Repository logo
  • Log In
    New user? Click here to register. Have you forgotten your password?
Repository logo
  • Communities & Collections
  • Browse TUstorage
  • Log In
    New user? Click here to register. Have you forgotten your password?
  1. Home
  2. Browse by Author

Browsing by Author "Xiaoyong Ni"

Now showing 1 - 6 of 6
Results Per Page
Sort Options
  • No Thumbnail Available
    Dataset
    An Urban Road-Traffic Commuting Dynamics Study Based on Hotspot Clustering and a New Proposed Urban Commuting Electrostatics Model
    (MDPI AG, 2019) Xiaoyong Ni; Hong Huang; Yangyang Meng; Shiwei Zhou; Boni Su
  • No Thumbnail Available
    Distribution
    An Urban Road-Traffic Commuting Dynamics Study Based on Hotspot Clustering and a New Proposed Urban Commuting Electrostatics Model
    Xiaoyong Ni; Hong Huang; Yangyang Meng; Shiwei Zhou; Boni Su
  • No Thumbnail Available
    Distribution
    An Urban Road-Traffic Commuting Dynamics Study Based on Hotspot Clustering and a New Proposed Urban Commuting Electrostatics Model
    Xiaoyong Ni; Hong Huang; Yangyang Meng; Shiwei Zhou; Boni Su
  • No Thumbnail Available
    Dataset
    Revealing Spatial-Temporal Characteristics and Patterns of Urban Travel: A Large-Scale Analysis and Visualization Study with Taxi GPS Data
    (MDPI AG, 2019) Huihui Wang; Hong Huang; Xiaoyong Ni; Weihua Zeng
  • No Thumbnail Available
    Distribution
    Revealing Spatial-Temporal Characteristics and Patterns of Urban Travel: A Large-Scale Analysis and Visualization Study with Taxi GPS Data
    Huihui Wang; Hong Huang; Xiaoyong Ni; Weihua Zeng
  • No Thumbnail Available
    Distribution
    Revealing Spatial-Temporal Characteristics and Patterns of Urban Travel: A Large-Scale Analysis and Visualization Study with Taxi GPS Data
    Huihui Wang; Hong Huang; Xiaoyong Ni; Weihua Zeng

Contact

wdm@ulb.tu-darm...

This service is provided by University and State Library Darmstadt

Cookie settings / Privacy policy / About / Impressum

DFG-Logo mit Schriftzug und Förderhinweis

NFDI4Ing wird gefördert durch die DFG unter Projektnummer 442146713