Short-Term Forecasting of Bicycle Traffic Using Structural Time Series Models
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PDFItem Type:
Conference PaperDate:
2014Access:
openAccessCitation:
Doorley, R., Pakrashi, V., Caulfield, B., Ghosh, B., Short-Term Forecasting of Bicycle Traffic Using Structural Time Series Models, 17th International IEEE Conference on Intelligent Transportation Systems, Qingdao, China, 2014, 2014Abstract:
Short term forecasting algorithms are widely
used for prediction of vehicular traffic
flows
for adaptive traffic
management. However, despite the increasing interest in the
promotion of cycling in cities, little research has been carried
out into the use of traffic forecasting algorithms for bicycle
traffic. Structural time series models allow the various
components of a time series such as level, seasonal and
regression
effects
to be modelled separately to allow analysis of
previous trends and forecasting.
In this paper, a case study at
a
segregated bicycle lane in Dublin,
Ireland
was performed to
test the forecasting accuracy of structural time series models
applied to continuous
observations of
cyclist
traffic volumes. It
has been shown that the proposed
models can produce accurate
peak period
forecasts of
cyclist
traffic
volumes
at
both 1 hour
and
fifteen
minute resolution
and that the
percentage
errors
are lower for hourly forecasts.
The inclusion of weather metrics
as explanatory variables had varying effects on the forecasting
accuracies of the models.
These results directly aid the design of
traffic signal control systems accommodating cyclists.
Author's Homepage:
http://people.tcd.ie/caulfibhttp://people.tcd.ie/bghosh
Description:
PUBLISHEDQingdao, China
Author: GHOSH, BIDISHA; CAULFIELD, BRIAN
Other Titles:
17th International IEEE Conference on Intelligent Transportation SystemsType of material:
Conference PaperAvailability:
Full text availableKeywords:
TransportSubject (TCD):
Smart & Sustainable Planet , CYCLINGLicences: