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- W4285182450 abstract "Drivers and traffic system planners require accurate forecasting of future travel times. To alleviate traffic congestion on Taiwan’s Provincial Highway No. 61, this study considers temporal (weekdays, weekends or continuous holidays) and spatial characteristics (road types), and establishes short-term and long-term travel time prediction models. Data pre-processing is accomplished using the Google Maps API and floating car method to verify travel time comparisons, finding that post-processing travel times are reliable, credible and reasonable. This study develops a collaborative intelligent transportation system (CITS) based on 9 different algorithms for the prediction of current and future travel-time. The results show that <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink> <tex-math notation=LaTeX>$k$ </tex-math></inline-formula> NN-R provides the most accurate short-term predictions, with average prediction error within 20 seconds per kilometer. For long-term forecasting, SARIMAX and fbProphet provide the most accurate results for weekday and continuous holiday modes. Travel time prediction can assist traffic management agencies in the timely implementation of appropriate traffic management measures. CITS also provides real-time traffic query and travel time prediction functions to help drivers avoid traffic congestion." @default.
- W4285182450 created "2022-07-14" @default.
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- W4285182450 date "2022-09-01" @default.
- W4285182450 modified "2023-10-15" @default.
- W4285182450 title "Constructing Cooperative Intelligent Transport Systems for Travel Time Prediction With Deep Learning Approaches" @default.
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- W4285182450 doi "https://doi.org/10.1109/tits.2022.3148269" @default.
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