Real-World Data Driven Characterization of Urban Human Mobility Patterns

Details

• Creators:
  Jauhri, Abhinav ; Shen, John Paul
• Corporate Creators:
  Carnegie Mellon University. Traffic21 University Transportation Center (UTC)
• Corporate Contributors:
  United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
• Subject/TRT Terms:
  Adverserial Networks Algorithms Cities City Planning Power Law Data Analysis Densification Forecasting Graph Generators Graph Mining Mobility Network Evolution Online Algorithms Ridesharing Ridesourcing Spatial Analysis Synthetic Data Travel Patterns Urban Areas

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• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final Report
• Corporate Publisher:
  Mobility21, Carnegie Mellon University
• Abstract:
  Human movement in urban areas is a complex phenomenon to analyze and understand. The geographical spread of human movement, described by locations between which individuals tend to travel, varies from city to city. The geographical spread of such locations within a city would also vary with time. This research explores how to formally analyze and understand human mobility using large sets of real-world data from ride-sharing services for more than a dozen cities and to derive succinct characterizations for large urban areas which account for both geographical and temporal changes. A wide range of machine learning problems require immense amounts of data. To overcome this issue for human mobility, we propose a framework which includes a stochastic graph model, and adversarial networks to generate synthetic human mobility data which conforms with the geographical and temporal characteristics observed in real data. Deriving interesting insights from large sets of data and applying those to real-world applications can be challenging. Here we also highlight how formal characterizations can be applied to applications like ride pooling and vehicle placement. We also derive performance bounds for online algorithms using city level characterizations. Finally, we provide an open-source framework to understand properties of human movement, generate synthetic human mobility data, and apply it to different what-if scenarios for real-world applications. Using such a framework would help public entities and researchers alike to thoroughly understand a complex and highly relevant phenomenon.
• Format:
  PDF
• Funding:
  69A3551747111
• Collection(s):
  University Transportation Centers US Transportation Collection
• Main Document Checksum:
  urn:sha-512:c48a07b96839e7714d26ace89322e2f4cd0a1787a889b1780b9bc9034f7f1685cdd054bf8b63cd5fab2225b09f805f3f71d6db73ab63fa2155288ca1a156c4d7
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/57451/dot_57451_DS1.pdf
• File Type:
  [PDF - 20.78 MB ]