The Estimation of Road Passenger Travel Time by Decision Tree

• Main Authors: Chih-Wei Sung, 宋志偉
• Other Authors: Jia-Sheng Heh
• Format: Others
• Language: zh-TW
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/37469107567616003301

碩士 === 中原大學 === 資訊工程研究所 === 101 === The travel times of public buses are variant due to different traffic situations, as a result, the schedules listed on the bus stop are seldom accurate and can not fulfill the need of the passengers. In this research, we build a travel time estimation model based on hours, car types, driving behaviors, distances between bus stops, to improve the accuracy of travel time estimation. The data used in this research are collected by GPS probing buses which send their traveling records back to the server. The data are collected from March to April, 2012 in the area of Hsin-Chu County, and the route is from Zhu-Dong to Hsin-Chu. The total number of records is 383,473 with 192,757 records in March and 190,716 records in April. We used the records in March as the training data set and clustered the travel time and waiting time into several clusters. We used the clustering results, together with car types, drivers, weekdays, hours, arrival time, depart time, to build decision tree models, according to four different cluster numbers: 4 clusters (fast, medium fast, medium slow, slow), 3 clusters (fast, medium, slow), 2 clusters (fast, slow), and 1 cluster (medium, i.e. no clustering is performed). We used the records in April as the testing data set to test the models, and applied the standard deviation of percentage error method to evaluate the accuracies of the models. By applying the above mentioned methods, the SDPE of the 4 models of each stops and each hours are calculated and we choose the best models and calculate their percentage. The experiment results indicate that, after comparing the percentage of the estimations which is under 20% SDPE of each models, the percentages of best models of 1-cluster to 4-cluster are 23.97%, 43.83%, 16.99%, 15.21% respectively. It shows that 2-cluster model is the best one, mostly. In conclusion, we choose these best models as the recommended travel time estimation method.