Freeway Travel Time Prediction Based on Seamless Spatio-temporal Data Fusion: Case Study of the Freeway in Taiwan

Abstract

Travel time prediction has been the fundamental brick for developing both Advanced Traffic Management Systems (ATMS) and Advanced Traveler Information Systems (ATIS). The problem of travel time prediction is primarily shaped by the data available for model development and the needs from the perspectives of both travelers and operators. A distance-based Electronic Toll Collection (ETC) system over the freeway system in Taiwan has been fully implemented since December 2013, which provides more comprehensive online data acquisition of freeway traffic conditions, thereby enabling the capability to more reliably predict of travel time over freeway segments. Based on the data from both the ETC system and traditional Vehicle Detectors (VDs), this research proposes a travel time prediction approach whose core technique of data fusion seeks to seamlessly capture the spatio-temporal pattern of freeway traffic flows by matching traffic dynamics revealed from the ETC and VD data. Further, prediction models are constructed thereupon, where the Kalman filter is employed for short-term prediction and the Fourier transform for long-term prediction based on the continuous parameterized modeling of spot travel speed. The proposed approach is implemented as an online deployable system using Java, together with real-world data collected from the freeway in Taiwan, for the numerical experiments. The prediction errors are no greater than 10% in most cases, which illustrates the high accuracy of prediction capability of the model. The encouraging results also highlight the benefits of the pre-processing of data and data fusion in improving data quality and applicability.