Mobility Compass

Discover mobility and transportation research. Find experts, partners, networks.

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The Mobility Compass is an open tool for improving networking and interdisciplinary exchange within mobility and transport research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

To Graph

8.284 Topics available

To Map

890 Locations available

479.575 PEOPLE
479.575 People People

479.575 People

Show results for 479.575 people that are selected by your search filters.

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PeopleLocationsStatistics
Ziakopoulos, ApostolosAthens
  • 17
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  • 2023
Vigliani, AlessandroTurin
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Catani, JacopoRome
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Statheros, ThomasStevenage
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Utriainen, RoniTampere
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Guglieri, GiorgioTurin
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Martínez Sánchez, Joaquín
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Tobolar, Jakub
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Volodarets, M.
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Piwowar, Piotr
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Tennoy, AudOslo
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Matos, Ana Rita
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Cicevic, Svetlana
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Sommer, CarstenKassel
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Liu, Meiqi
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Pirdavani, AliHasselt
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Niklaß, Malte
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Lima, PedroBraga
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Turunen, Anu W.
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Antunes, Carlos Henggeler
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Krasnov, Oleg A.
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Lopes, Joao P.
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Turan, Osman
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Lučanin, VojkanBelgrade
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Tanaskovic, Jovan
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Nowaczyk, Slawomir

  • Google
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Halmstad University

in Cooperation with on an Cooperation-Score of 37%

Topics

  • data
  • computer science
  • railway train
  • forecasting
  • maintenance
  • monitoring
  • diagnosis
  • automobile industry
  • machinery
  • learning
  • machine learning
  • submarine
  • data file
  • technician
  • complex system
  • taxonomy
  • industry
  • assessment
  • modeling
  • theory
  • telematics
  • costs
  • vehicle fleet
  • trajectory
  • health
  • sensor
  • truck
  • repairing
  • turbocharger
  • connectivity
  • Markov process
  • history
  • Probability theory
  • case study
  • noise
  • neural network
  • architecture
  • aggregate
  • heavy duty truck
  • downtime
  • indicating instrument
  • forest
  • coding system
  • mining
  • time window
  • signal processing
  • driver
  • app
  • heavy duty vehicle
  • supporting
  • human being
  • foundation
  • big data
  • data analysis
  • algorithm
  • mechanical engineering
  • workshop
  • bus
  • profit
  • data mining
  • forecasting model
  • air compressor
  • city
  • control device
  • road
  • commercial vehicle
  • compressor
  • information system
  • deviation
  • coast
  • field study
  • histogram
  • air pressure
  • self organising system
  • data collection
  • customer
  • telecommunication
  • train consist
  • composite material
  • inductive interference
  • timetable
  • printed publicity
  • employed
  • experiment
  • engineering
  • sensitivity
  • show 56 more
  • show less

Publications

  • 2021Hierarchical Multi-class Classification for Fault Diagnosiscitations
  • 2020Early Prediction of Quality Issues in Automotive Modern Industry16citations
  • 2020Modeling turbocharger failures using Markov process for predictive maintenancecitations
  • 2020Stacked Ensemble of Recurrent Neural Networks for Predicting Turbocharger Remaining Useful Life12citations
  • 2019Interactive feature extraction for diagnostic trouble codes in predictive maintenance : A case study from automotive domain9citations
  • 2018Self-monitoring for maintenance of vehicle fleets23citations
  • 2018Features extracted from APPES to enable the categorization of heavy-duty vehicle drivers1citations
  • 2016Supporting analytical reasoning a study from the automotive industrycitations
  • 2016Supporting analytical reasoning : A study from the automotive industrycitations
  • 2015Predicting the need for vehicle compressor repairs using maintenance records and logged vehicle data87citations
  • 2015Evaluation of Self-Organized Approach for Predicting Compressor Faults in a City Bus Fleet25citations
  • 2013Analysis of Truck Compressor Failures Based on Logged Vehicle Datacitations
  • 2013Towards a Machine Learning Algorithm for Predicting Truck Compressor Failures Using Logged Vehicle Datacitations
  • 2012Ideas for Fault Detection Using Relation Discoverycitations

Places of action

Chart of shared publication
Pashami, Sepideh
6 / 7 shared
Del Moral, Pablo
1 / 1 shared
Mashhadi, Peyman Sheikholharam
1 / 1 shared
Shahbandi, Saeed Gholami
1 / 2 shared
Berck, Peter
2 / 2 shared
Sheikholharam Mashhadi, Peyman
2 / 2 shared
Niklasson, Tobias
1 / 1 shared
Khoshkangini, Reza
1 / 1 shared
Kharazian, Zahra
1 / 1 shared
Rahat, Mahmoud
1 / 1 shared
Thunberg, Klas
1 / 1 shared
Pirasteh, Parivash
1 / 1 shared
Löwenadler, Magnus
1 / 1 shared
Ydreskog, Henrik
1 / 1 shared
Byttner, Stefan
6 / 6 shared
Rögnvaldsson, Thorsteinn
6 / 9 shared
Prytz, Rune
5 / 5 shared
Svensson, Magnus
1 / 1 shared
Lodin, J.
1 / 1 shared
Carpatorea, I.
1 / 1 shared
Helldin, T.
1 / 2 shared
Pashami, S.
1 / 1 shared
Riveiro, Maria
2 / 8 shared
Byttner, S.
1 / 3 shared
Falkman, G.
1 / 2 shared
Helldin, Tove
1 / 3 shared
Falkman, Göran
1 / 5 shared
Fan, Yuantao
1 / 3 shared
Chart of publication period
2021
2020
2019
2018
2016
2015
2013
2012

Co-Authors (by relevance)

  • Pashami, Sepideh
  • Del Moral, Pablo
  • Mashhadi, Peyman Sheikholharam
  • Shahbandi, Saeed Gholami
  • Berck, Peter
  • Sheikholharam Mashhadi, Peyman
  • Niklasson, Tobias
  • Khoshkangini, Reza
  • Kharazian, Zahra
  • Rahat, Mahmoud
  • Thunberg, Klas
  • Pirasteh, Parivash
  • Löwenadler, Magnus
  • Ydreskog, Henrik
  • Byttner, Stefan
  • Rögnvaldsson, Thorsteinn
  • Prytz, Rune
  • Svensson, Magnus
  • Lodin, J.
  • Carpatorea, I.
  • Helldin, T.
  • Pashami, S.
  • Riveiro, Maria
  • Byttner, S.
  • Falkman, G.
  • Helldin, Tove
  • Falkman, Göran
  • Fan, Yuantao