Dynamic Queue Length Estimation Using Gps and Lpr Data

作     者：MUHAMMAD YASIR 

作者单位：东南大学 

学位级别：硕士

导师姓名：夏井新

授予年度：2020年

学科分类：08[工学] 082303[工学-交通运输规划与管理] 082302[工学-交通信息工程及控制] 0823[工学-交通运输工程] 

主      题：traffic management queue length estimation GPS DATA LPR DATA 

摘      要：Real-time and accurate queue length information is very imperative in evaluating the performance and to develop adequate queue management systems,especially under the congested condition in an advanced traffic control *** intelligent mobility technologies such as automatic number plate recognition systems and automatic vehicle identification systems for queue length estimation,have received tremendous attention due to their active deployments in recent years.A machine learning algorithm based real-time dynamic queue length estimation was proposed using the GPS and license plate recognition（LPR）*** to the former shock-fitting methods,the proposed method is fully data-driven,robust,and no need for any prior knowledge or assumptions about the shockwave *** this research project,the stop locations of vehicles and 18 representative features of traffic flow characteristics around the vehicles were extracted from GPS and LPR data respectively for the training of the machine learning algorithm *** that,a feature selection has been carried out through extracted features with the help of different feature selection techniques to remove irrelevant or redundant features,which could be harmful or at least have no contribution to the accuracy of the *** on the best-selected features,a Random Forest and Support Vector Regression model had been trained,and then a trained model was used to predict the stop locations of vehicles using the LPR data as *** cyclic lane-based maximum dynamic queue lengths were estimated based on the predicted stop *** proposed method was implemented for thirty-nine lanes in Kunshan city,P.R *** findings and conclusion include:（1）By the feature selection process,the travel time in control delay feature categories had the most significant impacts on the prediction accuracy of the Random Forests *** travel time of the leading vehicle as well as travel time of the labeled vehicle was stro