The freight landscape : using secondary data sources to describe metropolitan freight flows.

Details

• Creators:
  Giuliano, Genevieve ; Kang, Sanggyun ; Yuan, Juan ; Hutson, Nathan
• Corporate Creators:
  University of Southern California. School of Public Policy
• Corporate Contributors:
  United States. Department of Transportation. University Transportation Centers (UTC) Program
• Subject/TRT Terms:
  • Freight traffic
  • Traffic flow
  • Travel demand
  • Data analysis
  • Data mining
  • Population density
  • Spatial analysis
  • Case studies
• Publication/ Report Number:
  METRANS UTC 1-1B
• Resource Type:
  Tech Report
• Geographical Coverage:
  California
• Corporate Publisher:
  METRANS Transportation Center (Calif.)
• Abstract:
  Metropolitan areas around the world are seeking to better manage freight flows and reduce negative impacts on local populations. A major challenge to better urban freight management is the lack of data; little is known about freight movements at the intra-metropolitan level. We develop the concept of a freight landscape: spatial patterns of freight activity. We use population and employment density quartiles to explain spatial patterns of development in four metropolitan areas in California: Los Angeles, San Francisco, Sacramento and San Diego. We hypothesize that the freight landscape can be described using data on population, employment and transport system supply. We test the concept using network model data for the Los Angeles region and San Francisco region. We find that in both cases, our simple proxies have significant explanatory value, and hence may provide an effective means for approximating spatial patterns of freight activity.
• Format:
  PDF
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/29687/dot_29687_DS1.pdf
• File Type:
  [PDF - 4.57 MB]
• Collection(s):
  University Transportation Centers ; US Transportation Collection
• Main Document Checksum:
  urn:sha-512:c98eea808d10d4af8c0c98eb15aa41d1e80c6070825ae1b8a555622c05d0fc2db57b98c2d27dcbbcd9e4ab8f7a9bd05e26cfc84505d54acedee1c67e9d7967ea