Analysis of Freight Tours in a Congested Urban Area
UFC – Long Beach CA – 4 December 2007
Figliozzi
1
Analysis of Freight Tours in a
Congested Urban Area Using
Disaggregated Data: Characteristics
and Data Collection Challenges
Miguel A. Figliozzi
Portland State University
Lynsey Kingdon
Andrea Wilkitzki
University of Sydney
Analysis of Freight Tours in a Congested Urban Area
UFC – Long Beach CA – 4 December 2007
Figliozzi
2
Motivation
• Confidentiality issues are usually an insurmountable
barrier that precludes the collection of detailed and
complete freight data.
• Understandably, companies are unwilling to
disclose any type of information that may be used
by competitors or that may infringe customers’
rights regarding privacy, proprietary data, or
security
Analysis of Freight Tours in a Congested Urban Area
UFC – Long Beach CA – 4 December 2007
Figliozzi
3
Motivation
• If LTL disaggregated tour data is available, what
can we learn from the disaggregated data?
• Is several months of daily truck data in a
congested urban area enough for operational
purposes (route design) ?
• How much information is needed for adequate LTL
route planning?
Analysis of Freight Tours in a Congested Urban Area
UFC – Long Beach CA – 4 December 2007
Figliozzi
4
A Case Study of LTL Deliveries in Sydney
Analysis of Freight Tours in a Congested Urban Area
UFC – Long Beach CA – 4 December 2007
Figliozzi
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LTL Sydney Data
• The routing data to be presented in the next slides
was extracted from truck activity sheets over an
eight-month period.
• The data corresponds to less than truckload (LTL)
routes of a twelve-ton truck, making deliveries to
companies in the retailing, service, and
manufacturing sectors.
Analysis of Freight Tours in a Congested Urban Area
UFC – Long Beach CA – 4 December 2007
Figliozzi
6
LTL Sydney Data
• The data was provided by a freight forwarding
company based in Botany Bay (port area)
• In the eight-month period the truck served 190
different customers, however, the top 20% of
delivery locatio