Mul"-source Heterogeneous Data

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Mul"-source Heterogeneous Data

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Urban big data for analyzing dras"cally different metro riderships in. Zaoying and Jiangtai sta"ons, which highlights the value of: â mul(-source and ...

A Tale of Two Sta,ons: Analysing Metro Ridership with Big Data Liang Ma, Qingning Chen, Ke Han ([email protected]), Yong Gao, Dong Li Urban big data for analyzing dras1cally different metro riderships in Zaoying and Jiangtai sta1ons, which highlights the value of: q  mul,-source and heterogeneous data in analyzing complex urban problems; q  fine-granular data concerning individual ac,vi,es and behaviors of commuters

Mul1-source Heterogeneous Data Transit Smart Card Data p  metro (6 million records) p  bus (4.3 million records)

POI Data p  422,000 records p  15 categories

Network Data p  geographic informa,on p  transport infrastructure

Taxi GPS Data p  1.5 million records p  60,000+ taxis

Metro: Line 14 Open since 2014

Second-hand Property Price p  1,2316 communi,es

Bike Sharing Data p  1.3 m bike trips

Cell Phone Signaling Data p  17m subscribers p  70G per day

Metro Ridership Jiangtai

Land-Use: Work Area Residential Area Commercial Area 1400

Popula,on & Demographic Data

Zaoying

# of POIs

1200 1000 800 600

Population:

400

Residence

Work

Total

Zaoying

200

Jiangtai

0

0

Z J

10000

20000

30000

40000

50000

60000

70000

Since introduction of bike sharing in Sep 2016, Zaoying’s ridership has increased by 80%

Dras1cally different metro sta1on riderships ü  contribu1ng factors ü  what can we learn

Demographic Informa1on Jiangtai Zaoying

Connec1vity with Bus System Bike Sharing Spa1al Commute PaHern

Proportion of senior citizen

•  Multi-dimensional analysis for transport planning

Maizidian (Zaoying)

•  Multi-source and heterogeneous data to interpret urban dynamics

Jiuxianqiao

Jiangtai

Population size

•  Provide insights on urban mobility and local accessibility