A GPS Data Processing Framework for Analysis of Drayage Truck Tours

A Clean Trucks Program (CTP) has been enacted at California’s San Pedro Bay Ports (SPBP) of Long Beach and Los Angeles, to help address major environmental issues associated with port operations. “Clean trucks” (meeting 2007 model year emission standards) that utilized public funds to replace older,...

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Veröffentlicht in:KSCE journal of civil engineering 2018, 22(4), , pp.1454-1465
Hauptverfasser: You, Soyoung Iris, Ritchie, Stephen G.
Format: Artikel
Sprache:eng
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Zusammenfassung:A Clean Trucks Program (CTP) has been enacted at California’s San Pedro Bay Ports (SPBP) of Long Beach and Los Angeles, to help address major environmental issues associated with port operations. “Clean trucks” (meeting 2007 model year emission standards) that utilized public funds to replace older, polluting drayage trucks were required to be fitted with GPS units for compliance monitoring, with an expectation that freight truck movements could be investigated more precisely. Such implementation also served as a prototype of emerging smart freight mobility concepts, which are often heavily data-driven processes, but which should provide data and insights that are useful to both researchers and practitioners. Accordingly, this paper reports on research to develop a comprehensive framework for processing SPBP clean truck GPS data, to both interpret tour behavior of clean drayage trucks, and to prepare sufficient tour data for clean truck modeling at the SPBP. An important finding is that clean trucks at the SPBP have distinct tour characteristics. First, most completed a tour within one day, but one day of travel behavior is not necessarily representative of any other day. Second, the identified tour types contain repetitive trip patterns while other commercial trucks mostly tend to travel as circulative patterns. These insights into clean truck behavior at the SPBP potentially provide more accurate depictions of current conditions and better projections of future conditions for freight related improvement plans and models.
ISSN:1226-7988
1976-3808
DOI:10.1007/s12205-017-0160-6