Methods for large scale urban network design with concave improvement costs
Linköping Institute of Technology, Department of Mathematics, 1980 - Technology & Engineering - 18 pages
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150 assignment problems accuracy and computational appnoximation to investment approach in LeBlanc bound on F(x*,y bound technique branch and bound computational requirements concave function CONCAVE IMPROVEMENT COSTS continuous improvement variables convex denote the optimal DESIGN WITH CONCAVE discuss solution algorithms Figune flow on link function gIJ(yIJ given to computationally improvement cost functions IMPROVEMENT COSTS Kurt Institute of Technology iteration Jornsten Linkoping Institute LARGE SCALE URBAN LeBlanc Vanderbilt University lemma linear approximation linear function linearized problem link ij link improvement Linkﬁping METHODS FOR LARGE model accuracy Nashville network design problem non-convex objective function value optimal solution original problem P2 and P3 possible improvement levels practical capacity problem P1 proposed approach road network design SCALE URBAN NETWORK shown in figure slope béj solution to P1 solved and added solving 150 assignment specified urban network Sweden Tennesee Report terms gij(yij traffic assignment problem upper bound URBAN NETWORK DESIGN variables yij yfJ1 Zine