Abstract
Short-term traffic prediction is a key component of Intelligent Transportation Systems. It uses historical data to construct models for reliably predicting traffic state at specific locations in road networks in the near future. Despite being a mature field, short-term traffic prediction still poses some open problems related to the choice of optimal data resolution, prediction of nonrecurring congestion, and the modelling of relevant spatiotemporal dependencies. As a step towards addressing these problems, this paper investigates the ability of Artificial Neural Networks, Random Forests, and Support Vector Regression algorithms to reliably model traffic flow at different data resolutions and respond to unexpected traffic incidents. We also explore different feature selection methods to identify and better understand the spatiotemporal attributes that most influence the reliability of these models. Experimental results indicate that data aggregation does not necessarily achieve good performance for multivariate spatiotemporal machine learning models. The models learned using high-resolution 30-second input data outperformed the corresponding baseline ARIMA models by 8%. Furthermore, feature selection based on Recursive Feature Elimination resulted in models that outperformed those based on linear correlation-based feature selection.
Highlights
Traffic congestion results in significant monetary losses in countries around the world, with the cost of traffic congestion in 2014 estimated to be $160 billion in the US alone [1]
Park et al [7] investigated the effect of aggregation on travel time prediction and considered aggregation levels from 2 min to 60 min in the context of an ARIMA model. ey concluded that higher levels of aggregation were required to forecast route travel time than when forecasting link travel times
Vlahogianni and Karlaftis [9] looked at aggregation levels and they found that temporal aggregation may distort critical traffic flow information, they concluded that further research was necessary to determine the optimum aggregation level(s)
Summary
Traffic congestion results in significant monetary losses in countries around the world, with the cost of traffic congestion in 2014 estimated to be $160 billion in the US alone [1]. Intelligent Transportation Systems, Advanced Traffic Management Systems, and route guidance systems use realtime data of traffic flow gathered from various sensors. Recent approaches still use variations of the original ARIMA models, for example, seasonal ARIMA [3, 4], but there has been a shift towards using machine learning algorithms to address the traffic prediction challenges [5]. Ese include building responsive algorithms that are able to predict nonrecurring congestion, determining the optimum data resolution, and identifying and modelling the important spatiotemporal dependencies in traffic data. (iii) Identify the spatiotemporal traffic attributes that most influence the performance of these models and their ability to model the complex dependencies in traffic data We illustrate these contributions using historical data of volume and occupancy measurements on a highway in Auckland (New Zealand).
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