You are how you travel: A multi-task learning framework for Geodemographic inference using transit smart card data

Geodemographics, providing the information of population's characteristics in the regions on a geographical basis, is of immense importance in urban studies, public policy-making, social research and business, among others. Such data, however, are difficult to collect from the public, which is usually done via census, with a low update frequency. In urban areas, with the increasing prevalence of public transit equipped with automated fare payment systems, researchers can collect massive transit smart card (SC) data from a large population. The SC data record human daily activities at an individual level with high spatial and temporal resolutions. It can reveal frequent activity areas (e.g., residential areas) and travel behaviours of passengers that are intimately intertwined with personal interests and characteristics. This provides new opportunities for geodemographic study. This paper seeks to develop a framework to infer travellers' demographics (such as age, income level and car ownership, et al.) and their residential areas for geodemographic mapping using SC data with a household survey. We first use a decision tree diagram to detect passengers' residential areas. We then represent each individual's spatio-temporal activity pattern derived from multi-week SC data as a 2D image. Leveraging this representation, a multi-task convolutional neural network (CNN) is employed to predict multiple demographics of individuals from the images. Combing the demographics and locations of their residence, geodemographic information is further obtained. The methodology is applied to a large-scale SC dataset provided by Transport for London. Results provide new insights in understanding the relationship between human activity patterns and demographics. To the best of our knowledge, this is the first attempt to infer geodemographics by using the SC data

Identifying Urban Functional Areas and Their Dynamic Changes in Beijing: Using Multiyear Transit Smart Card Data

A growing number of megacities have been experiencing changes to their landscape due to rapid urbanisation trajectories and travel behaviour dynamics. Therefore, it is of great significance to investigate the distribution and evolution of a city’s urban functional areas over different periods of time. Although the smart card automated fare collection system (SCAFC) is already widely used, few studies have used smart card data to infer information about changes in urban functional areas, particularly in developing countries. Thus, this research aims to delineate the dynamic changes that have occurred in urban functional areas based on passengers’ travel patterns, using Beijing as a case study. We established a Bayesian framework and applied a Gaussian mixture model (GMM) derived from transit smart card data in order to gain insight into passengers’ travel patterns at station level and then identify the dynamic changes in their corresponding urban functional areas. Our results show that Beijing can be clustered into five different functional areas based on the analysis of corresponding transit station functions, namely: multimodal interchange hub and leisure area; residential area; employment area; mixed but mainly residential area; and a mixed residential and employment area. In addition, we found that urban functional areas have experienced slight changes between 2014 and 2017. The findings can be used to inform urban planning strategies designed to tackle urban spatial structure issues, as well as guiding future policy evaluation of urban landscape pattern use

Análisis urbano y comunidades inteligentes: “una aproximación al empleo de la tecnología en la movilidad cotidiana”

Concentration of population in urban centers is a global problem for which different strategies in order to organize different processes in cities and improve the quality of life are required. The creation of smart communities is shown as a sustainable solution since they deal with various key aspects, such as traffc management and mobility, through the use of information technologies (ITs). This work presents a review of recent studies using information technologies for urban analysis and mobility in cities. A descriptive analysis of automated methods for collecting and analyzing citizens’ mobility patterns is performed; it is centered in smart card use, geolocation and geotagging. It is concluded that a robust communication infrastructure, supported by an effcient computational platform allowing big data management and ubiquitous computing, is a crucial aspect for urban management in a smart community.La concentración de la población en los centros urbanos es una problemática mundial que requiere de estrategias que permitan organizar sus procesos y mejorar la calidad de vida. La creación de comunidades inteligentes se muestra como una solución sostenible, debido a que éstas trabajan aspectos claves para el desarrollo urbano, como la gestión de tráfco y la movilidad, apoyada en las tecnologías de la información (TICs). Este trabajo presenta una revisión del estado del arte en cuanto a la aplicación de las TICs al análisis urbano y movilidad ciudadana. Se analizan descriptivamente diversos métodos automáticos para la recolección y el análisis del patrón de movilidad de los ciudadanos, enfocándose en el uso de tarjetas inteligentes, geolocalización y geoetiquetado. Se encuentra que una infraestructura de comunicaciones robusta, apoyada en una plataforma computacional ágil con manejo de grandes datos y computación ubicua, es primordial para la gestión urbana en una comunidad inteligente

Análisis Urbano y Comunidades Inteligentes: Una Aproximación al Empleo de la Tecnología en la Movilidad Cotidiana

Concentration of population in urban centers is a global problem for which different strategies in order to organize different processes in cities and improve the quality of life are required. The creation of smart communities is shown as a sustainable solution since they deal with various key aspects, such as traffic management and mobility, through the use of information technologies (ITs). This work presents a review of recent studies using information technologies for urban analysis and mobility in cities. A descriptive analysis of automated methods for collecting and analyzing citizens’ mobility patterns is performed; it is centered in smart card use, geolocation and geotagging. It is concluded that a robust communication infrastructure, supported by an efficient computational platform allowing big data management and ubiquitous computing, is a crucial aspect for urban management in a smart communityLa concentración de la población en los centros urbanos es una problemática mundial que requiere de estrategias que permitan organizar sus procesos y mejorar la calidad de vida. La creación de comunidades inteligentes se muestra como una solución sostenible, debido a que éstas trabajan aspectos claves para el desarrollo urbano, como la gestión de tráfico y la movilidad, apoyada en las tecnologías de la información (TICs). Este trabajo presenta una revisión del estado del arte en cuanto a la aplicación de las TICs al análisis urbano y movilidad ciudadana. Se analizan descriptivamente diversos métodos automáticos para la recolección y el análisis del patrón de movilidad de los ciudadanos, enfocándose en el uso de tarjetas inteligentes, geolocalización y geoetiquetado. Se encuentra que una infraestructura de comunicaciones robusta, apoyada en una plataforma computacional ágil con manejo de grandes datos y computación ubicua, es primordial para la gestión urbana en una comunidad inteligente

Inferring Social-Demographics of Travellers based on Smart Card Data

[EN] With the wide application of the smart card technology in public transit system, traveller’s daily travel behaviours can be possibly obtained. This study devotes to investigating the pattern of individual mobility patterns and its relationship with social-demographics. We first extract travel features from the raw smart card data, including spatial, temporal and travel mode features, which capture the travel variability of travellers. Then, travel features are fed to various supervised machine learning models to predict individual’s demographic attributes, such as age group, gender, income level and car ownership. Finally, a case study based on London’s Oyster Card data is presented and results show it is a promisingZhang, Y.; Cheng, T. (2018). Inferring Social-Demographics of Travellers based on Smart Card Data. En 2nd International Conference on Advanced Reserach Methods and Analytics (CARMA 2018). Editorial Universitat Politècnica de València. 55-62. https://doi.org/10.4995/CARMA2018.2018.8310OCS556

Advances in crowd analysis for urban applications through urban event detection

The recent expansion of pervasive computing technology has contributed with novel means to pursue human activities in urban space. The urban dynamics unveiled by these means generate an enormous amount of data. These data are mainly endowed by portable and radio-frequency devices, transportation systems, video surveillance, satellites, unmanned aerial vehicles, and social networking services. This has opened a new avenue of opportunities, to understand and predict urban dynamics in detail, and plan various real-time services and applications in response to that. Over the last decade, certain aspects of the crowd, e.g., mobility, sentimental, size estimation and behavioral, have been analyzed in detail and the outcomes have been reported. This paper mainly conducted an extensive survey on various data sources used for different urban applications, the state-of-the-art on urban data generation techniques and associated processing methods in order to demonstrate their merits and capabilities. Then, available open-access crowd data sets for urban event detection are provided along with relevant application programming interfaces. In addition, an outlook on a support system for urban application is provided which fuses data from all the available pervasive technology sources and finally, some open challenges and promising research directions are outlined

Routine pattern discovery and anomaly detection in individual travel behavior

Discovering patterns and detecting anomalies in individual travel behavior is a crucial problem in both research and practice. In this paper, we address this problem by building a probabilistic framework to model individual spatiotemporal travel behavior data (e.g., trip records and trajectory data). We develop a two-dimensional latent Dirichlet allocation (LDA) model to characterize the generative mechanism of spatiotemporal trip records of each traveler. This model introduces two separate factor matrices for the spatial dimension and the temporal dimension, respectively, and use a two-dimensional core structure at the individual level to effectively model the joint interactions and complex dependencies. This model can efficiently summarize travel behavior patterns on both spatial and temporal dimensions from very sparse trip sequences in an unsupervised way. In this way, complex travel behavior can be modeled as a mixture of representative and interpretable spatiotemporal patterns. By applying the trained model on future/unseen spatiotemporal records of a traveler, we can detect her behavior anomalies by scoring those observations using perplexity. We demonstrate the effectiveness of the proposed modeling framework on a real-world license plate recognition (LPR) data set. The results confirm the advantage of statistical learning methods in modeling sparse individual travel behavior data. This type of pattern discovery and anomaly detection applications can provide useful insights for traffic monitoring, law enforcement, and individual travel behavior profiling