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Weak Nodes Detection in Urban Transport Systems: Planning for Resilience in Singapore
... The world's population is continuously making urban centers their home, and the result is that large cities are now overpopulated. About 54% of the world's population lives in urban areas, especially in metropolises where better working and entertainment opportunities are available [1]. In cities, various jobs, education and training platforms, and fast communication modes are available within a few kilometers. ...
... For the taxi driver's behavioral feature mapping, we map the categorical features of the taxi driver. The first important item is time; we express the time of day in hours, e.g., h ∈ [1,24]. The total number of days in a week is represented as weekdays, wkd = [1,7]. ...
... The first important item is time; we express the time of day in hours, e.g., h ∈ [1,24]. The total number of days in a week is represented as weekdays, wkd = [1,7]. On weekdays, days are divided into different categories, such as working day, weekend, holiday, and pre-holiday. ...
This paper uses a neural network approach transformer of taxi driver behavior to predict the next destination with geographical factors. The problem of predicting the next destination is a well-studied application of human mobility, for reducing traffic congestion and optimizing the electronic dispatching system’s performance. According to the Intelligent Transport System (ITS), this kind of task is usually modeled as a multi-class problem. We propose the novel model Deep Wide Spatial-Temporal-Based Transformer Networks (DWSTTNs). In our approach, the encoder and decoder are the transformer’s primary units; with the help of Location-Based Social Networks (LBSN), we encode the geographical information based on visited semantic locations. In particular, we trained our model for the exact longitude and latitude coordinates to predict the next destination. The benefit in the real world of this kind of research is to reduce the customer waiting time for a ride and driver waiting time to pick up a customer. Taxi companies can also optimize their management to improve their company’s service, while urban transport planner can use this information to better plan the urban traffic. We conducted extensive experiments on two real-word datasets, Porto and Manhattan, and the performance was improved compared to the previous models.
... The field of Network Science has extensively studied such spatial networks, first from a single-layer and more recently with a multilayer perspective, with special attention to transportation networks (Lin and Ban, 2013;Barthelemy, 2011;Ding et al., 2019), focusing on topological properties (Jiang and Claramunt, 2004;Cardillo et al., 2006;Barthelemy and Flammini, 2008;Batty, 2008;Barthelemy, 2011;Strano et al., 2013;Louf and Barthelemy, 2014;Boeing, 2020), centrality metrics (Crucitti et al., 2008;Boeing, 2018;Kirkley et al., 2018), and growth processes (Makse et al., 1995;Strano et al., 2012;Szell et al., 2021). Other topics include the impact of the street networks on pedestrian volume (Hajrasouliha and Yin, 2015), accessibility and vitality of cities (De Nadai et al., 2016;Biazzo et al., 2019;Natera Orozco et al., 2019), resilience of transportation networks (Baggag et al., 2018;Ferretti et al., 2019;Natera Orozco et al., 2020a). These recent approaches can be seen as the beginning of emerging fields like a Science of Cities or Urban Data Science which exploit new large-scale urban datasets with quantitative tools from physics, geoinformatics, and data/network science (Batty, 2013;Resch and Szell, 2019). ...
... To mimic realistic trips, Baggag et al. introduced several constrains on the complexity of the trips, for instance limiting the maximum number of transport mode changes. More recently Ferretti et al. (2019) used the multiplex framework to model Singapore's public transportation infrastructure and test its resilience against floods in the city in different scenarios, finding that the system is extremely resilient as it faces the first significant disruption only after the removal of 50% of it edges. ...
Transportation networks, from bicycle paths to buses and railways, are the backbone of urban mobility. In large metropolitan areas, the integration of different transport modes has become crucial to guarantee the fast and sustainable flow of people. Using a network science approach, multimodal transport systems can be described as multilayer networks, where the networks associated to different transport modes are not considered in isolation, but as a set of interconnected layers. Despite the importance of multimodality in modern cities, a unified view of the topic is currently missing. Here, we provide a comprehensive overview of the emerging research areas of multilayer transport networks and multimodal urban mobility, focusing on contributions from the interdisciplinary fields of complex systems, urban data science, and science of cities. First, we present an introduction to the mathematical framework of multilayer networks. We apply it to survey models of multimodal infrastructures, as well as measures used for quantifying multimodality, and related empirical findings. We review modelling approaches and observational evidence in multimodal mobility and public transport system dynamics, focusing on integrated real-world mobility patterns, where individuals navigate urban systems using different transport modes. We then provide a survey of freely available datasets on multimodal infrastructure and mobility, and a list of open source tools for their analyses. Finally, we conclude with an outlook on open research questions and promising directions for future research.
... Urban population is increasing strikingly and human mobility is becoming more complex and bulky, affecting crucial aspects of people lives such as the spreading of viral diseases (e.g., the COVID-19 pandemic) [105,109,131,134,145,159], the behavior of people in case of natural disasters [87,178,204], the public and private transportation and the resulting traffic volumes [34,61,98,156], the well-being of citizens [141,179,197]. The severity of air pollution, energy and water consumption [21,26,130,182]. ...
The study of human mobility is crucial due to its impact on several aspects of our society, such as disease spreading, urban planning, well-being, pollution, and more. The proliferation of digital mobility data, such as phone records, GPS traces, and social media posts, combined with the predictive power of artificial intelligence, triggered the application of deep learning to human mobility. Existing surveys focus on single tasks, data sources, mechanistic or traditional machine learning approaches, while a comprehensive description of deep learning solutions is missing. This survey provides a taxonomy of mobility tasks, a discussion on the challenges related to each task and how deep learning may overcome the limitations of traditional models, a description of the most relevant solutions to the mobility tasks described above, and the relevant challenges for the future. Our survey is a guide to the leading deep learning solutions to next-location prediction, crowd flow prediction, trajectory generation, and flow generation. At the same time, it helps deep learning scientists and practitioners understand the fundamental concepts and the open challenges of the study of human mobility.
... Under simulation-based approaches, existing studies have employed several simulation tools including GISbased applications, SWMM, City CAT, Spatial Importance Measure (SIM), Macroscopic Fundamental Diagram (MFD) etc. [15], [16]. A few of the recent studies have utilised advanced computational assessment methods based on Machine learning algorithms [17], [18]. ...
This study presents a methodology to assess transport network resilience to urban flooding. The proposed methodology is developed based on the centrality measures and graph theory. The study utilises Open-Source GIS tools to compute betweenness and closeness centrality values. The case study was carried out in Greater Colombo
- Sri Lanka, with reference to three significant urban flooding events in 2010, 2016, and 2017. The study assessed the resilience of road network in terms of topological impacts and accessibility changes.
The results revealed three key findings. First, over 60% of road network revealed a significant change in its topological structural coherence during each flooding event. This was particularly pronounced in vehicular movements relative to pedestrian movements. Second, the study revealed a redundant depreciation of the transport accessibility as it shifted from city centre to peripheral areas creating temporary accessibility hotpots in the periphery. Third, a significant drawback of the resilience of road network was identified in terms of the deviation from the shortest path, increasing the travel time and trip length. In overall, the study concluded that the proposed methodology can be utilised as a planning and designing tool to assess road network`s resilience devising precautionary measures to mitigate disaster risk.
... Urban population is increasing strikingly and human mobility is becoming more complex and bulky, affecting crucial aspects of people lives such as the spreading of viral diseases (e.g., the COVID-19 pandemic) [101,105,127,130,141,156], the behavior of people in case of natural disasters [83,175,199], the public and private transportation and the resulting traffic volumes [31,58,94,153], the well-being of citizens [137,176,192], the severity of air pollution, energy and water consumption [19,126,178]. Furthermore, crowds' movement between cities is influenced by migrations from rural to urban areas, such as those induced by natural disasters, climate change, and conflicts [2,70,117,144,149,166,169]. ...
The study of human mobility is crucial due to its impact on several aspects of our society, such as disease spreading, urban planning, well-being, pollution, and more. The proliferation of digital mobility data, such as phone records, GPS traces, and social media posts, combined with the predictive power of artificial intelligence, triggered the application of deep learning to human mobility. Existing surveys focus on single tasks, data sources, mechanistic or traditional machine learning approaches, while a comprehensive description of deep learning solutions is missing. This survey provides a taxonomy of mobility tasks, a discussion on the challenges related to each task and how deep learning may overcome the limitations of traditional models, a description of the most relevant solutions to the mobility tasks described above and the relevant challenges for the future. Our survey is a guide to the leading deep learning solutions to next-location prediction, crowd flow prediction, trajectory generation, and flow generation. At the same time, it helps deep learning scientists and practitioners understand the fundamental concepts and the open challenges of the study of human mobility.
... In our work we make use of these concepts to model in a similar way the mobility of culturally relevant people. In particular, inspired by multidimensional network theory and it recent applications in modeling human mobility [27,28,29], we propose a multilevel approach to cultural mobility. In this framework, every cultural discipline works as a separate system described by a cultural radiation model. ...
The steady growth of digitized historical information is continuously stimulating new different approaches to the fields of Digital Humanities and Computational Social Science. In this work, we use Natural Language Processing techniques to retrieve large amounts of historical information from Wikipedia. In particular, the pages of a set of historically notable individuals are processed to catch the locations and the date of people's movements. This information is then structured in a geographical network of mobility patterns. We analyze the mobility of historically notable individuals from different perspectives to better understand the role of migrations and international collaborations in the context of innovation and cultural development. In this work, we first present some general characteristics of the dataset from a social and geographical perspective. Then, we build a spatial network of cities, and we model and quantify the tendency to explore by a set of people that can be considered historically and culturally notable. In this framework, we show that by using a multilevel radiation model for human mobility, we are able to catch important features of migration's behavior. Results show that the choice of the target migration place for historically and culturally relevant people is limited to a small number of locations and that it depends on the discipline a notable is interested in and on the number of opportunities she/he can find there.
... In our work we make use of these concepts to model in a similar way the mobility of culturally relevant people. In particular, inspired by multidimensional network theory and its recent applications in modeling human mobility [15,31,32], we propose a multilevel approach to cultural mobility. In this framework, every cultural discipline works as a separate system described by a cultural radiation model. ...
The steady growth of digitized historical information is continuously stimulating new different approaches to the fields of Digital Humanities and Computational Social Science. In this work we use Natural Language Processing techniques to retrieve large amounts of historical information from Wikipedia. In particular, the pages of a set of historically notable individuals are processed to catch the locations and the date of people’s movements. This information is then structured in a geographical network of mobility patterns.
We analyze the mobility of historically notable individuals from different perspectives to better understand the role of migrations and international collaborations in the context of innovation and cultural development. In this work, we first present some general characteristics of the dataset from a social and geographical perspective. Then, we build a spatial network of cities, and we model and quantify the tendency to explore of a set of people that can be considered as historically and culturally notable. In this framework, we show that by using a multilevel radiation model for human mobility, we are able to catch important features of migration’s behavior. Results show that the choice of the target migration place for historically and culturally relevant people is limited to a small number of locations and that it depends on the discipline a notable is interested in and on the number of opportunities she/he can find there.
... In a smart city environment, urban data are captured by sensors, actuators, and mobile devices, and then analyzed based on network infrastructures [1]. It goes without saying that using such data opens the door to several applications, including forecasting of urban flows in order to improve and integrate the dimensions of the physical, digital and institutional spaces of a regional agglomeration [2]. ...
In this paper, we study how to model taxi drivers’ behaviour and geographical information for an interesting and challenging task: the next destination prediction in a taxi journey. Predicting the next location is a well studied problem in human mobility, which finds several applications in real-world scenarios, from optimizing the efficiency of electronic dispatching systems to predicting and reducing the traffic jam. This task is normally modeled as a multiclass classification problem, where the goal is to select, among a set of already known locations, the next taxi destination. We present a Recurrent Neural Network (RNN) approach that models the taxi drivers’ behaviour and encodes the semantics of visited locations by using geographical information from Location-Based Social Networks (LBSNs). In particular, RNNs are trained to predict the exact coordinates of the next destination, overcoming the problem of producing, in output, a limited set of locations, seen during the training phase. The proposed approach was tested on the ECML/PKDD Discovery Challenge 2015 dataset — based on the city of Porto —, obtaining better results with respect to the competition winner, whilst using less information, and on Manhattan and San Francisco datasets.
This study presents a method to analyze the most critical elements of the public road system concerning outer effects which hinder the normal operation of the whole system. The surveyed public road network in Budapest, Hungary is studied as a graph: Dijkstra’s algorithm is applied to find the shortest path, and the Boykov-Kolmogorov method is used to calculate the maximum flow of the network. Those elements are identified whose damage can critically influence the operation of the network, and where the infrastructure available for rescue teams has a bottleneck. Finally, the Wilcoxon post hoc test was applied with Bonferroni correction. The tests have proven that the new method can successfully identify the critical vulnerabilities of the network to determine its weak points by considering reduced road capacities and the increased needs for transportation arising due to a disaster. This pilot study confirmed that after the elimination of the problems in statistical methods, the new framework can robustly identify those road network elements whose development is of key importance from a disaster management perspective.
Transportation networks, from bicycle paths to buses and railways, are the backbone of urban mobility. In large metropolitan areas, the integration of different transport modes has become crucial to guarantee the fast and sustainable flow of people. Using a network science approach, multimodal transport systems can be described as multilayer networks, where the networks associated to different transport modes are not considered in isolation, but as a set of interconnected layers. Despite the importance of multimodality in modern cities, a unified view of the topic is currently missing. Here, we provide a comprehensive overview of the emerging research areas of multilayer transport networks and multimodal urban mobility, focusing on contributions from the interdisciplinary fields of complex systems, urban data science, and science of cities. First, we present an introduction to the mathematical framework of multilayer networks. We apply it to survey models of multimodal infrastructures, as well as measures used for quantifying multimodality, and related empirical findings. We review modeling approaches and observational evidence in multimodal mobility and public transport system dynamics, focusing on integrated real-world mobility patterns, where individuals navigate urban systems using different transport modes. We then provide a survey of freely available datasets on multimodal infrastructure and mobility, and a list of open-source tools for their analyses. Finally, we conclude with an outlook on open research questions and promising directions for future research.
Public transportation plays a pivotal part in urban traffic systems, where enhancing the efficiency of public transport is one of the essential strategies for solving the traffic congestion. In this paper, we propose a novel method to define the weights of edges which connected neighbor stations, by considering multi-layer structural and functional properties. To set the structural weights, a three-layer network is constructed to show the importance of links in the whole public transport system rather than a single traffic mode. Besides, the functional weight represents transport capacity of bus lines, which helps to improve the efficiency of public transport. In order to prove the practicality and reliability of our proposed model, the experiment is conducted by comparing with the traditional index. The experimental results demonstrate that our proposed model is superior to the state-of-the-art models. Moreover, we propose an optimization algorithm to improve single bus line’s efficiency without losing the performance of the whole network.
In this paper, we study how to model taxi drivers' behavior and geographical information for an interesting and challenging task: the next destination prediction in a taxi journey. Predicting the next location is a well-studied problem in human mobility, which finds several applications in real-world scenarios, from optimizing the efficiency of electronic dispatching systems to predicting and reducing the traffic jam. This task is normally modeled as a multiclass classification problem, where the goal is to select, among a set of already known locations, the next taxi destination. We present a Recurrent Neural Network (RNN) approach that models the taxi drivers' behavior and encodes the semantics of visited locations by using geographical information from Location-Based Social Networks (LBSNs). In particular, the RNNs are trained to predict the exact coordinates of the next destination, overcoming the problem of producing, in output, a limited set of locations, seen during the training phase. The proposed approach was tested on the ECML/PKDD Discovery Challenge 2015 dataset--based on the city of Porto--, obtaining better results with respect to the competition winner, whilst using less information, and on Manhattan and San Francisco datasets.
The inclusion of tracking technologies in personal devices opened the doors to the analysis of large sets of mobility data like GPS traces and call detail records. This tutorial presents an overview of both modeling principles of human mobility and machine learning models applicable to specific problems. We review the state of the art of five main aspects in human mobility: (1) human mobility data landscape; (2) key measures of individual and collective mobility; (3) generative models at the level of individual, population and mixture of the two; (4) next location prediction algorithms; (5) applications for social good. For each aspect, we show experiments and simulations using the Python library ”scikit-mobility” developed by the presenters of the tutorial.
Urban transportation systems are vulnerable to congestion, accidents, weather, special events, and other costly delays. Whereas typical policy responses prioritize reduction of delays under normal conditions to improve the efficiency of urban road systems, analytic support for investments that improve resilience (defined as system recovery from additional disruptions) is still scarce. In this effort, we represent paved roads as a transportation network by mapping intersections to nodes and road segments between the intersections to links. We built road networks for 40 of the urban areas defined by the U.S. Census Bureau. We developed and calibrated a model to evaluate traffic delays using link loads. The loads may be regarded as traffic-based centrality measures, estimating the number of individuals using corresponding road segments. Efficiency was estimated as the average annual delay per peak-period auto commuter, and modeled results were found to be close to observed data, with the notable exception of New York City. Resilience was estimated as the change in efficiency resulting from roadway disruptions and was found to vary between cities, with increased delays due to a 5% random loss of road linkages ranging from 9.5% in Los Angeles to 56.0% in San Francisco. The results demonstrate that many urban road systems that operate inefficiently under normal conditions are nevertheless resilient to disruption, whereas some more efficient cities are more fragile. The implication is that resilience, not just efficiency, should be considered explicitly in roadway project selection and justify investment opportunities related to disaster and other disruptions.
Understanding and modeling the mobility of individuals is of paramount importance for public health. In particular, mobility characterization is key to predict the spatial and temporal diffusion of human-transmitted infections. However, the mobility behavior of a person can also reveal relevant information about her/his health conditions. In this paper, we study the impact of people mobility behaviors for predicting the future presence of flu-like and cold symptoms (i.e. fever, sore throat, cough, shortness of breath, headache, muscle pain, malaise, and cold). To this end, we use the mobility traces from mobile phones and the daily self-reported flu-like and cold symptoms of 29 individuals from February 20, 2013 to March 21, 2013. First of all, we demonstrate that daily symptoms of an individual can be predicted by using his/her mobility trace characteristics (e.g. total displacement, radius of gyration, number of unique visited places, etc.). Then, we present and validate models that are able to successfully predict the future presence of symptoms by analyzing the mobility patterns of our individuals. The proposed methodology could have a societal impact opening the way to customized mobile phone applications, which may detect and suggest to the user specific actions in order to prevent disease spreading and minimize the risk of contagion.
The social functions of urbanized areas are highly dependent on and supported by the convenient access to public transportation systems, particularly for the less privileged populations who have restrained auto ownership. To accurately evaluate the public transit accessibility, it is critical to capture the spatiotemporal variation of transit services. This can be achieved by measuring the shortest paths or minimum travel time between origin-destination (OD) pairs at each time-of-day (e.g. every minute). In recent years, General Transit Feed Specification (GTFS) data has been gaining popularity for between-station travel time estimation due to its interoperability in spatiotemporal analytics. Many software packages, such as ArcGIS, have developed toolbox to enable the travel time estimation with GTFS. They perform reasonably well in calculating travel time between OD pairs for a specific time-of-day (e.g. 8:00 AM), yet can become computational inefficient and unpractical with the increase of data dimensions (e.g. all times-of-day and large network). In this paper, we introduce a new algorithm that is computationally elegant and mathematically efficient to address this issue. An open-source toolbox written in C++ is developed to implement the algorithm. We implemented the algorithm on City of St. George’s transit network to showcase the accessibility analysis enabled by the toolbox. The experimental evidence shows significant reduction on computational time. The proposed algorithm and toolbox presented is easily transferable to other transit networks to allow transit agencies and researchers perform high resolution transit performance analysis.
Recent years have witnessed an explosion of extensive geolocated datasets related to human movement, enabling scientists to quantitatively study individual and collective mobility patterns, and to generate models that can capture and reproduce the spatiotemporal structures and regularities in human trajectories. The study of human mobility is especially important for applications such as estimating migratory flows, traffic forecasting, urban planning, and epidemic modeling. In this survey, we review the approaches developed to reproduce various mobility patterns, with the main focus on recent developments. This review can be used both as an introduction to the fundamental modeling principles of human mobility, and as a collection of technical methods applicable to specific mobility-related problems. The review organizes the subject by differentiating between individual and population mobility and also between short-range and long-range mobility. Throughout the text the description of the theory is intertwined with real-world applications.
Public transit is described by a wide range of data, which include sensor data, open data, and social network data. Data come in large real-time streams, and are heterogeneous. How to integrate such data in real time? We propose MOBility ANAlyzer (MOBANA), a distributed stream-based framework. MOBANA deals with the integration of heterogeneous information, processing efficiency, and redundancy reduction. As far as integration is concerned, MOBANA integrates data at different layers, and converts them into exchangeable data formats. Specifically, to integrate feed information, MOBANA uses an improved incremental text classifier, based on Kullback Leibler distance. As far as efficiency is concerned, MOBANA is implemented by distributed stream processing engine and distributed messaging system, which enable scalable, efficient, and reliable real-time processing. Specifically, within the transport domain, MOBANA identifies the real-time position of vehicles by an as-needed adjustment of planned position against the real-time position, thus dropping network load. As far as redundancy is concerned, MOBANA filters tweets through a three-fold similarity analysis, which encompasses geo-location, text, and image. In addition, MOBANA is a complete framework, which has been tested as a pilot with real data in the city of Pavia, Italy.
In this study, with Singapore as an example, we demonstrate how we can use mobile phone call detail record (CDR) data, which contains millions of anonymous users, to extract individual mobility networks comparable to the activity-based approach. Such an approach is widely used in the transportation planning practice to develop urban micro simulations of individual daily activities and travel; yet it depends highly on detailed travel survey data to capture individual activity-based behavior. We provide an innovative data mining framework that synthesizes the state-of-the-art techniques in extracting mobility patterns from raw mobile phone CDR data, and design a pipeline that can translate the massive and passive mobile phone records to meaningful spatial human mobility patterns readily interpretable for urban and transportation planning purposes. With growing ubiquitous mobile sensing, and shrinking labor and fiscal resources in the public sector globally, the method presented in this research can be used as a low-cost alternative for transportation and planning agencies to understand the human activity patterns in cities, and provide targeted plans for future sustainable development.
Significance
Social capital ties are ubiquitous in modern life. For societies with people and landscapes tightly connected, in variable or marginal ecosystems, and with unreliable market sectors, social relations are critical. Each relation is a potential source of food, information, cash, labor, or expertise. Here, we present an analysis of multiplex, directed, and weighted networks representing actual flows of subsistence-related goods and services among households in three remote indigenous Alaska communities exposed to both extreme climate change and industrial development. We find that the principal challenge to the robustness of such communities is the loss of key households and the erosion of cultural ties linked to sharing and cooperative social relations rather than resource depletion.
Significance
Individual mobility models are important in a wide range of application areas. Current mainstream urban mobility models require sociodemographic information from costly manual surveys, which are in small sample sizes and updated in low frequency. In this study, we propose an individual mobility modeling framework, TimeGeo, that extracts required features from ubiquitous, passive, and sparse digital traces in the information and communication technology era. The model is able to generate individual trajectories in high spatial–temporal resolutions, with interpretable mechanisms and parameters capturing heterogeneous individual travel choices. The modeling framework can flexibly adapt to input data with different resolutions, and be further extended for various modeling purposes.
Information technologies today can inform each of us about the best alternatives for shortest paths from origins to destinations, but they do not contain incentives or alternatives that manage the information efficiently to get collective benefits. To obtain such benefits, we need to have not only good estimates of how the traffic is formed but also to have target strategies to reduce enough vehicles from the best possible roads in a feasible way. The opportunity is that during large events the traffic inconveniences in large cities are unusually high, yet temporary, and the entire population may be more willing to adopt collective recommendations for social good. In this paper, we integrate for the first time big data resources to quantify the impact of events and propose target strategies for collective good at urban scale. In the context of the Olympic Games in Rio de Janeiro, we first predict the expected increase in traffic. To that end, we integrate data from: mobile phones, Airbnb, Waze, and transit information, with game schedules and information of venues. Next, we evaluate the impact of the Olympic Games to the travel of commuters, and propose different route choice scenarios during the peak hours. Moreover, we gather information on the trips that contribute the most to the global congestion and that could be redirected from vehicles to transit. Interestingly, we show that (i) following new route alternatives during the event with individual shortest path can save more collective travel time than keeping the routine routes, uncovering the positive value of information technologies during events; (ii) with only a small proportion of people selected from specific areas switching from driving to public transport, the collective travel time can be reduced to a great extent. Results are presented on-line for the evaluation of the public and policy makers.
Human mobility modelling is of fundamental importance in a wide range of applications, such as the developing of protocols for mobile ad hoc networks or for what-if analysis and simulation in urban ecosystems. Current generative models generally fail in accurately reproducing the individuals' recurrent daily schedules and at the same time in accounting for the possibility that individuals may break the routine and modify their habits during periods of unpredictability of variable duration. In this article we present DITRAS (DIary-based TRAjectory Simulator), a framework to simulate the spatio-temporal patterns of human mobility in a realistic way. DITRAS operates in two steps: the generation of a mobility diary and the translation of the mobility diary into a mobility trajectory. The mobility diary is constructed by a Markov model which captures the tendency of individuals to follow or break their routine. The mobility trajectory is produced by a model based on the concept of preferential exploration and preferential return. We compare DITRAS with real mobility data and synthetic data produced by other spatio-temporal mobility models and show that it reproduces the statistical properties of real trajectories in an accurate way.
Modeling the properties of individual human mobility is a challenging task that has received increasing attention in the last decade. Since mobility is a complex system, when modeling individual human mobility one should take into account that human movements at a collective level influence, and are influenced by, human movement at an individual level. In this paper we propose the $d$-EPR model, which exploits collective information and the gravity model to drive the movements of an individual and the exploration of new places on the mobility space. We implement our model to simulate the mobility of thousands synthetic individuals, and compare the synthetic movements with real trajectories of mobile phone users and synthetic trajectories produced by a prominent individual mobility model. We show that the distributions of global mobility measures computed on the trajectories produced by the $d$-EPR model are much closer to empirical data, highlighting the importance of considering collective information when simulating individual human mobility.
The Death and Life of Great American Cities was written in 1961 and is now one of the most influential book in city planning. In it, Jane Jacobs proposed four conditions that promote life in a city. However, these conditions have not been empirically tested until recently. This is mainly because it is hard to collect data about "city life". The city of Seoul recently collected pedestrian activity through surveys at an unprecedented scale, with an effort spanning more than a decade, allowing researchers to conduct the first study successfully testing Jacobs's conditions. In this paper, we identify a valuable alternative to the lengthy and costly collection of activity survey data: mobile phone data. We extract human activity from such data, collect land use and socio-demographic information from the Italian Census and Open Street Map, and test the four conditions in six Italian cities. Although these cities are very different from the places for which Jacobs's conditions were spelled out (i.e., great American cities) and from the places in which they were recently tested (i.e., the Asian city of Seoul), we find those conditions to be indeed associated with urban life in Italy as well. Our methodology promises to have a great impact on urban studies, not least because, if replicated, it will make it possible to test Jacobs's theories at scale.
Rapid urbanization and increasing demand for transportation burdens urban road infrastructures. The interplay of number of vehicles and available road capacity on their routes determines the level of congestion. Although approaches to modify demand and capacity exist, the possible limits of congestion alleviation by only modifying route choices have not been systematically studied. Here we couple the road networks of five diverse cities with the travel demand profiles in the morning peak hour obtained from billions of mobile phone traces to comprehensively analyse urban traffic. We present that a dimensionless ratio of the road supply to the travel demand explains the percentage of time lost in congestion. Finally, we examine congestion relief under a centralized routing scheme with varying levels of awareness of social good and quantify the benefits to show that moderate levels are enough to achieve significant collective travel time savings.
The study of socio-technical systems has been revolutionized by the unprecedented amount of digital records that are constantly being produced by human activities such as accessing Internet services, using mobile devices, and consuming energy and knowledge. In this paper, we describe the richest open multi-source dataset ever released on two geographical areas. The dataset is composed of telecommunications, weather, news, social networks and electricity data from the city of Milan and the Province of Trentino. The unique multi-source composition of the dataset makes it an ideal testbed for methodologies and approaches aimed at tackling a wide range of problems including energy consumption, mobility planning, tourist and migrant flows, urban structures and interactions, event detection, urban well-being and many others.
The availability of massive digital traces of human whereabouts has offered a series of novel insights on the quantitative patterns characterizing human mobility. In particular, numerous recent studies have lead to an unexpected consensus: the considerable variability in the characteristic travelled distance of individuals coexists with a high degree of predictability of their future locations. Here we shed light on this surprising coexistence by systematically investigating the impact of recurrent mobility on the characteristic distance travelled by individuals. Using both mobile phone and GPS data, we discover the existence of two distinct classes of individuals: returners and explorers. As existing models of human mobility cannot explain the existence of these two classes, we develop more realistic models able to capture the empirical findings. Finally, we show that returners and explorers play a distinct quantifiable role in spreading phenomena and that a correlation exists between their mobility patterns and social interactions.
Human mobility in a city represents a fascinating complex system that combines social interactions, daily constraints and random explorations. New collections of data that capture human mobility not only help us to understand their underlying patterns but also to design intelligent systems. Bringing us the opportunity to reduce traffic and to develop other applications that make cities more adaptable to human needs. In this paper, we propose an adaptive routing strategy which accounts for individual constraints to recommend personalized routes and, at the same time, for constraints imposed by the collectivity as a whole. Using big data sets recently released during the Telecom Italia Big Data Challenge, we show that our algorithm allows us to reduce the overall traffic in a smart city thanks to synergetic effects, with the participation of individuals in the system, playing a crucial role.
Significance
Network theory has been exploited in the last decades to deepen our comprehension of complex systems. However, real-world complex systems exhibit multiple levels of relationships and require modeling by interconnected networks, characterizing interactions on several levels simultaneously. Questions such as “what is the efficiency of exploration of a city using the multiple transportation layers, like subway and bus?” and “what is its resilience to failures?” have to be answered using the multiplex framework. Here, we introduce fundamental mechanisms to perform such exploration, using random walks on multilayer networks, and we show how the topological structure, together with the navigation strategy, influences the efficiency in exploring the whole structure.
An analysis of the ways that software creates new spatialities in everyday life, from supermarket checkout lines to airline flight paths.
After little more than half a century since its initial development, computer code is extensively and intimately woven into the fabric of our everyday lives. From the digital alarm clock that wakes us to the air traffic control system that guides our plane in for a landing, software is shaping our world: it creates new ways of undertaking tasks, speeds up and automates existing practices, transforms social and economic relations, and offers new forms of cultural activity, personal empowerment, and modes of play. In Code/Space, Rob Kitchin and Martin Dodge examine software from a spatial perspective, analyzing the dyadic relationship of software and space. The production of space, they argue, is increasingly dependent on code, and code is written to produce space. Examples of code/space include airport check-in areas, networked offices, and cafés that are transformed into workspaces by laptops and wireless access. Kitchin and Dodge argue that software, through its ability to do work in the world, transduces space. Then Kitchin and Dodge develop a set of conceptual tools for identifying and understanding the interrelationship of software, space, and everyday life, and illustrate their arguments with rich empirical material. And, finally, they issue a manifesto, calling for critical scholarship into the production and workings of code rather than simply the technologies it enables—a new kind of social science focused on explaining the social, economic, and spatial contours of software.
The advent of social media have allowed us to build massive networks of weak ties: acquaintances and nonintimate ties we use all the time to spread information and thoughts. Conversely, strong ties are the people we really trust, people whose social circles tightly overlap with our own and, often, they are also the people most like us. Unfortunately, the social media do not incorporate tie strength in the creation and management of relationships, and treat all users the same: friend or stranger, with little or nothing in between. In the current work, we address the challenging issue of detecting on online social networks the strong and intimate ties from the huge mass of such mere social contacts. In order to do so, we propose a novel multidimensional definition of tie strength which exploits the existence of multiple online social links between two individuals. We test our definition on a multidimensional network constructed over users in Foursquare, Twitter and Facebook, analyzing the structural role of strong e weak links, and the correlations with the most common similarity measures.
A network representation is useful for describing the structure of a large
variety of complex systems. However, most real and engineered systems have
multiple subsystems and layers of connectivity, and the data produced by such
systems is very rich. Achieving a deep understanding of such systems
necessitates generalizing "traditional" network theory, and the newfound deluge
of data now makes it possible to test increasingly general frameworks for the
study of networks. In particular, although adjacency matrices are useful to
describe traditional single-layer networks, such a representation is
insufficient for the analysis and description of multiplex and time-dependent
networks. One must therefore develop a more general mathematical framework to
cope with the challenges posed by multi-layer complex systems. In this paper,
we introduce a tensorial framework to study multi-layer networks, and we
discuss the generalization of several important network descriptors and
dynamical processes --including degree centrality, clustering coefficients,
eigenvector centrality, modularity, Von Neumann entropy, and diffusion-- for
this framework. We examine the impact of different choices in constructing
these generalizations, and we illustrate how to obtain known results for the
special cases of single-layer and multiplex networks. Our tensorial approach
will be helpful for tackling pressing problems in multi-layer complex systems,
such as inferring who is influencing whom (and by which media) in multichannel
social networks and developing routing techniques for multimodal transportation
systems.
Introduced in its contemporary form in 1946 (ref. 1), but with roots that go back to the eighteenth century, the gravity law is the prevailing framework with which to predict population movement, cargo shipping volume and inter-city phone calls, as well as bilateral trade flows between nations. Despite its widespread use, it relies on adjustable parameters that vary from region to region and suffers from known analytic inconsistencies. Here we introduce a stochastic process capturing local mobility decisions that helps us analytically derive commuting and mobility fluxes that require as input only information on the population distribution. The resulting radiation model predicts mobility patterns in good agreement with mobility and transport patterns observed in a wide range of phenomena, from long-term migration patterns to communication volume between different regions. Given its parameter-free nature, the model can be applied in areas where we lack previous mobility measurements, significantly improving the predictive accuracy of most of the phenomena affected by mobility and transport processes.
Complex networks have been receiving increasing attention by the scientific community, thanks also to the increasing availability of real-world network data. In the last years, the multidimensional nature of many real world networks has been pointed out, i.e. many networks containing multiple connections between any pair of nodes have been analyzed. Despite the importance of analyzing this kind of networks was recognized by previous works, a complete framework for multidimensional network analysis is still missing. Such a framework would enable the analysts to study different phenomena, that can be either the generalization to the multidimensional setting of what happens inmonodimensional network, or a new class of phenomena induced by the additional degree of complexity that multidimensionality provides in real networks. The aim of this paper is then to give the basis for multidimensional network analysis: we develop a solid repertoire of basic concepts and analytical measures, which takes into account the general structure of multidimensional networks. We tested our framework on a real world multidimensional network, showing the validity and the meaningfulness of the measures introduced, that are able to extract important, nonrandom, information about complex phenomena.
The technologies of mobile communications pervade our society and wireless networks sense the movement of people, generating
large volumes of mobility data, such as mobile phone call records and Global Positioning System (GPS) tracks. In this work,
we illustrate the striking analytical power of massive collections of trajectory data in unveiling the complexity of human
mobility. We present the results of a large-scale experiment, based on the detailed trajectories of tens of thousands private
cars with on-board GPS receivers, tracked during weeks of ordinary mobile activity. We illustrate the knowledge discovery
process that, based on these data, addresses some fundamental questions of mobility analysts: what are the frequent patterns
of people’s travels? How big attractors and extraordinary events influence mobility? How to predict areas of dense traffic
in the near future? How to characterize traffic jams and congestions? We also describe M-Atlas, the querying and mining language
and system that makes this analytical process possible, providing the mechanisms to master the complexity of transforming
raw GPS tracks into mobility knowledge. M-Atlas is centered onto the concept of a trajectory, and the mobility knowledge discovery process can be specified by M-Atlas queries that realize data transformations, data-driven
estimation of the parameters of the mining methods, the quality assessment of the obtained results, the quantitative and visual
exploration of the discovered behavioral patterns and models, the composition of mined patterns, models and data with further
analyses and mining, and the incremental mining strategies to address scalability.
Real-time arrival information can significantly enhance the usability of public transit systems. OneBusAway provides such information to more than 7,000 Seattle-area bus riders ’ mobile devices every day. By helping travelers move from single-occupancy vehicles to public transit systems, communities can reduce traffic congestion as well as its environmental impact. Here, we describe our efforts to increase the satisfaction of current public transit users and help motivate more people to ride. OneBusAway
Despite a century of effort, our understanding of how cities evolve is still woefully inadequate. Recent research, however, suggests that cities are complex systems that mainly grow from the bottom up, their size and shape following well-defined scaling laws that result from intense competition for space. An integrated theory of how cities evolve, linking urban economics and transportation behavior to developments in network science, allometric growth, and fractal geometry, is being slowly developed. This science provides new insights into the resource limits facing cities in terms of the meaning of density, compactness, and sprawl, and related questions of sustainability. It has the potential to enrich current approaches to city planning and replace traditional top-down strategies with realistic city plans that benefit all city dwellers.
Despite their importance for urban planning, traffic forecasting and the spread of biological and mobile viruses, our understanding of the basic laws governing human motion remains limited owing to the lack of tools to monitor the time-resolved location of individuals. Here we study the trajectory of 100,000 anonymized mobile phone users whose position is tracked for a six-month period. We find that, in contrast with the random trajectories predicted by the prevailing Lévy flight and random walk models, human trajectories show a high degree of temporal and spatial regularity, each individual being characterized by a time-independent characteristic travel distance and a significant probability to return to a few highly frequented locations. After correcting for differences in travel distances and the inherent anisotropy of each trajectory, the individual travel patterns collapse into a single spatial probability distribution, indicating that, despite the diversity of their travel history, humans follow simple reproducible patterns. This inherent similarity in travel patterns could impact all phenomena driven by human mobility, from epidemic prevention to emergency response, urban planning and agent-based modelling.
The concept of resilience has received a great deal of attention in the past decades. Starting from the first fundamental definitions offered by Holling, Pimms and Perrings in an economic-ecological modeling context, the present paper explores the ‘evolution’ of the resilience concept – as well as related different measures – in both a continuous and discrete time setting.
From this perspective, the paper explores the relevance of the resilience concept in socio- economic systems, by focussing the attention on the relationships among resilience, transition dynamics and lock-in effects, in particular in the light of the dynamics of technological innovation diffusion and adaptive behaviour of firms. In this framework we will describe an empirical application, in which the resilience and dynamics of the West-German labour market will be investigated. This empirical illustration is offered by making use of an algorithm constructed for detecting Lyapunov exponents, so as to classify the resilience among employment sectors in our case study.
In this paper, we study how to model taxi drivers' behavior and geographical information for an interesting and challenging task: the next destination prediction in a taxi journey. Predicting the next location is a well-studied problem in human mobility, which finds several applications in real-world scenarios, from optimizing the efficiency of electronic dispatching systems to predicting and reducing the traffic jam. This task is normally modeled as a multiclass classification problem, where the goal is to select, among a set of already known locations, the next taxi destination. We present a Recurrent Neural Network (RNN) approach that models the taxi drivers' behavior and encodes the semantics of visited locations by using geographical information from Location-Based Social Networks (LBSNs). In particular, the RNNs are trained to predict the exact coordinates of the next destination, overcoming the problem of producing, in output, a limited set of locations, seen during the training phase. The proposed approach was tested on the ECML/PKDD Discovery Challenge 2015 dataset--based on the city of Porto--, obtaining better results with respect to the competition winner, whilst using less information, and on Manhattan and San Francisco datasets.
Humanitarian relief agencies must assess humanitarian crises occurring in the world to prioritize the aid that can be offered. While the rapidly growing availability of relevant information enables better decisions to be made, it also creates an important challenge: How to find, collect, and categorize this information in a timely manner. To address the problem, we propose a targeted retrieval system that automates these tasks. The system uses historical data collected and labeled by subject matter experts to train a classifier that identifies relevant content. Using this classifier, it deploys a focused crawler to locate and retrieve data at scale. The system also incorporates feedback from subject matter experts to adapt to new concepts and information sources. A novel component of the system is an algorithm for re-crawling that improves the crawler efficiency in retrieving recent data. Our preliminary result shows that the algorithm can increase the freshness of collected data while simultaneously decreasing crawling effort. Furthermore, we show that focused crawling outperforms general crawling in this domain. Our initial prototype has received positive feedback from analysts at the Assessment Capacities Project, a humanitarian response agency.
This paper suggests that as pervasive computing technologies have gained purchase in urban space they have also become more implicitly blended with everyday life and more contingent on information that is inductively compiled from Internet-based data services. It is argued that existing theorizations of the technologically mediated production of urban must engage with the increasingly implicit nature of informational transactions as well as the emergent semantic structuring of information. Drawing on examples of ongoing pervasive computing projects, implicit computing procedures are explored in relation to the mediation of everyday urban life. Literatures from computing science and geographical theory are brought into conversation in order to examine the consequences of a convergence between implicit pervasive technologies and the spaces of everyday life.
The search for scientific bases for confronting problems of social policy is bound to fail, becuase of the nature of these problems. They are wicked problems, whereas science has developed to deal with tame problems. Policy problems cannot be definitively described. Moreover, in a pluralistic society there is nothing like the undisputable public good; there is no objective definition of equity; policies that respond to social problems cannot be meaningfully correct or false; and it makes no sense to talk about optimal solutions to social problems unless severe qualifications are imposed first. Even worse, there are no solutions in the sense of definitive and objective answers.
Multi-topology routing is a new strategy to provide traffic-engineering and resilience in IP networks. In case of network failures, affected traffic demands are routed in intact sub-topologies for which the routing information is predetermined. This paper investigates an optimal design of the topologies with respect to a shortest path protection routing. We formulate mathematical programs for global and local protection schemes and investigate a case study. Our results show that only very few topologies are necessary to provide an optimal protection configuration.
To analyze the resilience of transportation networks, it is proposed to use a quantificational resilience evaluation approach. First, we represent transportation networks by an undirected graph with the nodes as cities and edges as traffic roads. Because the survival ability of transportation of a pair of cities depends on the number of independent paths between them, the resilience of a city node can be evaluated by the weighted average number of reliable independent paths with all other city nodes in the networks. The network resilience can then be calculated by the weighted sum of all node resilience. Based on the recommended approaches, the resilience of a transportation network is evaluated and analyzed. Several interesting conclusions are drawn from the computational results.
Estimating origin-destination flows using mobile phone location data
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The death and life of great italian cities: A mobile phone data perspective
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A multi-scale approach to data-Juij mass migration analysis
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Calabrese, Michele Ferretti, Vincent Lonij, Rahul Nair, Rana Novack,
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- Hugo Barbosa-Filho
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- Ronaldo Menezes
- José J Ramasco
- Filippo Simini
- Marcello Tomasini
Hugo Barbosa-Filho, Marc Barthelemy, Gourab Ghoshal, Charlotte R.
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An efficient general transit feed specification (gtfs) enabled algorithm for dynamic transit accessibility analysis
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