Feature Engineering for Data-driven Traffic State Forecast in Urban Road Networks

• URL: http://arxiv.org/abs/2009.08354v1
• Date: Thu, 17 Sep 2020 15:03:33 GMT
• Title: Feature Engineering for Data-driven Traffic State Forecast in Urban Road Networks
• Authors: Felix Rempe, Klaus Bogenberger
• Abstract summary: For longer-term forecasts the traffic state of more distant links or regions of the network are expected to provide valuable information for a data-driven algorithm. This paper studies these expectations of using a clustering network algorithm and one year of Floating Car (FCD) collected by a large fleet of vehicles.
• Score: 1.370633147306388
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Most traffic state forecast algorithms when applied to urban road networks consider only the links in close proximity to the target location. However, for longer-term forecasts also the traffic state of more distant links or regions of the network are expected to provide valuable information for a data-driven algorithm. This paper studies these expectations of using a network clustering algorithm and one year of Floating Car (FCD) collected by a large fleet of vehicles. First, a clustering algorithm is applied to the data in order to extract congestion-prone regions in the Munich city network. The level of congestion inside these clusters is analyzed with the help of statistical tools. Clear spatio-temporal congestion patterns and correlations between the clustered regions are identified. These correlations are integrated into a K- Nearest Neighbors (KNN) travel time prediction algorithm. In a comparison with other approaches, this method achieves the best results. The statistical results and the performance of the KNN predictor indicate that the consideration of the network-wide traffic is a valuable feature for predictors and a promising way to develop more accurate algorithms in the future.

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