Scalable data-driven short-term traffic prediction

Abstract

Short-term traffic prediction has a lot of potential for traffic management. However, most research has traditionally focused on either traffic models -which do not scale very well to large networks, computationally -or on data-driven methods for freeways, leaving out urban arterials completely. Urban arterials complicate traffic predictions, compared to freeways, because the non-linear effects of traffic are more pronounced on short links and with the presence of more crossings, more modalities and nonnecessarily conservation of flow (parking). In this paper we consider several data-driven methods and their prediction performance for various situations, including both freeways and urban arterials, for prediction horizons from five minutes up to one week. The focus lies on predicting the traffic flow and speed given available measured data on a certain location. Thus challenges regarding data fusion, state estimation, and other methods providing complete temporal and spatial data is not addressed.The methods evaluated include several naive, parametric and non-parametric methods. For the evaluation of the prediction performance several weeks of data of various locations were used. Performance indicators contained the Root Mean Square Error, Mean Absolute Percentage Error and Mean Absolute Error. Because evaluating average performance might ignore the performance for non-regular traffic conditions, the evaluation also focused on non-regular traffic conditions. Especially these conditions are important in practice, because in these situations the need for accurate information is the highest.Real-world applicability of traffic prediction requires not only accurate results, but also an indication of the accuracy for each prediction. Earlier research has mostly ignored this, leaving this up to the intuition of users of these predictions. This paper introduces a simple way to calculate confidence intervals, applicable to any traffic prediction method. For comparing these confidence intervals between different prediction horizons, an error measure for the accuracy of a confidence interval is defined. Two methods, SARIMA (Seasonal Auto-Regressive Integrated Moving Average) and NLM (Neighborhood Link Method), proved to be the best. The results also indicate key features necessary for accurate traffic prediction with data-driven methods. The results also show reasonably accurate confidence intervals, with those intervals able to adapt well to different traffic situations.

Short-Term Forecasting of Off-Street Parking Occupancy

“…For example, Zhu et al applied an ARIMA model with an additional real-time short-term forecasting framework to create a parking guidance system in Nanjing, China, which outperformed both a conventional neural network method and the Markov chain method (14). Friso et al implemented seasonal ARIMA in a short-term traffic prediction case study that, despite its simplicity, obtained more accurate results than more complicated methods like multivariate spatial-temporal ARIMA (15). A major difference between these studies, which are for real-time purposes and predict one-step ahead, and the cuurent study is that the purpose of this research was to gain insights into parking occupancy over a greater time span.…”

Long and Short-Term Bus Arrival Time Prediction With Traffic Density Matrix

“…On the other hand, data-driven methods [12] are exploiting both historical and real-time traffic data to perform analysis with data mining-inspired techniques in order to provide possible predictions for future traffic situations. Here, the decisions are based on data analysis and interpretation [13] and can be used for both short-term and long-term prediction Well-known forecast techniques [14], [15], still widely used today, mainly include time series analysis [16] exponential smoothening [17], Kalman filtering [18], machine learning (SVMs) [19], ANNs (Artificial Neural Networks) [20], or wavelet-based analysis [21].…”