Abstract In the field of transport planning we are often interested in solving combinatorial optimization problems on subsets of network nodes which are requiring visit with a certain probability (for example, this is typical in dial-a-ride problems). Such cases are well suited for the so-called a priori optimization strategy that represents a potential more effective alternative to the traditional reoptimization strategy. Indeed, such strategy copes when there are some stochastic elements expressly enclosed into the basic problem formulation. This is an important feature, because the resulting model is more respondent to the real world characteristics that are always affected by random causes, such as travel times, stop locations, demand fluctuations, and so on. The paper first presents the a priori optimization criteria and than discusses its applications to some interesting problems of transit network planning. Namely, they are the probabilistic traveling salesman problem (PTSP) and the probabilistic traveling salesman facility location problem (PTSFLP). The PTSP is better suited for the analysis and design of a demand responsive network. The PTSFLP is better suited for strategic planning studies of depot (or terminal) locations in a low demand operations contest. The second part of the paper deals with some heuristic procedures suited for near optimal solutions.Finally, we report the results of a number of computational experiments that we have performed on a demand-responsive transit network embedded in a real low demand contest of a rural area in Central Tuscany. -------------------------------------------------------------------------------- Reaxys Database Information |

A-priori combinatorial optimization tools for transit network planning

PRATELLI, ANTONIO
2000

Abstract

Abstract In the field of transport planning we are often interested in solving combinatorial optimization problems on subsets of network nodes which are requiring visit with a certain probability (for example, this is typical in dial-a-ride problems). Such cases are well suited for the so-called a priori optimization strategy that represents a potential more effective alternative to the traditional reoptimization strategy. Indeed, such strategy copes when there are some stochastic elements expressly enclosed into the basic problem formulation. This is an important feature, because the resulting model is more respondent to the real world characteristics that are always affected by random causes, such as travel times, stop locations, demand fluctuations, and so on. The paper first presents the a priori optimization criteria and than discusses its applications to some interesting problems of transit network planning. Namely, they are the probabilistic traveling salesman problem (PTSP) and the probabilistic traveling salesman facility location problem (PTSFLP). The PTSP is better suited for the analysis and design of a demand responsive network. The PTSFLP is better suited for strategic planning studies of depot (or terminal) locations in a low demand operations contest. The second part of the paper deals with some heuristic procedures suited for near optimal solutions.Finally, we report the results of a number of computational experiments that we have performed on a demand-responsive transit network embedded in a real low demand contest of a rural area in Central Tuscany. -------------------------------------------------------------------------------- Reaxys Database Information |
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11568/164464
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