Bayesian Support Vector Regression for traffic speed prediction with error bars

Abstract

Abstract-Traffic prediction algorithms can help improve the performance of Intelligent Transportation Systems (ITS). To this end, ITS require algorithms with high prediction accuracy. For more robust performance, the traffic systems also require a measure of uncertainty associated with prediction data. Data driven algorithms such as Support Vector Regression (SVR) perform traffic prediction with overall high accuracy. However, they do not provide any information about the associated uncertainty. The prediction error can only be calculated once field data becomes available. Consequently, the applications which use prediction data, remain vulnerable to variations in prediction error. To overcome this issue, we propose Bayesian Support Vector Regression (BSVR). BSVR provides error bars along with the predicted traffic states. We perform sensitivity and specificity analysis to evaluate the efficiency of BSVR in anticipating variations in prediction error. We perform multi-horizon prediction and analyze the performance of BSVR for expressways as well as general road segments.

A Temporal Directed Graph Convolution Network for Traffic Forecasting Using Taxi Trajectory Data

“…In addition to ARIMA-based models, support vector regression (SVR)-based models also have outstanding performance in traffic flow forecasting [20]. For instance, Su et al [21] utilized the incremental support vector regression (ISVR) model to implement the real-time forecasting of traffic flow states, and Gopi et al [22] proposed a Bayesian support vector regression model, which can provide error bars along with predicted traffic states. Besides this, other common machine learning models have also been applied to the task of traffic flow forecasting.…”

Predicting traffic speed in urban transportation subnetworks for multiple horizons

“…For comparison, we will consider support vector regression (SVR), which is commonly used for traffic forecasting [5], [8], [10], [11], [13], [33], [34]. We will train individual SVR models for each link and prediction horizon.…”

“…Traffic prediction is important for many ITS applications such as route guidance, urban traffic control and management, and sustainable mobility [1]- [3]. Consequently, traffic prediction has garnered considerable attention in the field of transportation studies [4]- [13]. However, these studies, mostly focus on the development of separate prediction models for each road segment and prediction horizon.…”

Compression, estimation and prediction models for large road networks

“…• In addition to above mentioned works, the author also contributed to the works published in [81,82,83,84,69,85,86,87,51,82] and [88].…”