Browsing by Author "Li, Chi-Sen"

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A data mining based approach for travel time prediction in freeway with non-recurrent congestion
(2014-06-10)
Li, Chi-Sen
;
Chen, Mu-Chen
;
運輸與物流管理系註：原交通所+運管所
;
Department of Transportation and Logistics Management
This study integrates three data mining techniques, K-means clustering, decision trees, and neural networks, to predict the travel time of freeway with non-recurrent congestion. By creating dummy variables and identifying important variables, not only is the prediction performance increased without increasing investment in equipment, but also important variables are obtained concerning the important locations of equipment in order to effectively assist public transit agencies with system maintenance. The experimental results for a segment of 36.1 km of National Freeway No. 1, Taiwan, with non-recurrent congestion show that, whether or not the data generated by the Electronic Toll Collection (etc) system is used as input variables, the travel time prediction method developed in this study is able to improve the prediction performance. Meanwhile, the proposed approach also reduces the percentage of samples with mean absolute percentage error (MAPE) > 20%. Furthermore, in this study, important variables are extracted from the decision tree in order to predict the travel time. Finally, the prediction models constructed in accordance with six scenarios are highly accurate due to the low MAPE values, which are from 6% to 9%. (C) 2014 Elsevier B.V. All rights reserved.
Identifying important variables for predicting travel time of freeway with non-recurrent congestion with neural networks
(2013-11-01)
Li, Chi-Sen
;
Chen, Mu-Chen
;
運輸與物流管理系註：原交通所+運管所
;
Department of Transportation and Logistics Management
The provision of long-distance travel time information has been a major factor facilitating the intelligent transportation system to become more successful. Previous studies have pointed out that non-recurrent congestion is the major cause of freeway delay. The long travel distance complicates the characteristics of traffic flow. Hence, how to improve the prediction capability of long-distance travel time in the case of non-recurrent congestion is an important issue that must be overcome in the field of travel time prediction. This study constructs the travel time prediction model for a segment of 36.1 kms (including eight interchanges) in the National Freeway No. 1, Taiwan, by using the multilayer perceptron. To improve the prediction capability of the model in the case of non-recurrent congestion, this study collects data of average spot speed and heavy vehicle volume gathered by dual-loop vehicle detectors, in addition to rainfall and temporal feature. Furthermore, the historical travel time inferred from the original data of electronic toll collection (ETC) system is also used as the input variable, and the actual travel time inferred from ETC is used as the training target to establish a robust prediction model. As suggested by the results of 168 experimental combinations, the most appropriate prediction model established in this study is a highly accurate forecasting model with MAPE of 6.47 %.