Academia.eduAcademia.edu

Outline

Title
Abstract
Introduction
Related Work
Dataset
Conclusions
References
All Topics
Computer Science

City Indicators for Mobility Data Mining

Profile image of Agnese BONAVITAAgnese BONAVITA

2021

Abstract

Classifying cities and other geographical units is a classical task in urban geography, typically carried out through manual analysis of specific characteristics of the area. The primary objective of this paper is to contribute to this process through the definition of a wide set of city indicators that capture different aspects of the city, mainly based on human mobility and automatically computed from a set of data sources, including mobility traces and road networks. The secondary objective is to prove that such set of characteristics is indeed rich enough to support a simple task of geographical transfer learning, namely identifying which groups of geographical areas can share with each other a basic traffic prediction model. The experiments show that similarity in terms of our city indicators also means better transferability of predictive models, opening the way to the development of more sophisticated solutions that leverage city indicators.

Related papers

Spatial Machine Learning Personal Mobility Predictive Model Trained with Smartphone-Collected Trajectory Data
Renato Filjar

The Journal of CIEES, 2022

Individual and group mobility is an essential information for numerous segments of technology (including transport and logistics), society, and economy. The ability of telecommunications devices, such as smartphones, to collect accurate and reliable data on personal mobility with the embedded sensors, inspires research in personal mobility. We confirm the ability of suitably defined indicators to compare sets of trajectories, and identify outliers/differences among the individual ones. Furthermore, we demonstrate development of a machine learning (ML) regression predictive model based on experimental data collected on the real urban environment of the city of Krapina, Croatia, suitable for utilisation in personal mobility analysis, and traffic and transport planning and optimisation.

View PDFchevron_right
Travel Prediction Methodology in Medium Sized Cities with Gis T Maximum to Minimum Cost Disaggregation
Enrique Eugenio Ruiz Labrador

Universidad de Granada, 2015

This paper describes the design of a traffic assignment model that predicts flows for each segment of an urban network with a higher resolution than a traditional four stage model, retaining the origins and destinations of travel. The research objectives are to determine the traffic intensity in specific areas of the network, and then to identify the origins and destinations of travel to predict changes in urban mobility. To achieve these objectives, relational databases and the geographic information system for transport environment are used (GIS-T), together with data from household and intercept interviews, to identify mobility patterns in the middle-sized city of Mérida, Spain. These application programs can detect changes in the mobility patterns and can locate problem areas. The results obtained show a high degree of adjustment between the predictions and the actual observations of the trips. In addition, the disaggregation levels in each midpoint section of the network combined with population data adjustment using population pyramids avoid bias in the travel samples.

View PDFchevron_right
Spatiotemporal Patterns of Urban Human Mobility
Samiul Hasan

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.

View PDFchevron_right
Monitoring travel patterns in German city regions with the help of mobile phone network data
Jigeeshu Joshi

International Journal of Digital Earth

This paper discusses the possibility to use mobile phone network data to monitor spatial policies in land use and transport planning. Monitoring requires robust time series and reproducible concepts linking spatial policies to monitoring outcomes, a requirement differing from current literature where mobile phone data analysis is exemplified in selected areas with privileged data access. Concepts need to serve the evaluation of policy objectives, for example in regional or local area plans. In this study, we, therefore, extend the application of mobile phone network data to monitoring applications comparing urban settlement types and their characteristic mobility patterns. To accomplish this, we link mobile phone records with urban classifications and transport network data, using both visual and computational approaches to mine the data. The article presents comparisons of travel patterns for selected monocentric and polycentric city regions in Germany, testing hypotheses of transit-oriented regional development, as well as testing for congestion risks in the transport network. The results help us to gain a more detailed understanding of spatial and temporal patterns in mobility for different urban types and assess future potentials for monitoring spatial policies with mobile phone network data.

View PDFchevron_right
GLOBAL URBAN CITYSCAPE -UNSUPERVISED CLUSTERING EXPLORATION OF HUMAN ACTIVITY AND MOBILITY INFRASTRUCTURE
Tania Papasotiriou

PROJECTIONS - CAADRIA, 2021

It is widely accepted that cities cultivate innovation and are the engines of productivity. The identification of strengths and weaknesses will enchant social mobility providing equal opportunities for all. The study at hand investigates the relationship between social mobility and transportation planning in 1,860 central urban areas across the globe. Datamining processes combining open-sourced, automated, and crowdsourced information from four major pillars of social mobility (demographics, human activity, transport infrastructure, and environmental quality) are used to describe each location. Next, unsupervised clustering algorithms are used to analyse the extracted information, in order to identify similar characteristics and patterns among urban areas. The process, which comprises an objective framework for the analysis of urban environments, resulted in four major types of central areas, that represent similar patterns of human activity and transport infrastructure.

View PDFchevron_right
Revealing travel patterns and city structure with taxi trip data
Yu Liu

Delineating travel patterns and city structure has long been a core research topic in transport geography. Different from the physical structure, the city structure beneath the complex travel-flow system shows the inherent connection patterns within the city. On the basis of taxi-trip data from Shanghai, we built spatially embedded networks to model intra-city spatial interactions and to introduce network science methods into the analysis. The community detection method is applied to reveal sub-regional structures, and several network measures are used to examine the properties of sub-regions. Considering the differences between long-and short-distance trips, we reveal a two-level hierarchical polycentric city structure in Shanghai. Further explorations of sub-network structures demonstrate that urban subregions have broader internal spatial interactions, while suburban centers are more influential on local traffic. By incorporating the land use of centers from a travel-pattern perspective, we investigate subregion formation and the interaction patterns of center-local places. This study provides insights into using emerging data sources to reveal travel patterns and city structures, which could potentially aid in developing and applying urban transportation policies. The sub-regional structures revealed in this study are more easily interpreted for transportation-related issues than for other structures, such as administrative divisions.

View PDFchevron_right
Mining urban data (part A)
Vana Kalogeraki

Information Systems, 2015

View PDFchevron_right
Big data analytics for extracting mobility patterns in a large urban center
Joel Teixeira

2019

Big cities show a wide public transportation network, allowing people to travel within cities. However, with the overpopulation of urban areas, the demand for new mobility strategies has increasing. Every day, citizens need to commute fast, easily and comfortable, which is not always easy due to the complexity of the transport network. The main objective of this paper, is to explore the ability of Big Data technologies to cope with data collected from public transportation, by inferring automatically and continuously, complex mobility patterns about human mobility, in the form of insightful indicators (such as connections, transshipments or pendular movements), creating a new perspective in public transports data analytics. The presented work, focus on the Lisbon metropolitan area, aiming to analyze the demand and supply side of transportation network of Lisbon, considering ticketing data transactions provided by the different transportation

View PDFchevron_right
Analyzing large-scale human mobility data: a survey of machine learning methods and applications
Boaz Lerner, Eran Toch

Human mobility patterns reflect many aspects of life, from the global spread of infectious diseases to urban planning and daily commute patterns. In recent years, the prevalence of positioning methods and technologies, such as the global positioning system, cellular radio tower geo-positioning, and WiFi positioning systems, has driven efforts to collect human mobility data and to mine patterns of interest within these data in order to promote the development of location-based services and applications. The efforts to mine significant patterns within large-scale, high-dimensional mobility data have solicited use of advanced analysis techniques, usually based on machine learning methods, and therefore, in this paper, we survey and assess different approaches and models that analyze and learn human mobility patterns using mainly machine learning methods. We categorize these approaches and models in a taxonomy based on their positioning characteristics, the scale of analysis, the properties of the modeling approach, and the class of applications they can serve. We find that these applications can be categorized into three classes: user modeling, place modeling, and trajectory modeling, each class with its characteristics. Finally, we analyze the short-term trends and future challenges of human mobility analysis.

View PDFchevron_right
Identifying Atypical Travel Patterns for Improved Medium-Term Mobility Prediction
Ansgar Gerlicher

IEEE Transactions on Intelligent Transportation Systems

View PDFchevron_right

References (39)

  1. W Alonso. 1976. A Theory of Movements: Introduction. Working Paper 266 (1976).
  2. Gennady Andrienko et al. 2020. (So) Big Data and the transformation of the city. International Journal of Data Science and Analytics (2020).
  3. Valerio Arnaboldi, Marco Conti, Andrea Passarella, and Robin IM Dunbar. 2017. Online social networks and information diffusion: The role of ego networks. Online Social Networks and Media 1 (2017), 44-55.
  4. Albert-László Barabási and Márton Pósfai. 2016. Network science. Cambridge University Press, Cambridge.
  5. Michael Batty. [n.d.]. Spatial Entropy. Geographical Analysis 6, 1 ([n. d.]), 1-31. https://doi.org/10.1111/j.1538-4632.1974.tb01014.x
  6. Vincent D Blondel, Jean-Loup Guillaume, Renaud Lambiotte, and Etienne Lefebvre. 2008. Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment 2008, 10 (2008), P10008.
  7. Harold Carter. 1995. The Study of Urban Geography. E. Arnold publications.
  8. Tianqi Chen et al. 2016. XGBoost: A Scalable Tree Boosting System.
  9. CITEAIR consortium. 2007. Air Quality in Europe web site. http://www. airqualitynow.eu/ [Online; accessed 21-December-2020].
  10. Michele Coscia, Giulio Rossetti, et al. 2012. Demon: a local-first discovery method for overlapping communities. In ACM SIGKDD. 615-623.
  11. H.G De Sherbinin, A.; Bittar. London, UK, 2003. The Role of Sustainability Indicators as a Tool for Assessing Territorial. Environmental Competitiveness; International Forum for Rural Development (London, UK, 2003).
  12. George E. P. Box et al. 2015. Time Series Analysis: Forecasting and Control. John Wiley and Sons.
  13. Liu F. et al. 2020. Citywide Traffic Analysis Based on the Combination of Visual and Analytic Approaches. J geovis spat anal 4, 15 (2020).
  14. W. A. Fuller. 1976. Introduction to Statistical Time Series. John Wiley and Sons.
  15. Fosca Giannotti, Mirco Nanni, Dino Pedreschi, et al. 2011. Unveiling the complexity of human mobility by querying and mining massive trajectory data. The VLDB Journal 20, 5 (Oct. 2011), 695-719.
  16. Fosca Giannotti, Mirco Nanni, Fabio Pinelli, and Dino Pedreschi. 2007. Tra- jectory pattern mining. In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining. 330-339.
  17. D. Gillis, I. Semanjski, and D. Lauwers. 2015. How to Monitor Sustainable Mo- bility in Cities? Literature Review in the Frame of Creating a Set of Sustainable Mobility Indicators. Sustainability 8 (2015), 29.
  18. Marta C. Gonzalez, Cesar A. Hidalgo, et al. 2008. Understanding individual human mobility patterns. Nature 453, 7196 (June 2008), 779-782.
  19. Riccardo Guidotti and Mirco Nanni. 2020. Crash Prediction and Risk As- sessment with Individual Mobility Networks. In 2020 21st IEEE International Conference on Mobile Data Management (MDM). IEEE, 89-98.
  20. Riccardo Guidotti, Mirco Nanni, Salvatore Rinzivillo, Dino Pedreschi, and Fosca Giannotti. 2017. Never drive alone: Boosting carpooling with network analysis. Information Systems 64 (2017), 237-257.
  21. Riccardo Guidotti, Roberto Trasarti, Mirco Nanni, Fosca Giannotti, and Dino Pedreschi. 2017. There's a path for everyone: A data-driven personal model reproducing mobility agendas. In 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 303-312.
  22. M. K. Jat, P. K. Garg, and D. Khare. 2008. Modelling of urban growth using spatial analysis techniques: a case study of Ajmer city (India). International Journal of Remote Sensing 29, 2 (2008), 543-567. https://doi.org/10.1080/ 01431160701280983 arXiv:https://doi.org/10.1080/01431160701280983
  23. Gabriel Lang, Eric Marcon, and Florence Puech. 2016. Distance-based Measures of Spatial Concentration: Introducing a Relative Density Function. (Sept. 2016).
  24. Z. Liu, Z. Li, K. Wu, and M. Li. 2018. Urban Traffic Prediction from Mobility Data Using Deep Learning. IEEE Network 32, 4 (2018), 40-46. https://doi.org/ 10.1109/MNET.2018.1700411
  25. A Paolo Masucci, Joan Serras, Anders Johansson, and Michael Batty. 2013. Gravity versus radiation models: On the importance of scale and heterogeneity in commuting flows. Physical Review E 88, 2 (2013), 022812.
  26. P. A. P. Moran. 1950. Notes on Continuous Stochastic Phenomena. Biometrika 37, 1/2 (1950), 17-23.
  27. Paul Newson and John Krumm. 2009. Hidden markov map matching through noise and sparseness. In Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, 336-343.
  28. Luca Pappalardo, Salvatore Rinzivillo, Zehui Qu, Dino Pedreschi, and Fosca Giannotti. 2013. Understanding the patterns of car travel. The European Physical Journal Special Topics 215, 1 (01 Jan 2013), 61-73.
  29. Karl Pearson. 1895. Notes on regression and inheritance in the case of two parents. Proceedings of the Royal Society of London 58 (1895), 240-242.
  30. S. Porta, P. Crucitti, and V. Latora. 2006. Centrality measures in spatial net- works of urban streets. Physical Review E 73, 3, part 2 (24 3 2006), 036125-1.
  31. Salvatore Rinzivillo, Lorenzo Gabrielli, Mirco Nanni, Luca Pappalardo, Dino Pedreschi, and Fosca Giannotti. 2014. The purpose of motion: Learning activi- ties from individual mobility networks. In 2014 International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 312-318.
  32. Antônio Nélson Rodrigues da Silva et al. 2015. A comparative evaluation of mobility conditions in selected cities of the five Brazilian regions. Transport Policy 37 (2015), 147 -156.
  33. P.A. Rogerson. 2010. Statistical Methods for Geography: A Student's Guide. SAGE Publications. https://books.google.ch/books?id=Zz69Ab8i0QsC
  34. Meead Saberi, Hani S. Mahmassani, Dirk Brockmann, and Amir Hosseini. 2017. A complex network perspective for characterizing urban travel demand patterns: graph theoretical analysis of large-scale origin-destination demand networks. Transportation 44, 6 (November 2017), 1383-1402.
  35. Claude Elwood Shannon. 1948. A Mathematical Theory of Communication. The Bell System Technical Journal 27, 3 (7 1948), 379-423.
  36. Sulochana Shekhar. 2004. Urban sprawl assessment Entropy approach. GIS Development 2004, Vol 8 issue 5, Page ., 6 Pages (05 2004), 43 -48.
  37. Filippo Simini, Marta C. Gonzalez, Amos Maritan, et al. 2012. A universal model for mobility and migration patterns. Nature 484, 7392 (2012), 96-100.
  38. Pavlos Tafidis et al. 2017. Sustainable urban mobility indicators: policy versus practice in the case of Greek cities. Transportation Research Procedia 24 (2017), 304 -312. 3rd Conference on Sustainable Urban Mobility.
  39. Roberto Trasarti, Riccardo Guidotti, Anna Monreale, and Fosca Giannotti. 2017. Myway: Location prediction via mobility profiling. Information Systems 64 (2017), 350-367.

Related papers

Reconstructing commuters network using machine learning and urban indicators
Jose F Rodrigues-Jr

Scientific Reports, 2019

Human mobility has a significant impact on several layers of society, from infrastructural planning and economics to the spread of diseases and crime. Representing the system as a complex network, in which nodes are assigned to regions (e.g., a city) and links indicate the flow of people between two of them, physics-inspired models have been proposed to quantify the number of people migrating from one city to the other. Despite the advances made by these models, our ability to predict the number of commuters and reconstruct mobility networks remains limited. Here, we propose an alternative approach using machine learning and 22 urban indicators to predict the flow of people and reconstruct the intercity commuters network. Our results reveal that predictions based on machine learning algorithms and urban indicators can reconstruct the commuters network with 90.4% of accuracy and describe 77.6% of the variance observed in the flow of people between cities. We also identify essential features to recover the network structure and the urban indicators mostly related to commuting patterns. As previously reported, distance plays a significant role in commuting, but other indicators, such as Gross Domestic Product (GDP) and unemployment rate, are also driven-forces for people to commute. We believe that our results shed new lights on the modeling of migration and reinforce the role of urban indicators on commuting patterns. Also, because link-prediction and network reconstruction are still open challenges in network science, our results have implications in other areas, like economics, social sciences, and biology, where node attributes can give us information about the existence of links connecting entities in the network.

View PDFchevron_right
Enhancing Data Offloading in Urban Networks via Machine Learning-based Mobility Prediction
Antonio Montieri

In the context of smart cities, mobile data offloading (i.e. redirecting network traffic from cellular infrastructure to alternatives such as Wi-Fi) is essential to ensure high-quality connectivity under growing urban demand. A key enabler of this strategy is the accurate prediction of Offloading Regions (ORs), i.e. geographic zones where users are likely to transfer data to alternative networks. This paper introduces a Machine Learning-based framework for predicting the next OR visited during user mobility. Using urban mobility traces from the city of Beijing, we develop novel spatio-temporal features and adopt a progressive evaluation strategy aimed at identifying the bestperforming model under closed-world assumptions and assessing its robustness under open-world conditions. The approach enhances robustness to both dynamic mobility patterns and unseen ORs at inference time, leveraging an Open Set Recognition (OSR) mechanism. Our optimized framework achieves up to +42% accuracy gain in the closed-world scenario and +16% relative gain in AUC when evaluated in an open-world setting. These results show that the integration of feature engineering and OSR significantly enhances both predictive performance and generalization capabilities, paving the way for intelligent and adaptive data offloading in next-generation urban networks.

View PDFchevron_right
The City Browser: Utilizing Massive Call Data to Infer City Mobility Dynamics
Abdullah Almaatouq

This paper presents the City Browser, a tool developed to analyze the complexities underlying human mobility at the city scale. The tool uses data generated from mobile phones as a proxy to provide several insights with regards to the commuting patterns of the population within the bounds of a city. The three major components of the browser are the data warehouse, modules and algorithm, and the visualization interface. The modules and algorithm component utilizes Call Detail Records (CDRs) stored within the data warehouse to infer mobility patterns that are then communicated through the visualization interface. The modules and algorithm component consists of four modules: the spatial-temporal decomposition module, the home/work capturing module, the community detection module, and the flow estimation module. The visualiza-tion interface manages the output of each module to provide a comprehensive view of a city's mobility dynamics over varying time scales. A case study is presented on the city of Riyadh in Saudi Arabia, where the browser was developed to better understand city mobility patterns.

View PDFchevron_right
Discovering Spatial Patterns in Origin-Destination Mobility Data
Diansheng Guo

Transactions in GIS, 2012

Mobility and spatial interaction data have become increasingly available due to the wide adoption of location-aware technologies. Examples of mobility data include human daily activities, vehicle trajectories, and animal movements, among others. In this article we focus on a special type of mobility data, i.e. origin-destination pairs, and present a new approach to the discovery and understanding of spatio-temporal patterns in the movements. Specifically, to extract information from complex connections among a large number of point locations, the approach involves two steps: (1) spatial clustering of massive GPS points to recognize potentially meaningful places; and (2) extraction and mapping of the flow measures of clusters to understand the spatial distribution and temporal trends of movements. We present a case study with a large dataset of taxi trajectories in Shenzhen, China to demonstrate and evaluate the methodology. The contribution of the research is twofold. First, it presents a new methodology for detecting location patterns and spatial structures embedded in

View PDFchevron_right
Trip predictions in middle-sized cities with GIS environment for transport: maximun disaggregation with minimun cost
Enrique Eugenio Ruiz Labrador, Francisco Javier Jaraíz-Cabanillas

. Travel prediction methodology in medium-sized cities with GIS-T Abstract This paper describes the design of a traffic assignment model that predicts flows for each segment of an urban network with a higher resolution than a traditional four stage model, retaining the origins and destinations of travel. The research objectives are to determine the traffic intensity in specific areas of the network, and then to identify the origins and destinations of travel to predict changes in urban mobility. To achieve these objectives, relational databases and the geographic information system for transport environment are used (GIS-T), together with data from household and intercept interviews, to identify mobility patterns in the middle-sized city of Mérida, Spain. These application programs can detect changes in the mobility patterns and can locate problem areas. The results obtained show a high degree of adjustment between the predictions and the actual observations of the trips. In addition, the disaggregation levels in each midpoint section of the network combined with population data adjustment using population pyramids avoid bias in the travel samples.

View PDFchevron_right

Related topics

  • Computer Science
    • 580 California St., Suite 400
    San Francisco, CA, 94104
    © 2025 Academia. All rights reserved