The proposed research builds on current applications and findings of LinTim, a collection of different algorithms for planning steps in public transportation (currently railway lines). The objective of the research was twofold. In a first place, to implement a bus typical dataset to LinTim and compare delays in bus and in railway settings. In a second place, to evaluate delay management policies where bus lines are included. At the end of the research, a comparative service reliability analysis of high frequency (e.g. metro) and low frequency (e.g. railway) public transport systems takes place. Known reliability indices with respect to this question are critically reviewed, and a novel index accounting for the average delay experienced by the passengers in the network is introduced. Numerical tests on the performance of this index, using close to real world data from the German railway system and from the Athens metro, are also presented. Finally, the effects of delay management strategies in high and low frequency systems are discussed.

Implementation of the LinTim package for evaluating delay management strategies in public transportation / Gkoumas, Konstantinos. - (2009).

Implementation of the LinTim package for evaluating delay management strategies in public transportation

GKOUMAS, Konstantinos
2009

Abstract

The proposed research builds on current applications and findings of LinTim, a collection of different algorithms for planning steps in public transportation (currently railway lines). The objective of the research was twofold. In a first place, to implement a bus typical dataset to LinTim and compare delays in bus and in railway settings. In a second place, to evaluate delay management policies where bus lines are included. At the end of the research, a comparative service reliability analysis of high frequency (e.g. metro) and low frequency (e.g. railway) public transport systems takes place. Known reliability indices with respect to this question are critically reviewed, and a novel index accounting for the average delay experienced by the passengers in the network is introduced. Numerical tests on the performance of this index, using close to real world data from the German railway system and from the Athens metro, are also presented. Finally, the effects of delay management strategies in high and low frequency systems are discussed.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/417372
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