On Strategies Improving Accuracy of Speed Prediction from Floating Car Data (FCD)

For smart mobility, speed data extracted from Floating Car Data (FCD) plays an important role in speed predictionaccuracy. However, there are reliability issues for commercial FCD due to processing of individual vehicletracking data, and imposed temporal averaging to compress data size. Furthermore, spatial discretizationsignificantly affects the accuracy of the prediction due to uneven segment lengths and highly variable dataavailability in the network. In this study, these issues are examined in detail, and several strategies to improveaverage speed prediction are proposed. An extensive FCD data from a 75-km long corridor is utilized in thecalculations. Firstly, for data reliability, several filters are applied to clean data, then, a robust algorithm is appliedto smoothen the speed data. Secondly, to investigate and reduce prediction errors due to spatial segmentation, anumber of segmentation approaches are developed, and their effects on the average speed prediction are assessed.Finally, several autoregressive prediction models are implemented and a comprehensive comparison of results ispresented.


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Citation Formats
H. Tüydeş Yaman, K. Kocamaz, and K. Tuncay, “On Strategies Improving Accuracy of Speed Prediction from Floating Car Data (FCD),” İstanbul, Türkiye, 2021, vol. 1, Accessed: 00, 2021. [Online]. Available: https://ace2020.org/en/.