access icon free Integrated framework for real-time urban network travel time prediction on sparse probe data

© The Institution of Engineering and Technology
Received 30/03/2017, Accepted 08/10/2017, Revised 11/09/2017, Published 10/10/2017

The study presents the methodology and system architecture of an integrated urban road network travel time prediction framework based on low-frequency probe vehicle data. Intended applications include real-time network traffic management, vehicle routing and information provision. The framework integrates methods for receiving a stream of probe vehicle data, map matching and path inference, link travel time estimation, calibration of prediction model parameters and network travel time prediction in real time. The system design satisfies three crucial aspects: computational efficiency of prediction, internal consistency between components and robustness against noisy and missing data. Prediction is based on a multivariate hybrid method of probabilistic principal component analysis, which captures global correlation patterns between links and time intervals, and local smoothing, which considers local correlations among neighbouring links. Computational experiments for the road network of Stockholm, Sweden and probe data from taxis show that the system provides high accuracy for both peak and off-peak traffic conditions. The computational efficiency of the framework makes it capable of real-time prediction for large-scale networks. For links with large speed variations between days, prediction significantly outperforms the historical mean. Furthermore, prediction is reliable also for links with high proportions of missing data.

Inspec keywords: data handling; principal component analysis; vehicle routing; probability; intelligent transportation systems

Other keywords: vehicle routing; multivariate hybrid method; link travel time estimation; real-time network traffic management; prediction model parameter calibration; probe vehicle data; map matching; integrated urban road network travel time prediction; local smoothing; sparse probe data; missing data; noisy data; information provision; Sweden; Stockholm; real-time urban network travel time prediction; probabilistic principal component analysis; path inference; global correlation patterns; time intervals; low-frequency probe vehicle data

Subjects: Traffic engineering computing; Data handling techniques; Other topics in statistics

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