In high frequency transit services, irregularity is unavoidable due to the stochastic context in which bus services are operated. As a result, the measurement of the regularity and the identification of possible irregularity sources provide an opportunity to maintain scheduled headways. As far as the authors know, the irregularity was typically investigated from data on scheduled and actual arrival (or departure) times at bus stops. However, information has been seldom inferred on arrival and departure headways between two consecutive bus stops. Since it is difficult to maintain the planned timetable with short headways, this paper proposes an offline framework to measure the regularity over all bus stops and time periods and disclose the most common irregularity sources from collected Automatic Vehicle Location (AVL) data. Easy-to-read control dashboards show the viability of this framework on a real bus routedirection with about 124,500 AVL data records. Therefore, transit managers would benefit from using this framework to make accurate regularity analysis and possible service revisions.

Diagnosis of Irregularity Sources by Automatic Vehicle Location Data

Barabino, Benedetto
Primo
;
Di Francesco, Massimo
Ultimo
2021-01-01

Abstract

In high frequency transit services, irregularity is unavoidable due to the stochastic context in which bus services are operated. As a result, the measurement of the regularity and the identification of possible irregularity sources provide an opportunity to maintain scheduled headways. As far as the authors know, the irregularity was typically investigated from data on scheduled and actual arrival (or departure) times at bus stops. However, information has been seldom inferred on arrival and departure headways between two consecutive bus stops. Since it is difficult to maintain the planned timetable with short headways, this paper proposes an offline framework to measure the regularity over all bus stops and time periods and disclose the most common irregularity sources from collected Automatic Vehicle Location (AVL) data. Easy-to-read control dashboards show the viability of this framework on a real bus routedirection with about 124,500 AVL data records. Therefore, transit managers would benefit from using this framework to make accurate regularity analysis and possible service revisions.
Reliability; Schedules; High frequency; Analytical models; Computer architecture; Time-frequency analysis; Data models
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11584/259856
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