Reliability and Sharpness in Border Crossing Traffic Interval Prediction

TL;DR

This paper introduces a hybrid neural network model combining Extreme Learning Machine and particle swarm optimization to generate reliable and sharp traffic volume prediction intervals, outperforming traditional models in accuracy and confidence coverage.

Contribution

The paper presents a novel PSO-ELM hybrid model for short-term traffic interval prediction that guarantees reliability and sharpness, with improved confidence coverage over existing methods.

Findings

PSO-ELM models produce reliable, sharp prediction intervals.

Compared to ARMA and Kalman Filter, PSO-ELM achieves better confidence coverage.

PSO-ELM intervals are close to bounds for outliers.

Abstract

Short-term traffic volume prediction models have been extensively studied in the past few decades. However, most of the previous studies only focus on single-value prediction. Considering the uncertain and chaotic nature of the transportation system, an accurate and reliable prediction interval with upper and lower bounds may be better than a single point value for transportation management. In this paper, we introduce a neural network model called Extreme Learning Machine (ELM) for interval prediction of short-term traffic volume and improve it with the heuristic particle swarm optimization algorithm (PSO). The hybrid PSO-ELM model can generate the prediction intervals under different confidence levels and guarantee the quality by minimizing a multi-objective function which considers two criteria reliability and interval sharpness. The PSO-ELM models are built based on an hourly traffic dataset and compared with ARMA and Kalman Filter models. The results show that ARMA models are the worst for all confidence levels, and the PSO-ELM models are comparable with Kalman Filter from the aspects of reliability and narrowness of the intervals, although the parameters of PSO-ELM are fixed once the training is done while Kalman Filter is updated in an online approach. Additionally, only the PSO-ELMs are able to produce intervals with coverage probabilities higher than or equal to the confidence levels. For the points outside of the prediction levels given by PSO-ELMs, they lie very close to the bounds.