A Traffic State-Based Model for Freeway Travel Time Prediction

• Main Authors: Chiu, Meng-Yu, 邱孟佑
• Other Authors: Wong, Jinn-Tsai
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/96946337879201582163

博士 === 國立交通大學 === 交通運輸研究所 === 99 === Traditionally, travel time estimation and prediction in a Traffic Management Center is mostly based on the data obtained from loop and/or image detectors. A prediction model solely based on these data, however, is difficult to consider the dynamic transformation and delay of traffic flow. To partially resolve this issue, this paper proposes a novel travel time prediction framework with the capability to predict inter-ramp travel time at a satisfactory level of prediction performance. First, historical traffic data collected by each loop detector were classified into different traffic states. For each state, regression techniques were then applied to build up a travel time prediction model. And then the travel time of vehicles passing Electronic Toll Collect (ETC) booths was considered to adjust the predicted traffic states and link travel time. The results showed satisfactory performance of the proposed models. More importantly, the estimated traffic parameters could provide system managers with fruitful information about how travel time is increased by different road geometry and traffic characteristics. Consequently, effective control strategies could be devised. Therefore, missing values is an inevitable issue in actual operations. Mean and moving average values based on historical data are common choices to replace missing values in past studies, which does not consider the features of vehicle flow continuation and lagging. The other purpose of this study is proposes a hybrid imputation strategy which, based on data mining techniques, a decision tree was then established using Classification And Regression Tree (CART) to connect each detect point to the adjacent detectors and the ETC travel time on the associated road section. When missing data were imputed based on the developed CART model. The empirical study showed that CART imputation method based on traffic state works effectively to impute data with missing values, especially under the circumstance of long-period data missing. Moreover, it was found that hybrid imputation strategies varied in different missing time-windows circumstances fit better into real time and various traffic conditions. Finally, due to more sophisticated probe car data collection technology, which have the disadvantages: High transmission capacity, data filtering, map data matching complex procedures and so on. To resolve this problem, a Virtual Vehicle Detector (VVD) system with a method to set up VVD on road network is proposed. The VVD mechanism that verified by the field testing and simulation results is feasible.