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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: 2019-09-18Bibliographically approved
In thesis
1. Creating Knowledge with Data Science for Design in Systems
Open this publication in new window or tab >>Creating Knowledge with Data Science for Design in Systems
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Designing in large-scale engineering systems is a difficult cognitive task undertaken by experts. Knowledge of experts continually changes as they are confronted with similar by different problems in designing in such systems. However, it is also important that designers are presented information that is representative of the system,and that they are cognizant of activities on a system scale so they can create diverse choices for designs in early phase of design process.Data Science has been proven to be effective at informing people for decisions at immediate horizons. But the use of data science to drive long terms designs where experts have to make the right series of decisions i.e. designs is yet unknown. The use of data science is to inform decision makers of activities at system scale.In this thesis, I have looked at how data science can be used to create knowledge in designers for designing in large scale systems. I have also investigated further questions regarding imitation of expertise using AI, and in generating similar knowledge by creating diverse options in design.The results point out that data science can indeed inform designers, change their designs and hence create knowledge. They also point out that design cognition in experts can be partly imitated in data science itself, through careful modeling of the ill-defined problem in design. This therefore points to a promising future direction where data can be used as an interface between human thinking and machine learning, by translation of conceptual forms such as differential diagnoses and cognitive artefacts using data.

Place, publisher, year, edition, pages
KTH Royal Institute of Technology, 2019. p. 33
Series
TRITA-CBH-FOU ; 46
Keywords
Data Science, Design Cognition, Transport, Healthcare, Artificial Intelligence, Imitation, Design Space Exploration
National Category
Computer Systems Health Sciences Transport Systems and Logistics
Research subject
Technology and Health
Identifiers
urn:nbn:se:kth:diva-259566 (URN)978-91-7873-299-9 (ISBN)
Public defence
2019-10-11, T2, Hälsovägen 11, 141 57, Huddinge, 09:30 (English)
Opponent
Supervisors
Note

QC 2019-09-20

Available from: 2019-09-20 Created: 2019-09-18 Last updated: 2019-10-18Bibliographically approved

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Shreenath, Vinutha MagalMeijer, Sebastiaan

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