Short-term traffic speed forecasting based on data recorded at irregular intervals

Abstract

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.