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
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  • 2023
Vigliani, AlessandroTurin
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  • 2023
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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Vanhulsel, Marlies

  • Google
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in Cooperation with on an Cooperation-Score of 37%

Topics

  • driving
  • shopping
  • case study
  • industry
  • automobile
  • contaminant
  • road
  • highway traffic
  • forecasting
  • transportation engineering
  • passenger
  • passenger car
  • vehicle occupant
  • environmental engineering
  • particulate
  • fuel consumption
  • definition
  • commercial vehicle
  • diesel fuel
  • electric power conditioning
  • trip length
  • air pollution
  • highway
  • air quality
  • night
  • trip purpose
  • data
  • demand-modeling
  • region
  • child
  • household
  • travel
  • scheduling
  • transport demand
  • adult
  • yielding
  • family
  • weekday
  • travel pattern
  • census
  • skeletal system
  • estimating
  • positioning
  • alignment
  • algorithm
  • simulation
  • tree
  • learning
  • regression analysis
  • modernization
  • modeling
  • traffic flow
  • inductive interference
  • base line
  • variable
  • prototype
  • state of the art
  • vehicle
  • survey
  • software
  • exhaust gas
  • health
  • public health
  • transient
  • automobile driver
  • gender
  • medical service
  • traffic mode
  • train consist
  • procurement
  • constraint
  • bottleneck
  • foundation
  • calibration
  • timetable
  • shopping facility
  • driver licence
  • implementation
  • employed
  • intelligence
  • artificial intelligence
  • sleep
  • history
  • waiting time
  • operations research
  • pollution
  • estimate
  • decision making
  • traffic safety
  • environmental impact
  • travel time
  • supervisor
  • supporting
  • aggregate
  • time window
  • land use
  • government
  • show 67 more
  • show less

Publications

  • 2014Is Driving 1 km to Work Worse for the Environment Than Driving 1 km for Shopping?citations
  • 2014Road transportation emission inventories and projections – Case study of Belgium: Methodology and pitfalls21citations
  • 2014Is driving 1 km to workworse for the environment than driving 1 km for shoppingcitations
  • 2012Tracking household routines using scheduling hypothesis embedded in skeletons7citations
  • 2011Measuring dissimilarity of geographically dispersed space-time paths11citations
  • 2009Simulation of sequential data: An enhanced reinforcement learning approach16citations
  • 2008Application of Online Regression Tree Induction to Forecast Traffic Flowscitations
  • 2008Implementing an Improved Reinforcement Learning Algorithm for the Simulation of Weekly Activity-Travel Sequencescitations
  • 2007Gender-linked disparity in vehicle exhaust emissions? Results from an activity-based survey1citations
  • 2007Calibrating a New Reinforcement Learning Mechanism for Modeling Dynamic Activity-Travel Behavior and Key Eventscitations
  • 2007Use of a relational reinforcement learning algorithm to generate dynamic activity-travel patternscitations
  • 2006Generating dynamic activity-travel schedulescitations

Places of action

Chart of shared publication
BELLEMANS, Tom
3 / 231 shared
Wets, Geerts
11 / 611 shared
Janssen, Stijn
1 / 9 shared
Beckx, Carolien
4 / 36 shared
Dhondt, Stijn
2 / 9 shared
De Vlieger, Ina
2 / 2 shared
Degraeuwe, Bart
3 / 12 shared
Kochan, Bruno
2 / 77 shared
Lefebvre, Wouter
2 / 12 shared
Janssens, Davy
10 / 344 shared
Panis, L. L. R. Int
3 / 220 shared
Vankerkom, Jean
1 / 2 shared
Vlieger, Ina De
2 / 3 shared
Vanhoof, Koen
4 / 74 shared
Liu, Feng
1 / 25 shared
Hannes, Els
1 / 28 shared
Beckx, Carolein
1 / 2 shared
Torfs, Rudi
1 / 9 shared
Chart of publication period
2014
2012
2011
2009
2008
2007
2006

Co-Authors (by relevance)

  • BELLEMANS, Tom
  • Wets, Geerts
  • Janssen, Stijn
  • Beckx, Carolien
  • Dhondt, Stijn
  • De Vlieger, Ina
  • Degraeuwe, Bart
  • Kochan, Bruno
  • Lefebvre, Wouter
  • Janssens, Davy
  • Panis, L. L. R. Int
  • Vankerkom, Jean
  • Vlieger, Ina De
  • Vanhoof, Koen
  • Liu, Feng
  • Hannes, Els
  • Beckx, Carolein
  • Torfs, Rudi