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  • 1.
    Raghothama, Jayanth
    et al.
    KTH, School of Technology and Health (STH), Health Systems Engineering, Health Care Logistics.
    Shreenath, Vinutha Magal
    KTH, School of Technology and Health (STH), Health Systems Engineering, Health Care Logistics.
    Meijer, Sebastiaan
    KTH, School of Technology and Health (STH), Health Systems Engineering, Health Care Logistics.
    Analytics on public transport delays with spatial big data2016In: Proceedings of the 5th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, BigSpatial 2016, ACM Digital Library, 2016, p. 28-33Conference 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.

  • 2.
    Shreenath, Vinutha Magal
    et al.
    KTH, School of Technology and Health (STH), Health Systems Engineering, Health Care Logistics.
    Kornevs, Maksims
    KTH, School of Technology and Health (STH), Health Systems Engineering, Health Care Logistics.
    Raghothama, Jayanth
    Meijer, Sebastiaan
    KTH, School of Technology and Health (STH), Health Systems Engineering, Health Care Logistics.
    A Feasibility Study for Gamification in Transport Maintenance: Requirements to implement gamification in heterogeneous organizations2015In: Games and Virtual Worlds for Serious Applications (VS-Games), 2015 7th International Conference on, IEEE conference proceedings, 2015, p. 1-7Conference paper (Refereed)
    Abstract [en]

    Gamification has been successfully applied in many domains, but mostly for simple, isolated and operational tasks. The hope for gamification as a method to radically change and improve behavior, to provide incentives for sustained engagement has proven to be more difficult to get right. Applying gamification in large networked organizations with heterogeneous tasks remains a challenge. Applying gamification in such enterprise environments posits different requirements, and a match between these requirements and the institution needs to be investigated before venturing into the design and implementation of gamification. The current paper contributes a study where the authors investigate the feasibility of implementing gamification in Trafikverket, the Swedish transport administration. Through an investigation of the institutional arrangements around data collection, procurement processes and links to institutional structures, the study finds areas within Trafikverket where gamification could be successfully applied, and suggests gaps and methods to apply gamification in other areas.

  • 3.
    Shreenath, Vinutha Magal
    et al.
    KTH, School of Technology and Health (STH).
    Meijer, Sebastiaan
    KTH, School of Technology and Health (STH), Health Systems Engineering.
    Spatial Big Data for designing large scale infrastructure A case-study of Electrical Road Systems2016In: 2016 3RD IEEE/ACM INTERNATIONAL CONFERENCE ON BIG DATA COMPUTING, APPLICATIONS AND TECHNOLOGIES (BDCAT), IEEE , 2016, p. 143-148Conference paper (Refereed)
    Abstract [en]

    Decision making and planning of large scale infrastructures within cities is often a long process encompassing years, between multiple institutions represented by experts that require negotiations and consensus of demands and goals. The role big data plays in such design could be crucial, by providing access to otherwise elusive information on movements of people and goods in a city which can then transparently inform the design process, especially about possible demands and related complexities on the infrastructure being planned. To harness this data, it is necessary to formulate the problem technically such that data can inform experts, by articulating their expertise through the data. In this paper we present an application to analyze millions of instances of spatial data to identify potential locations for electrical road installation(s) in a city, to aid urban planners and other relevant stakeholders in planning and designing an Electrical Road System for a city. The dataset being used is gathered from a major vehicle manufacturer in Sweden, containing millions of instances of GPS data emitted by thousands of vehicles. A plan for electrified transport system is formulated by retrieving locations suitable for both static and dynamic charging installations. We investigate the technical formulation of methods and metrics for such a complex design problem, based on criteria set by experts, thus contributing to the science of big data for design of infrastructure and to methodology of data science in an institutional context.

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