A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Inferring the Economic Attributes of Urban Rail Transit Passengers Based on Individual Mobility Using Multisource Data
Socioeconomic attributes are essential characteristics of people, and many studies on economic attribute inference focus on data that contain user profile information. For data without user profiles, like smart card data, there is no validated method for inferring individual economic attributes. This study aims to bridge this gap by formulating a mobility to attribute framework to infer passengers’ economic attributes based on the relationship between individual mobility and personal attributes. This framework integrates shop consumer prices, house prices, and smart card data using three steps: individual mobility extraction, location feature identification, and economic attribute inference. Each passenger’s individual mobility is extracted by smart card data. Economic features of stations are described using house price and shop consumer price data. Then, each passenger’s comprehensive consumption indicator set is formulated by integrating these data. Finally, individual economic levels are classified. From the case study of Beijing, commuting distance and trip frequency using the metro have a negative correlation with passengers’ income and the results confirm that metro passengers are mainly in the low- and middle-income groups. This study improves on passenger information extracted from data without user profile information and provides a method to integrate multisource big data mining for more information.
Inferring the Economic Attributes of Urban Rail Transit Passengers Based on Individual Mobility Using Multisource Data
Socioeconomic attributes are essential characteristics of people, and many studies on economic attribute inference focus on data that contain user profile information. For data without user profiles, like smart card data, there is no validated method for inferring individual economic attributes. This study aims to bridge this gap by formulating a mobility to attribute framework to infer passengers’ economic attributes based on the relationship between individual mobility and personal attributes. This framework integrates shop consumer prices, house prices, and smart card data using three steps: individual mobility extraction, location feature identification, and economic attribute inference. Each passenger’s individual mobility is extracted by smart card data. Economic features of stations are described using house price and shop consumer price data. Then, each passenger’s comprehensive consumption indicator set is formulated by integrating these data. Finally, individual economic levels are classified. From the case study of Beijing, commuting distance and trip frequency using the metro have a negative correlation with passengers’ income and the results confirm that metro passengers are mainly in the low- and middle-income groups. This study improves on passenger information extracted from data without user profile information and provides a method to integrate multisource big data mining for more information.
Inferring the Economic Attributes of Urban Rail Transit Passengers Based on Individual Mobility Using Multisource Data
Yadi Zhu (author) / Feng Chen (author) / Ming Li (author) / Zijia Wang (author)
2018
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
Study on Route Choice Behavior of Urban Rail Transit Passengers
| British Library Conference Proceedings | 2014
Design of Guidance Information for Arriving Passengers in Urban Rail Transit Carriage
| British Library Conference Proceedings | 2017
Predictability of short-term passengers’ origin and destination demands in urban rail transit
| Online Contents | 2022
Study of Passengers' Route Choice of Urban Rail Transit Network under the Emergency
| British Library Conference Proceedings | 2014