Prediction Hourly Bording Demand Of Bus Passenger Using Imbalance Record

Abstract

The tap-on smart-card data provides a
valuable source to learn passengers’ boarding
behaviour and predict future travel demand.
However, when examining the smart-card records
(or instances) by the time of day and by boarding
stops, the positive instances (i.e. boarding at a
specific bus stop at a specific time) are rare
compared to negative instances (not boarding at
that bus stop at that time). Imbalanced data has
been demonstrated to significantly reduce the
accuracy of machine learning models deployed for
predicting hourly boarding numbers from a
particular location. This paper addresses this data
imbalance issue in the smart-card data before
applying it to predict bus boarding demand. We
propose the deep generative adversarial nets (Deep-
GAN) to generate dummy travelling instances to
add to a synthetic training dataset with more
balanced travelling and non-travelling instances.
The synthetic dataset is then used to train a deep
neural network (DNN) for predicting the travelling
and non-travelling instances from a particular stop
in a given time window. The results show that
addressing the data imbalance issue can
significantly improve the predictive model’s
performance and better fit ridership’s actual profile.
Comparing the performance of the Deep-GAN with
other traditional resampling methods shows that the
proposed method can produce a synthetic training
dataset with a higher similarity and diversity and,
thus, a stronger prediction power. The paper
highlights the significance and provides practical
guidance in improving the data quality and model
performance on travel behaviour prediction and
individual travel behaviour analysis.