Techniques and algorithms for solving the multiobjective path optimisation problem for car navigation

The conventional information used to guide automobile drivers in selecting their driving routes is the shortest-distance path (SDP). This research proposes a multiobjective path optimisation (MOPO) decision model to make a more precise simulation of the decision-making behaviour of driver route selection. Seven single-objective path optimisation (SOPO) decision models are taken into account to establish the MOPO decision model. They relate to travel time, travel cost, cumulative distance, roadway capacity, roadway grade, passed intersections and number of turns. To solve the MOPO problem, a two-stage technique which incorporates shortest path (SP) algorithms and techniques for solving the multiobjective programming problem and a path genetic algorithm (PGA) are proposed. To conduct the empirical study, a software tool - the multiobjective path optimisation analysis tool (MOPOAT) - was implemented. To demonstrate the advantages of the proposed model in supporting more diverse information to drivers to assist in route selection, several experiments were conducted utilising three real road networks with different roadway types and numbers of nodes and links. Finally, to deal with improvements in computational efficiency for identifying SPs in a large road network and for population initialisation of the PGA, the critical-section (CS) approach and the seed-path expansion (SPE) approach are proposed. (a)

  • Corporate Authors:

    University of New South Wales

    Gate 9, High Street
    Kensington, New South Wales  Australia  2052
  • Authors:
    • CHIU, C -
  • Publication Date: 2009


  • English

Media Info

  • Pagination: 2 FILES

Subject/Index Terms

Filing Info

  • Accession Number: 01143909
  • Record Type: Publication
  • Source Agency: ARRB
  • Files: ITRD, ATRI
  • Created Date: Nov 16 2009 12:13PM