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Analytics on public transport delays with spatial big data
KTH, School of Technology and Health (STH), Health Systems Engineering, Health Care Logistics.
KTH, School of Technology and Health (STH), Health Systems Engineering, Health Care Logistics.ORCID iD: 0000-0001-5118-4856
KTH, School of Technology and Health (STH), Health Systems Engineering, Health Care Logistics.ORCID iD: 0000-0003-1126-3781
2016 (English)In: Proceedings of the 5th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2016, ACM Digital Library, 2016, p. 28-33Conference paper, Published paper (Refereed)
Abstract [en]

The increasing pervasiveness of location-aware technologies is leading to the rise of large, spatio-temporal datasets and to the opportunity of discovering usable knowledge about the behaviors of people and objects. Applied extensively in transportation, spatial big data and its analytics can deliver useful insights on a number of different issues such as congestion, delays, public transport reliability and so on. Predominantly studied for its use in operational management, spatial big data can be used to provide insight in strategic applications as well, from planning and design to evaluation and management. Such large scale, streaming spatial big data can be used in the improvement of public transport, for example the design of public transport networks and reliability. In this paper, we analyze GTFS data from the cities of Stockholm and Rome to gain insight on the sources and factors influencing public transport delays in the cities. The analysis is performed on a combination of GTFS data with data from other sources. The paper points to key issues in the analysis of real time data, driven by the contextual setting in the two cities. © 2016, Association for Computing Machinery, Inc. All rights reserved.

Place, publisher, year, edition, pages
ACM Digital Library, 2016. p. 28-33
Keywords [en]
Big data, Decision making, Public transport, Behavioral research, Gain insight, Location-aware technology, Operational management, Planning and design, Public transport networks, Real-time data, Spatio temporal
National Category
Medical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-202280DOI: 10.1145/3006386.3006387Scopus ID: 2-s2.0-85005781400ISBN: 9781450345811 (print)OAI: oai:DiVA.org:kth-202280DiVA, id: diva2:1075758
Conference
5th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2016, 31 October 2016
Note

Conference Paper. QC 20170221

Available from: 2017-02-21 Created: 2017-02-21 Last updated: 2017-02-21Bibliographically approved

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Shreenath, Vinutha MagalMeijer, Sebastiaan

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