Inverse vehicle routing for activity-based urban freight forecast modeling and city logistics

Soyoung Iris You, Joseph Ying Jun Chow, Stephen G. Ritchie

Research output: Contribution to journalArticle

Abstract

Goods movement is one of the fastest growing transportation sectors, affecting both economic and environmental sustainability, particularly in dense urban areas with traffic congestion and air pollution. To meet this challenge, urban public agencies have paid attention to policies and systems to facilitate efficient and sustainable city logistics. This paper proposes a modeling framework to consider both spatial–temporal constraints and a means to calibrate the model from observable data, based on an adaptation of an activity-based passenger model called the household activity pattern problem. Conceptual comparisons with a state-of-the-art freight forecasting methodology are made using an example. Application of the model is illustrated through formulating and implementing a Sequential Selective Vehicle Routing Problem associated with drayage truck activities at the San Pedro Bay Ports in Southern California.

Original languageEnglish (US)
Pages (from-to)650-673
Number of pages24
JournalTransportmetrica A: Transport Science
Volume12
Issue number7
DOIs
StatePublished - Aug 8 2016

Fingerprint

Vehicle routing
Logistics
logistics
Traffic congestion
traffic congestion
air pollution
Air pollution
Trucks
Sustainable development
urban area
sustainability
Economics
methodology
economics

Keywords

  • Activity-based model
  • City logistics
  • Inverse optimization
  • Tour-based model
  • Truck assignment
  • Vehicle routing problem

ASJC Scopus subject areas

  • Engineering(all)
  • Transportation

Cite this

Inverse vehicle routing for activity-based urban freight forecast modeling and city logistics. / You, Soyoung Iris; Chow, Joseph Ying Jun; Ritchie, Stephen G.

In: Transportmetrica A: Transport Science, Vol. 12, No. 7, 08.08.2016, p. 650-673.

Research output: Contribution to journalArticle

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