Inferring Social-Demographics of Travellers based on Smart Card Data

Zhang, Yang; Cheng, Tao

doi:10.4995/carma2018.2018.8310

Cited by 9 publications

(6 citation statements)

References 11 publications

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“…Scheiner and Holz-Rau [22] showed that employment status strongly affects travel distances as well as travel mode. Similar work can be also seen in [23] and [24]. The shortcomings of these works are twofold.…”

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confidence: 65%

A Deep Learning Approach to Infer Employment Status of Passengers by Using Smart Card Data

Zhang

Cheng

2020

IEEE Trans. Intell. Transport. Syst.

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Understanding the employment status of passengers in public transit systems is significant for transport operators in many real applications such as forecasting travel demand and providing personalized transportation service. This paper develops a deep learning approach to infer a passenger's employment status by using smart card data (SCD) with a household survey. This paper first extracts an individual passenger's weekly travel patterns in different travel modes from the raw SCD as a three-dimensional image. A deep learning architecture, called a thresholding multi-channel convolutional neural network, was developed to predict an individual's employment status. The approach proposed here solves two critical problems of using the SCD for employment status studies. First, it automatically incorporates learning temporal features in different travel modes without the need for handcrafted travel feature design. Second, it considers the class-imbalance problem by leveraging the ensemble of oversampling and thresholding techniques. By applying our approach to a real dataset collected from the metropolitan area of London, U.K., about 72% of passengers were correctly categorized into six types of employment statuses. The promising results show the tight correlation between temporal travel behavior, mode choice, and social-demographic roles. To the best of our knowledge, this is the first paper to infer employment status by using the SCD.Index Terms-Deep learning, employment status inference, travel mode choice, smart card data, temporal travel behavior.

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confidence: 65%

A Deep Learning Approach to Infer Employment Status of Passengers by Using Smart Card Data

Zhang

Cheng

2020

IEEE Trans. Intell. Transport. Syst.

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“…Although some possible relationship has been well-documented in many works, very limited attention has been paid to estimate demographics from SC data. Lately, Zhang and Cheng (2018) explored inferring demographics by leveraging a variety of spatial and temporal features extracted from the raw transaction records. Ding, Huang, Zhao and Fu (2019) developed a deep learning model to estimate socioeconomic status using temporal-sequential features and general statistical features generated from SC data.…”

Section: Demographic Inference Using Geo-tagged Datamentioning

confidence: 99%

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

Zhang

Aslam

Lai

et al. 2020

Computers, Environment and Urban Systems

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“…However, it lacks the social-demographic information of passengers to further explore 'who are the card carriers', 'why they behaved differently' and 'what factors affect their behaviours', which are crucial to better understand the users' travel demand and mobility patterns. Fortunately, leveraging household survey data, it might further explore the relationship between human travel patterns and their socialdemographic roles (Zhang et al 2018;Zhang et al 2019), which can help operators make better transportation planning and provide passengers with more personalised services.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Relationship Between Travel Pattern and Social-Demographics Using Smart Card Data and Household Survey

Zhang

Cheng

Aslam

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> Understanding social-demographics of passengers in public transit systems is significant for transportation operators and city planners in many real applications, such as forecasting travel demand and providing personalised transportation service. This paper develops an entire framework to analyse the relationship between passengers’ movement patterns and social-demographics by using smart card (SC) data with a household survey. The study first extracts various novel travel features of passengers from SC data, including spatial, temporal, travel mode and travel frequency features, to identify long-term travel patterns and their seasonality, for the in-depth understanding of ‘how’ people travel in cities. Leveraging household survey data, we then classify passengers into several groups based on their social-demographic characteristics, such as age, and working status, to identify the homogeneity of travellers for understanding ‘who’ travels using public transit. Finally, we explore the significant relationships between the travel patterns and demographic clusters. This research reveals explicit semantic explanations of ‘why’ passengers exhibit these travel patterns.</p>

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Inferring Social-Demographics of Travellers based on Smart Card Data

Abstract: Abstract

Cited by 9 publications

References 11 publications

A Deep Learning Approach to Infer Employment Status of Passengers by Using Smart Card Data

A Deep Learning Approach to Infer Employment Status of Passengers by Using Smart Card Data

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

Exploring the Relationship Between Travel Pattern and Social-Demographics Using Smart Card Data and Household Survey

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