Using Archived AVL-APC Data to Improve Transit Performance and Management (2006) / Chapter Skim
Currently Skimming:
Pages 14-20
The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.
From page 14... ...
The rate at which data is rejected for inability to match it to a route can be substantial, reaching 40% at agencies that were interviewed. Data matching was cited by many agencies as the single greatest challenge faced in making their AVL-APC data useful.
|
From page 15... ...
About 3 min of running time before each timepoint, the central computer radios to the bus a message indicating the odometer reading at which the coming timepoint will be located; local sensing and logic will then suffice to know when the bus has reached the next timepoint. This technique substantially improved King County Metro's success at matching timepoints.
|
From page 16... ...
Not being able to track operations from the start to the end of a line compromises the integrity of route-level running time analyses such as determining periods of homogeneous running time and the sufficiency of recovery time. Agencies have used various means to improve end-of-line identification.
|
From page 17... ...
The researchers found no examples of transit agencies extracting time-at-location information from polling data or basing any analysis of running time or schedule adherence on it. The only off-line use found for polling data was for detailed investigations of incidents using playback.
|
From page 18... ...
For example, some running time and schedule adherence measures are defined in terms of departure times, others in terms of arrival times, and others involve a difference between arrival time at one point and departure time at a previous timepoint. Off-line analysis therefore benefits from having both arrival and departure times recorded, particularly if operators hold at timepoints.
|
From page 19... ...
However, if exception data is all that is available for off-line analysis, analysis possibilities become severely limited. For example, if only off-schedule buses create timepoint records, running times can only be measured for off-schedule buses.
|
From page 20... ...
King County Metro recovers AVL data from about 80% of its scheduled trips. However, with the entire fleet instrumented, data recovery rates are not so important with AVL unless there is systematic data loss in particular regions.
|
Key Terms
This material may be derived from roughly machine-read images, and so is provided only to facilitate research.
More
information on Chapter Skim is available.