An Offline Framework for Handling Automatic Passenger Counting Raw Data

Barabino, Benedetto; Francesco, Massimo Di; Mozzoni, Sara

doi:10.1109/tits.2014.2315573

Cited by 29 publications

(24 citation statements)

References 21 publications

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“…To conclude, further studies are recommended to confirm these outcomes and perform the analysis on more complex AVL architectures for tram, trolleybus, and so on. Moreover, in the future, we will propose more sophisticated reliability metrics integrated with passengers volumes collected by Automatic Passengers Counting …”

Section: Discussionmentioning

confidence: 99%

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Time Reliability Measures in bus Transport Services from the Accurate use of Automatic Vehicle Location raw Data

Barabino¹,

Francesco

Mozzoni³

2016

Quality & Reliability Eng

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Archived automatic vehicle location (AVL) data are widely used in bus transportation, but typically contain anomalies, such as missing data points and unseen bus overtakings. These anomalies may alter the measurement of time reliability (i.e., headways and schedule time deviations) of buses at stops with respect to passenger experiences. However, in many studies, anomalies are ignored, neglected, or partially addressed. This paper investigates the effect of AVL anomalies on headways and schedule deviations, which are keys for regularity and punctuality evaluations and are well-practicized among bus operators worldwide. The analysis is carried out on six scenarios, each representing a combination of AVL data anomalies. The outcomes show that addressing anomalies result in more severe regularity measures, which point out criticalities suffered by passengers, whereas it can be skipped for the measurement of punctuality. In addition, an increase of 5% of missing data results in the increase of 5% in the coefficient of variation of the headway, but it does not lead to meaningful variations in punctuality measures. These results are useful for both practitioners interested in passenger-oriented measurement of time reliability and researchers working on the disclosure of the determinants of unreliability

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Section: Discussionmentioning

confidence: 99%

“…Moreover, in the future, we will propose more sophisticated reliability metrics integrated with passengers volumes collected by Automatic Passengers Counting. [44][45][46][47]…”

Section: Discussionmentioning

confidence: 99%

Time Reliability Measures in bus Transport Services from the Accurate use of Automatic Vehicle Location raw Data

Barabino¹,

Francesco

Mozzoni³

2016

Quality & Reliability Eng

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“…• Create a better understanding of the PTS, e.g. concerning PTS timeliness and vehicle-kilometers (Uniman et al 2010;Pelletier et al 2011;Wang et al 2011;Barabino et al 2014 (Pelletier et al 2011;Stelzer et al 2016). • Charge travelers for their journeys (Zeller 2013;Narzt et al 2015;Zöscher et al 2015).…”

Section: Literature Review: Solutionsmentioning

confidence: 99%

“…Some systems collecting traveler information focus on Automatic Passenger Counting (APC). APC technology is designed to measure the number of on-board passengers, for instance by means of infrared sensors or video image (Barabino et al 2014). The travelers are often detected at vehicle entrance and exit, for instance by sensors installed over the vehicle doors (Oberli et al 2010;Barabino et al 2014); however, detection when onboard also exists (Kotz et al 2015) (cf.…”

Section: Review Summarymentioning

confidence: 99%

“…Create a better understanding of travelers' PTS usage behavior (both individual behaviors and aggregated travel patterns), e.g. for transport network planning, terminus planning, urban planning, modeling, marketing or research purposes(Oberli et al 2010;Lathia and Capra 2011;Pelletier et al 2011;Stenneth et al 2011; Wang et al 2011;Munizaga and Palma 2012;Kostakos et al 2013;Ma et al 2013;Barabino et al 2014;Nitsche et al 2014;Arnone et al 2016;Fujiyama and Cao 2016;Stelzer et al 2016;Zhong et al 2016). Improve short-term forecasting, e.g.…”

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confidence: 99%

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Exploring the potential of using real-time traveler data in public transport disturbance management

Jevinger

Persson

2019

Public Transp

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New and emerging technologies, such as connected sensors, smartphones and smart cards, offer new possibilities to collect rich real-time information about travelers. Moreover, smartphones also enable travelers to actively share information, for instance, about their intended travel plans. This type of information can be used to improve public transport disturbance management. In this paper, the potential gain of collecting different types of information about travelers is explored to support action decisions made by public transport actors, during unplanned disturbances. Based on interviews and workshops, the paper provides a mapping between different information types and possible action decisions that can be supported. Furthermore, based on a literature review focused on current and potential technical solutions, a guidance to which solutions support which type of action decisions, is also provided. Amongst others, the results show that automated fare collection, which is one of the most commonly implemented systems providing real-time information about the traveler, can support a large number of action decisions relevant in unplanned disturbance scenarios. The technical solution providing the most extensive information, and thereby providing the best support for the action decisions, involves smartphone apps delivering user-generated information. The drawback with this solution is that it might violate privacy, and that it typically relies on the travelers providing relevant information voluntarily.

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Characterizing, measuring, and managing transit service quality

Barabino¹,

Francesco

2016

J of Advced Transportation

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SUMMARYRecent studies to evaluate the quality of transit service are generating a good amount of renewed interest in an old idea, the passenger's perspective; this new interest stems from recognizing that transit service quality should be characterised, measured, and managed by parameters capturing both passenger and transit operator perspectives. However, although the selected parameters are user-oriented in their input, the output may not be as user-oriented as considered, and the number or the percentage of passengers is often neglected. As a result, the findings are often misleading because the perspectives of transit operators dominate. Therefore, academics and practitioners must rethink their strategies of quality analysis of public transportation by stressing more on the role of passengers. These challenges are addressed in this paper with a practical, simple, and holistic framework, for Transit Quality (TRANSQUAL). This framework provides for the involvement of all stakeholders in the characterisation, measurement, and management of the stages of quality monitoring, which is jointly analyzed at different planning levels. In the characterization stage, the framework supports the selection of parameters to be monitored. The measurement stage sets and measures four quality areas in terms of percentage of passengers who expect a predefined level of service, for whom the service is designed, who receive the planned service, and who perceive the service as delivered. The management stage computes the differences between these percentages, points out criticalities, and recommends corrective actions. These stages are investigated in-depth, integrated, and discussed in a real-life case study.

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An Offline Framework for Handling Automatic Passenger Counting Raw Data

Cited by 29 publications

References 21 publications

Time Reliability Measures in bus Transport Services from the Accurate use of Automatic Vehicle Location raw Data

Time Reliability Measures in bus Transport Services from the Accurate use of Automatic Vehicle Location raw Data

Exploring the potential of using real-time traveler data in public transport disturbance management

Characterizing, measuring, and managing transit service quality

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