Time reliability problems are unavoidable, owing to the stochastic context in which bus services are operated. Therefore, characterizing their reliability and understanding possible sources of unreliability provides an opportunity to keep buses on schedule and/or maintain planned headways. Measuring time reliability is technologically feasible by automatic vehicle location (AVL) systems, which can collect disaggregated data on the delivered service and disclose information on its performance. This paper proposes the first offline framework applicable to any bus route in order to accurately characterize the bus stops and the time periods in which reliability is insufficient, and to disclose the systematic unreliability sources from collected AVL data and select preventive strategies, accordingly. The framework is tested on the real case study of a bus route, using about 40 000 AVL data records provided by the bus operator CTM in Cagliari, Italy. The experimentation shows that this framework can be adopted by transit managers for accurate reliability analysis.

An offline framework for the diagnosis of time reliability by automatic vehicle location data

BARABINO, BENEDETTO;DI FRANCESCO, MASSIMO;
2017-01-01

Abstract

Time reliability problems are unavoidable, owing to the stochastic context in which bus services are operated. Therefore, characterizing their reliability and understanding possible sources of unreliability provides an opportunity to keep buses on schedule and/or maintain planned headways. Measuring time reliability is technologically feasible by automatic vehicle location (AVL) systems, which can collect disaggregated data on the delivered service and disclose information on its performance. This paper proposes the first offline framework applicable to any bus route in order to accurately characterize the bus stops and the time periods in which reliability is insufficient, and to disclose the systematic unreliability sources from collected AVL data and select preventive strategies, accordingly. The framework is tested on the real case study of a bus route, using about 40 000 AVL data records provided by the bus operator CTM in Cagliari, Italy. The experimentation shows that this framework can be adopted by transit managers for accurate reliability analysis.
2017
automotive engineering, computer science applications1707 computer vision and pattern recognition; mechanical engineering; bus transportation; reliability analysis; stochastic systems; automatic vehicle location raw data; service quality; time reliability measure; transit route; wealth of data
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11584/182366
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