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- W4309374433 abstract "Realtime estimation of travel time is a key traffic parameter for designing and planning for transportation systems, particularly when providing mobility-on-demand (MOD) services. However, the analysis and prediction of travel time can be delayed significantly due to the complexity and huge computational requirements of microsimulation models. Thus, as an alternative solution, we propose a data-driven approach for the efficient and reliable prediction of travel time. Our approach takes advantage of the strengths of SVM and ARIMA for fully capturing the traffic patterns in the traffic data. We introduce a new parameter $kappa$ into the SVM-ARIMA model to adjust the weight of the ARIMA component, which significantly improves the performance. We validate the performance of the proposed approach using data generated from a microsimulation platform. Our experimental results and comparisons with the existing ML-based methods demonstrates the efficacy of the proposed data-driven approach." @default.
- W4309374433 created "2022-11-26" @default.
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- W4309374433 date "2022-10-09" @default.
- W4309374433 modified "2023-10-14" @default.
- W4309374433 title "A Data-driven Approach for Travel Time Prediction and Analysis" @default.
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- W4309374433 doi "https://doi.org/10.1109/smc53654.2022.9945254" @default.
- W4309374433 hasPublicationYear "2022" @default.
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