Sensitivity analysis of optimal routes, departure times and speeds for fuel-efficient truck journeys

Embedded within the vehicle "routing" problem of determining the order in which customers are served, is the route choice problem of which sequence of roads to use between a pair of pick-up/drop-off locations, and this latter is the focus of the paper. When the objective is something other than travel time, such as fuel consumption, an additional control dimension is that of speed, and in a time-varying context the question of optimal speed determination is no longer a local one, due to potential downstream interactions. This also brings in the possibility to adjust departure times. Recently this problem, of joint route, departure time and speed determination for fuel minimization in a time-varying network, was shown to be efficiently solvable using a Space-Time Extended Network (STEN). In the present paper, the authors explore the sensitivity of the optimal solutions produced to: i) the fidelity of the within-day traffic information; ii) the currency of between-day traffic information in comparison with historical mean conditions; iii) the availability of historical information on variability for risk-averse routing; and iv) competition from other equally-optimal or near equally-optimal solutions. The authors set out the methods by which each of these tests may be achieved by adaptation of the underlying STEN, taking care to ensure a consistent reference basis, and describe the potential real-life relevance of each test. The results of illustrative numerical experiments are reported from interfacing the methods with real-time data accessed through the Google Maps API.


  • English

Media Info

  • Media Type: Web
  • Features: References;
  • Pagination: 7p
  • Monograph Title: 2019 6th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS 2019)

Subject/Index Terms

Filing Info

  • Accession Number: 01747717
  • Record Type: Publication
  • ISBN: 9781538694855
  • Files: TRIS
  • Created Date: May 29 2020 12:32PM