Short-Term Traffic Forecasting Using High-Resolution Traffic Data

Abstract

This paper develops a data-driven toolkit for traffic forecasting using high-resolution (a.k.a. event-based) traffic data. This is the raw data obtained from fixed sensors in urban roads. Time series of such raw data exhibit heavy fluctuations from one time step to the next (typically on the order of 0.1-1 second). Short-term forecasts (10-30 seconds into the future) of traffic conditions are critical for traffic operations applications (e.g., adaptive signal control). But traffic forecasting tools in the literature deal predominantly with 3-5 minute aggregated data, where the typical signal cycle is on the order of 2 minutes. This renders such forecasts useless at the operations level. To this end, we model the traffic forecasting problem as a matrix completion problem, where the forecasting inputs are mapped to a higher dimensional space using kernels. The formulation allows us to capture both nonlinear dependencies between forecasting inputs and outputs but also allows us to capture dependencies among the inputs. These dependencies correspond to correlations between different locations in the network. We further employ adaptive boosting to enhance the training accuracy and capture historical patterns in the data. The performance of the proposed methods is verified using high-resolution data obtained from a real-world traffic network in Abu Dhabi, UAE. Our experimental results show that the proposed method outperforms other state-of-the-art algorithms.

Deep Learning for Road Traffic Forecasting: Does it Make a Difference?

“…The extremely efficient learning procedure of these models makes them particularly appropriate for traffic forecasting over large datasets. On the other hand, the high parametric sensitivity of models currently utilized for traffic forecasting has also motivated the renaissance of bagging and boosting tree ensembles for the purpose, which are known to be more robust against the variability of their hyper-parameters and less prone to overfitting [256], [257], [258]. Finally, initial evidences of the applicability of automated machine learning tools for efficiently finding precise traffic forecasting models have been recently reported in [259].…”