Use of Automatic Vehicle Location and Passenger Count Data to Evaluate Bus Operations

Hammerle, Meghan; Haynes, Michael; McNeil, Sue

doi:10.3141/1903-04

Cited by 23 publications

(5 citation statements)

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“…Originally, two causes, which are on-street effects and effects of the departure time at terminal, are analyzed using time-space trajectory graphs of several bus trips. Results showed that most of the headway problems originate as a result of irregular headways at the terminals [23,24]. Subsequently, empirical findings show that travel time between bus stops and dwell time at stops are the most two key causes of irregular headways [25] and several possible reasons are identified.…”

Section: Literature Reviewmentioning

confidence: 99%

Finding Causes of Irregular Headways Integrating Data Mining and AHP

Zhang

Wang

2015

IJGI

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Irregular headways could reduce the public transit service level heavily. Finding out the exact causes of irregular headways will greatly help to develop efficient strategies aiming to improve transit service quality. This paper utilizes bus GPS data of Harbin to evaluate the headway performance and proposes a statistical method to identify the abnormal headways. Association mining is used to dig deeper and recognize six causes of bus bunching. The AHP, embedded data analysis, is applied to determine the weight of each cause in the case of that these causes are combined with each other constantly. Results show that the front bus has a greater effect on bus bunching than the following bus, and the traffic condition is the most critical factor affecting bus headway.

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Section: Literature Reviewmentioning

confidence: 99%

Finding Causes of Irregular Headways Integrating Data Mining and AHP

Zhang

Wang

2015

IJGI

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“…Possible sources of unreliability include satellite unavailability, Partial/total signal blocking, or other temporary failure (Moreira-Matias et al, 2015). A common problem is data capture at the beginning or end of the route when the bus is in terminal (Furth et al, 2004;Hammerle et al, 2005). Some transit providers such as King Country Metro report 80% of data recovery from AVL (Furth, 2006).…”

Section: Introductionmentioning

confidence: 99%

Untitled

2017

AJER

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We analyzed AVL stop level data from a rural transit system to identify data completeness and systematic data capture failures. Systematic data loss could compromise the validity of further analyses of the data, such as schedule adherence or run time performance. We audited the data to identify missing values and possible data recording errors. The frequency of missing values was analyzed as a function of trip start time, stop number, day of the week, and last reported seconds late. We also perform an outlier and extreme value analysis as a function of missing records per trip. We conclude that there are systematic data capture errors in the system that needs to be addressed before further studies, such as run time analysis can be performed. Given the widespread adoption of an AVL system by rural transit system, it is recommended that detail data completeness analysis becomes routine before using the data generated to perform other studies. Contribution/ Originality This is, to our knowledge, the first paper to review data from a rural transit system and offer relatively easy and straight forward tools for data completeness analysis. The methodology could be replicated by other transit systems with limited data analysis resources. This research would be of interest to Transit systems that uses AVL technology, particularly rural transit systems as well as AVL hardware and software providers.

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“…Transit agencies deriving similar information with Automated Passenger Count (APC) data are able to use those data to support numerous planning and operational objectives (Hammerle et al 2005). Boarding and alighting data developed from AFC models are derived from passenger-specific travel patterns and can be connected with transfers to other bus services or subways.…”

Section: Introductionmentioning

confidence: 99%