Document Type

Conference Proceeding

Publication Date



Freight transportation, Traffic congestion -- Mathematical models


Confidentiality issues are usually an insurmountable barrier that precludes the collection of detailed and complete freight data. However, when detailed disaggregated truck activity data is available, the analysis of commercial vehicle routes and trip chain structures can provide insightful information about urban commercial vehicle tours, travel patterns, and congestion levels. Where truck data is usually aggregated and cross-sectional, this research analyzes several months of detailed truck activity records in a congested urban area and thus contributes a level of investigation unavailable with aggregated data. To the best of the author's knowledge, there is no published research regarding the analysis of disaggregated truck data in congested areas. Data corresponds to the daily activity of less than truckload (LTL) delivery tours in the city of Sydney. The analysis of the data provides insightful information about urban truck tours and congestion levels. This paper identifies route patterns and analyzes their relationship to trip and tour length distribution. Travel between different industrial suburbs explains the shape of multimodal trip length distributions. Variations in daily demand explain the normal-like shape of the tour trip distribution. Tour data indicates that there is no clear relationship between tour distance, percentage of empty trips, and percentage of empty distance. Congestion data indicates that the standard deviation of travel speed is significant. In addition, correlations between travel times are positive and they should be used as an additional congestion measure for trip-chains or urban freight tours. Despite the availability of complete tour data, tour congestion analysis proved challenging due to the large number of customers visited and links traveled during the period of study.


Proceedings 2nd Annual National Urban Freight Conference, Long Beach, CA. December, 2007.

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