| Summary: | 碩士 === 國立臺灣大學 === 土木工程學研究所 === 105 === Taiwan Boulevard is one of the most important roads in Taichung City. Due to the large number of short and medium range trips, there is often a large of traffic flow during commute hours or weekends, which leads to congestion in urban roads. Therefore, with the coming era of car-to-car connection , it will be more and more important to develop real-time travel time prediction model. With the model, people who driving on the road can choose the right path to reduce their traffic time and divert traffic congestion in the alternative path, thereby improving the quality of road network.
In this study, we collected the Tag information of Taichung City, and established the travel time database for the four-wheeled car from the intersection of Zhongming South road and Taiwan Boulevard to the intersection of Wenxin road and Taiwan Boulevard. In this study, we build the travel time fix model to filter the abnormal data., and the data is divided into training phase information and test phase information.
In this study, the training phase data were set up with the decision tree of Classification and Regression Tree to establish the travel time prediction model, and use the bayesian optimization combined with the cross validation K-fold method. With 30 times 10 fold cross validation, we find out the best parameters to determine the complexity of a reasonable selection model.
The overall predictive result in the early morning session in the MAPE evaluation criteria can reach high precision prediction or very close to high precision prediction. Regardless of weekdays or holidays, the prediction performance is within the range of good predictions in the MAPE evaluation criteria.
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