Eco-reliable path finding in time-variant and stochastic networks

Wenjie Li, Lixing Yang, Li Wang, Xuesong Zhou, Ronghui Liu, Ziyou Gao

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


This paper addresses a route guidance problem for finding the most eco-reliable path in time-variant and stochastic networks such that travelers can arrive at the destination with the maximum on-time probability while meeting vehicle emission standards imposed by government regulators. To characterize the dynamics and randomness of transportation networks, the link travel times and emissions are assumed to be time-variant random variables correlated over the entire network. A 0–1 integer mathematical programming model is formulated to minimize the probability of late arrival by simultaneously considering the least expected emission constraint. Using the Lagrangian relaxation approach, the primal model is relaxed into a dualized model which is further decomposed into two simple sub-problems. A sub-gradient method is developed to reduce gaps between upper and lower bounds. Three sets of numerical experiments are tested to demonstrate the efficiency and performance of our proposed model and algorithm.

Original languageEnglish (US)
Pages (from-to)372-387
Number of pages16
StatePublished - 2017


  • Eco-reliable path finding
  • Lagrangian relaxation approach
  • Time-variant and stochastic network
  • Vehicle emission

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Modeling and Simulation
  • Renewable Energy, Sustainability and the Environment
  • Building and Construction
  • Fuel Technology
  • Energy Engineering and Power Technology
  • Pollution
  • Mechanical Engineering
  • General Energy
  • Management, Monitoring, Policy and Law
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering


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