Recent interests in transit services have captured attention of experts on the monitoring of public transport quality. Previous research focused on relevant models and methods to monitor the quality of transit services and showed where and when diferent service quality levels occur. However, there was little attention to detect objectively a pool of key quality indicators (KQI) to be monitored, from a large set. This paper covers this gap by the proposal of an integrated approach, which identifes a long list of KQI, defnes their properties, involves experts to elicit judgments for each KQI, evaluates the long list, and points out the most promising set. This integrated approach is demonstrated with an application based on an international survey and a Monte Carlo simulation method. Moreover, a restricted and relevant set of 9 overlapping KQI is derived by linking these results with those obtained from two diferent approaches.

An integrated approach to select key quality indicators in transit services

Benedetto Barabino;Nicola Aldo Cabras;Claudio Conversano
;
Alessandro Olivo
2020-01-01

Abstract

Recent interests in transit services have captured attention of experts on the monitoring of public transport quality. Previous research focused on relevant models and methods to monitor the quality of transit services and showed where and when diferent service quality levels occur. However, there was little attention to detect objectively a pool of key quality indicators (KQI) to be monitored, from a large set. This paper covers this gap by the proposal of an integrated approach, which identifes a long list of KQI, defnes their properties, involves experts to elicit judgments for each KQI, evaluates the long list, and points out the most promising set. This integrated approach is demonstrated with an application based on an international survey and a Monte Carlo simulation method. Moreover, a restricted and relevant set of 9 overlapping KQI is derived by linking these results with those obtained from two diferent approaches.
2020
Transit service quality monitoring; Transit service quality indicators; Monte Carlo simulation; Integrated approach
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11584/284004
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