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.
|Original language||English (US)|
|Journal||CEUR Workshop Proceedings|
|State||Published - Jan 1 2018|
|Event||2018 Poster Track of the Workshop on Big Social Data and Urban Computing, BiDU-PS 2018 - Rio de Janeiro, Brazil|
Duration: Aug 31 2018 → …
ASJC Scopus subject areas
- Computer Science(all)