详情

原文传递 Assessment of Data Quality Needs for Use in Transportion Applications.

题名：	Assessment of Data Quality Needs for Use in Transportion Applications.
作者：	Chen, H.; Haghani, A.; Rakha, H.; Sadabadi, K. F.
关键词：	Algorithms; Data Base Management; Forecasting; Highway Corridors; Information Systems; Kalman Filter; Pattern Recognition; Traffic Control; Transportation; Travel Time; Virginia
摘要：	The objective of this project is to investigate the data quality measures and how they are applied to travel time prediction. This project showcases a short-term travel time prediction method that takes into account the data needs of the real-time applications. The objective of this research is to prepare and disseminate accurate short-term (up to 15 minutes ahead) travel time predictions on major highway corridors in the state of Maryland using real-time and archived Bluetooth travel time samples, probe-based INRIX data, and stationary sensor data pooled together in Regional Integrated Transportation Information System (RITIS). In addition the research effort also develops a medium-term travel time prediction algorithm using pattern recognition techniques. The algorithm is used to predict travel times between Richmond and Virginia Beach in the state of Virginia. Unlike previous studies that use travel time as the variable, the traffic state spatiotemporal evolution is used to predict traffic patterns. The approach uses traffic state data for the current day to match with a historical data set to identify similar traffic patterns and predict travel times into the future. The tasks of this study start from data collection and analysis. The raw INRIX data, including data from I-64 and I-264 between Richmond to Virginia Beach for the past three years, are used in this study. Several problems with the raw data are analyzed, including geographically inconsistent sections, irregular time intervals of data collection, and missing data. Subsequently, a travel database is constructed to obtain daily spatiotemporal traffic states in which traffic state information and dynamic travel times are included. A travel time prediction algorithm is developed using speed measurements and which fully utilizes the relationship between traffic state and travel time. INRIX data for the selected 37-mile freeway stretch (Newport News to Virginia Beach) are used to test the proposed algorithm
报告类型：	科技报告