, the role of a road segment depends on both: its betweeness and its degree in the road usage network. Moreover, our ability to pinpoint the few driver sources contributing to the major traffic flow allows us to create a strategy that achieves a significant reduction of the travel time across the entire road system, compared to a benchmark approach.