Local public transport companies, especially in large cities, are facing every day the problem of managing delays and small disruptions. Disruption management is a well-established practice in airlines and railways. However, in local public transport the approaches to these problems have followed a different path, mainly focusing on holding and short-turning strategies not directly associated with the driver schedul- ing. In this paper we consider the case of the management of urban surface lines of Azienda Trasporti Milanese (ATM) of Milan. The main issues are the service regu- larity as a measure of the quality of service, and the minimization of the operational costs due to changes in the planned driver scheduling. We propose a simulation- based optimization system to cope with delays and small disruptions that can be effectively used in a real-time environment and takes into account both vehicle and driver scheduling. The proposed approach is tested on real data to prove its actual applicability.

Delay and disruption management in local public transportation via real-time vehicle and crew re-scheduling: a case study

Malucelli, Federico;Tresoldi, Emanuele
2019-01-01

Abstract

Local public transport companies, especially in large cities, are facing every day the problem of managing delays and small disruptions. Disruption management is a well-established practice in airlines and railways. However, in local public transport the approaches to these problems have followed a different path, mainly focusing on holding and short-turning strategies not directly associated with the driver schedul- ing. In this paper we consider the case of the management of urban surface lines of Azienda Trasporti Milanese (ATM) of Milan. The main issues are the service regu- larity as a measure of the quality of service, and the minimization of the operational costs due to changes in the planned driver scheduling. We propose a simulation- based optimization system to cope with delays and small disruptions that can be effectively used in a real-time environment and takes into account both vehicle and driver scheduling. The proposed approach is tested on real data to prove its actual applicability.
2019
Delay management · Disruption management · Local public transportation · Real-time optimization · Real-word scenario · Big data · Vehicle scheduling · Crew scheduling
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1109637
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