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

Zhang, Yang; Aslam, Nilufer Sari; Lai, Juntao; Cheng, Tao

doi:10.1016/j.compenvurbsys.2020.101517

Cited by 25 publications

(8 citation statements)

References 34 publications

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“…Faced with such diverse datasets, many data-driven methods have also been proposed by researchers to mine passenger mobility patterns. For instance, multi-objective Convolutional Neural Network (CNN) was designed to infer the social demographic attributes and mobility features of passengers based on media data and land-use data [28]. Support vector machines (SVM) were introduced to divide passenger travel data into several types, and the passenger purpose was analyzed according to the characteristics of each type using sociodemographic data [8].…”

Section: Literature Reviewmentioning

confidence: 99%

Data-Driven Approach for Passenger Mobility Pattern Recognition Using Spatiotemporal Embedding

et al. 2021

Journal of Advanced Transportation

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Urban mobility pattern recognition has great potential in revealing human travel mechanism, discovering passenger travel purpose, and predicting and managing traffic demand. This paper aims to propose a data-driven method to identify metro passenger mobility patterns based on Automatic Fare Collection (AFC) data and geo-based data. First, Point of Information (POI) data within 500 meters of the metro stations are captured to characterize the spatial attributes of the stations. Especially, a fusion method of multisource geo-based data is proposed to convert raw POI data into weighted POI data considering service capabilities. Second, an unsupervised learning framework based on stacked auto-encoder (SAE) is designed to embed the spatiotemporal information of trips into low-dimensional dense trip vectors. In detail, the embedded spatiotemporal information includes spatial features (POI categories around the origin station and that around the destination station) and temporal features (start time, day of the week, and travel time). Third, a density-based clustering algorithm is introduced to identify passenger mobility patterns based on the embedded dense trip vectors. Finally, a case of Beijing metro network is used to verify the feasibility of the above methodology. The results show that the proposed method performs well in recognizing mobility patterns and outperforms the existing methods.

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Section: Literature Reviewmentioning

confidence: 99%

Data-Driven Approach for Passenger Mobility Pattern Recognition Using Spatiotemporal Embedding

et al. 2021

Journal of Advanced Transportation

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“…The mobility mining discipline has evolved from the development of travel surveys aimed to capture human-movement distributions towards the massive adoption of information and communication technologies (ICTs), fostering the collection of mobility data in a much larger and detailed scale. Hence, GPS spatio-temporal trajectories [28], mobilephone location data [12] and smart-card transactions [39] are widely used as data sources to extract different types of human-mobility patterns. However, these sources often have limited access for the scientific community due to commercial and privacy issues [36].…”

Section: Introductionmentioning

confidence: 99%

An analysis of twitter as a relevant human mobility proxy

et al. 2022

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During the last years, the analysis of spatio-temporal data extracted from Online Social Networks (OSNs) has become a prominent course of action within the human-mobility mining discipline. Due to the noisy and sparse nature of these data, an important effort has been done on validating these platforms as suitable mobility proxies. However, such a validation has been usually based on the computation of certain features from the raw spatiotemporal trajectories extracted from OSN documents. Hence, there is a scarcity of validation studies that evaluate whether geo-tagged OSN data are able to measure the evolution of the mobility in a region at multiple spatial scales. For that reason, this work proposes a comprehensive comparison of a nation-scale Twitter (TWT) dataset and an official mobility survey from the Spanish National Institute of Statistics. The target time period covers a three-month interval during which Spain was heavily affected by the COVID-19 pandemic. Both feeds have been compared in this context by considering different mobility-related features and spatial scales. The results show that TWT could capture only a limited number features of the latent mobility behaviour of Spain during the study period.

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“…In this article, we refer to portable sensing in the broadest sense, including any information that can be gauged about the status of an event or a stimulus through an electronic device. This includes smart cards that embed radio-frequency identification (RFID) technology (see, Zhang, Sari Aslam, Lai, & Cheng, 2020 in this special issue), as well as people who report their affective status or their immediate environment through their smartphones using repeat frequent surveys (also known as ecological momentary assessment) (Birenboim & Shoval, 2016;Helbich, 2018;Kou, Tao, Kwan, & Chai, 2020).…”

Section: Introductionmentioning

confidence: 99%

Advances in portable sensing for urban environments: Understanding cities from a mobility perspective

Birenboim

Helbich

Kwan

2021

Computers, Environment and Urban Systems

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Portable sensing, in which lightweight mobile sensors are used to measure stimuli, events, and human behavior, is a new and disruptive data collection paradigm. It has several methodological advantages compared to traditional methods and is suitable for investigating the dynamism of increasingly mobile and urban societies. In this article, we discuss the motivations behind the use of portable sensing and reflect upon the advances, limitations, and future of the field. Although portable sensing is still in its infancy, we foresee that its utilization will grow in the coming years. For portable sensing to become a prevalent and legitimate methodological approach, it is essential to have conceptually strong study designs that are grounded in suitable ethical procedures and comply with data protection regulations.

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

Cited by 25 publications

References 34 publications

Data-Driven Approach for Passenger Mobility Pattern Recognition Using Spatiotemporal Embedding

Data-Driven Approach for Passenger Mobility Pattern Recognition Using Spatiotemporal Embedding

An analysis of twitter as a relevant human mobility proxy

Advances in portable sensing for urban environments: Understanding cities from a mobility perspective

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