Abstract
Integrating Pattern Features to Sequence Model for Traffic Index Prediction
Highlights
Traffic index is a conceptual measure of traffic congestion, with a value between 0 and 10
Mean absolute error (MAE) and mean absolute percentage error (MAPE) of Seq2Seq model are in the middle of long short-term memory (LSTM) model and proposed prediction model with pattern feature (PMPF), and as the prediction period is shortened, the prediction error does not show a big difference, indicating that compared with LSTM, Seq2Seq model can remember the traffic indices for a longer time
PMPF model performed best in MAE and MAPE compared with the LSTM model and Seq2Seq model
Summary
Traffic index is a conceptual measure of traffic congestion, with a value between 0 and 10. As a part of intelligent transportation system, traffic index prediction plays a positive role in promoting the development of intelligent transportation system. The current approaches for traffic index prediction can be roughly divided into two types. The first type is the prediction methods based on specific mathematical model. Guo et al proposed to predict the traffic index by pattern sequence matching, and they used a time attenuation factor based on inverse distance weight to improve the accuracy [4]. Methods based on specific mathematical model are not designed to adapt to various data. They cannot update their model according to the data. When actual data disagree with the model assumption, their performances are limited
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