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- W4310007446 abstract "We propose a data preprocessing method for estimating the travel time of a path and a system model to supplement the data required for Artificial Neural Network (ANN) training. Providing an accurate and reliable estimated travel time from the origin to the destination of a path aids in efficient path planning. An ANN is used to estimate travel time based on historical and real-time traffic data. Machine learning algorithms include the ability to find patterns and infer results based on raw data. The data preprocessing capabilities of this raw data play an important role in getting the best outcomes from training. There are fixed sensor-based approaches and mobile sensor-based approaches to collecting traffic data. However, fixed sensor-based approaches have cost and implementation constraints, and mobile sensor-based approaches require numerous probe vehicles. However, probe vehicles are not everywhere. In addition, in urban areas, it is challenging to estimate the travel time because a long time interval can pass through 2–3 sections. Therefore, we present a model that predicts the fractional velocity of a segment using the last three data points of a set of Global Positioning System (GPS) points within a segment. If there is insufficient data, we present a method to fill the input data of the ANN by copying the previous velocity data. In addition, we propose a system model that divides the training according to the standard deviation and uses all the data as training data through a K-fold cross validation model if the data required for training is insufficient. As a result, it can be seen that the error rate of learning is reduced compared to learning all at once. The outcome enables a slightly more accurate prediction of the travel time of a segment." @default.
- W4310007446 created "2022-11-30" @default.
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- W4310007446 date "2022-10-19" @default.
- W4310007446 modified "2023-09-29" @default.
- W4310007446 title "Data Preprocessing Method for ANN-Based Travel Time Estimation Using Insufficient GPS Data" @default.
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- W4310007446 doi "https://doi.org/10.1109/ictc55196.2022.9952906" @default.
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