Efficient and comprehensive forecasting of information is of great importance
to traffic management. Three types of forecasting methods based on irregularly
spaced data—for situations when traffic detectors cannot be installed to generate
regularly spaced data on all roads—are studied in this thesis, namely, the single
segment forecasting method, multi-segment forecasting method and model-based
forecasting method.
The proposed models were tested using Global Positioning System (GPS) data
from 400 Hong Kong taxis collected within a 2-kilometer section on Princess
Margaret Road and Hong Chong Road, approaching the Cross Harbour Tunnel.
The speed limit for the road is 70 km/h. It has flyovers and ramps, with a small
number of merges and diverges. There is no signalized intersection along this road
section. A total of 14 weeks of data were collected, in which the first 12 weeks of
data were used to calibrate the models and the last two weeks of data were used for
validation.
The single-segment forecasting method for irregularly spaced data uses a
neural network to aggregate the predicted speeds from the naive method, simple
exponential smoothing method and Holt’s method, with explicit consideration of
acceleration information. The proposed method shows a great improvement in
accuracy compared with using the individual forecasting method separately. The
acceleration information, which is viewed as an indicator of the phase-transition
effect, is considered to be the main contribution to the improvement.
The multi-segment forecasting method aggregates not only the information
from the current forecasting segment, but also from adjacent segments. It adopts the
same sub-methods as the single-segment forecasting method. The forecasting
results from adjacent segments help to describe the phase-transition effect, so that
the forecasting results from the multi-segment forecasting method are more
accurate than those that are obtained from the single segment forecasting method.
For one-second forecasting length, the correlation coefficient between the forecasts
from the multi-segment forecasting method and observations is 0.9435, which
implies a good consistency between the forecasts and observations.
While the first two methods are based on pure data fitting techniques, the third
method is based on traffic models and is called the model-based forecasting
method. Although the accuracy of the one-second forecasting length of the
model-based method lies between those of the single-segment and multi-segment
forecasting methods, its accuracy outperforms the other two for longer forecasting
steps, which offers a higher potential for practical applications. / published_or_final_version / Civil Engineering / Master / Master of Philosophy
| Identifer | oai:union.ndltd.org:HKU/oai:hub.hku.hk:10722/146142 |
| Date | January 2011 |
| Creators | Ye, Qing, 叶青 |
| Contributors | Wong, SC |
| Publisher | The University of Hong Kong (Pokfulam, Hong Kong) |
| Source Sets | Hong Kong University Theses |
| Language | English |
| Detected Language | English |
| Type | PG_Thesis |
| Source | http://hub.hku.hk/bib/B47250732 |
| Rights | The author retains all proprietary rights, (such as patent rights) and the right to use in future works., Creative Commons: Attribution 3.0 Hong Kong License |
| Relation | HKU Theses Online (HKUTO) |
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