TY - JOUR
T1 - Zooming in on NYC taxi data with portal
AU - Stoyanovich, Julia
AU - Gilbride, Matthew
AU - Mott, Vera Z.
N1 - Funding Information:
★ This work was supported in part by NSF Grant No. 1750179.
Publisher Copyright:
© 2018 CEUR-WS. All rights reserved.
PY - 2018
Y1 - 2018
N2 - In this paper we develop a methodology for analyzing transportation data at different levels of temporal and spatial granularity, and apply our methodology to the TLC Trip Record Dataset, made publicly available by the NYC Taxi & Limousine Commission. This data is naturally represented by a set of trajectories, annotated with time and with additional information such as passenger count and cost. We analyze TLC data to identify hotspots, which point to lack of convenient public transportation options, and popular routes, which motivate ride-sharing solutions or addition of a bus route. Our methodology is based on using an open-source system called Portal that supports an algebraic query language for analyzing evolving property graphs. Portal is implemented as an Apache Spark library and is inter-operable with other Spark libraries like SparkSQL, which we also use in our analysis.
AB - In this paper we develop a methodology for analyzing transportation data at different levels of temporal and spatial granularity, and apply our methodology to the TLC Trip Record Dataset, made publicly available by the NYC Taxi & Limousine Commission. This data is naturally represented by a set of trajectories, annotated with time and with additional information such as passenger count and cost. We analyze TLC data to identify hotspots, which point to lack of convenient public transportation options, and popular routes, which motivate ride-sharing solutions or addition of a bus route. Our methodology is based on using an open-source system called Portal that supports an algebraic query language for analyzing evolving property graphs. Portal is implemented as an Apache Spark library and is inter-operable with other Spark libraries like SparkSQL, which we also use in our analysis.
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M3 - Conference article
AN - SCOPUS:85057539830
SN - 1613-0073
VL - 2247
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
T2 - 2018 Poster Track of the Workshop on Big Social Data and Urban Computing, BiDU-PS 2018
Y2 - 31 August 2018
ER -