Urban freight truck routing under stochastic congestion and emission considerations

Taesung Hwang, Yanfeng Ouyang

Research output: Contribution to journalArticlepeer-review


Freight trucks are known to be a major source of air pollutants as well as greenhouse gas emissions in U.S. metropolitan areas, and they have significant effects on air quality and global climate change. Emissions from freight trucks during their deliveries should be considered by the trucking service sector when they make routing decisions. This study proposes a model that incorporates total delivery time, various emissions including CO2, VOC, NOX, and PM from freight truck activities, and a penalty for late or early arrival into the total cost objective of a stochastic shortest path problem. We focus on urban transportation networks in which random congestion states on each link follows an independent probability distribution. Our model finds the best truck routing on a given network so as to minimize the expected total cost. This problem is formulated into a mathematical model, and two solution algorithms including a dynamic programming approach and a deterministic shortest path heuristic are proposed. Numerical examples show that the proposed approach performs very well even for the large-size U.S. urban networks.

Original languageEnglish (US)
Pages (from-to)6610-6625
Number of pages16
JournalSustainability (Switzerland)
Issue number6
StatePublished - 2015


  • Dynamic programming algorithm
  • Stochastic shortest path problem
  • Truck emission
  • Urban freight delivery

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Environmental Science (miscellaneous)
  • Geography, Planning and Development
  • Energy Engineering and Power Technology
  • Hardware and Architecture
  • Management, Monitoring, Policy and Law
  • Computer Networks and Communications
  • Renewable Energy, Sustainability and the Environment


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