Cheol Oh

Exploring the Relationship Between Data Aggregation and Predictability to Provide Better Predictive Traffic Information

Providing reliable predictive traffic information is a crucial element for successful operation of intelligent transportation systems. However, there are difficulties in providing accurate predictions mainly because of limitations in processing data associated with existing traffic surveillance systems and the lack of suitable prediction techniques. This study examines different aggregation intervals to characterize various levels of traffic dynamic representations and to investigate their effects on prediction accuracy. The relationship between data aggregation and predictability is explored by predicting travel times obtained from the inductive signature-based vehicle reidentification system on the I-405 freeway detector test bed in Irvine, California. For travel time prediction, this study employs three techniques: adaptive exponential smoothing, adaptive autoregressive model using Kalman filtering, and recurrent neural network with genetically optimized parameters. Finally, findings are discussed on s...

Real-time origin-destination (OD) estimation via anonymous vehicle tracking

With the advent of Advanced Transportation Management and Information System (ATMIS), much attention has been paid to the estimation of dynamic or time-variant OD matrices, as development of improved methods for the derivation of OD-based real time traffic information is vital for analysis of transportation systems and various ATMIS strategies involving traveler information systems and route guidance, dynamic traffic assignment, and adaptive traffic signal control, among others. This study performs a systematic simulation investigation of the performance and feasibility of anonymous vehicle tracking in signalized networks using the Paramics simulation model. Previous research experience with vehicle reidentification techniques on single roadway segments is used to investigate the performance obtainable from tracking individual vehicles across multiple detector stations through a network to obtain real-time OD path flow information such as travel time and volume. The findings of this and subsequent studies serve as a logical and necessary precursor to possible field implementation in signalized networks of vehicle reidentification techniques.

Development of Methodology to Design Advanced Traffic Surveillance Systems for Traffic Information Based on Origin–Destination

The innovative feature of this study is first to develop a methodology for designing advanced traffic surveillance systems based on microscopic traffic simulations. The developed methodology makes it possible ultimately to obtain algorithm structures and associated parameter values. The methodology consists of two main simulation experiments. The first experiment includes a parametric evaluation based on Monte Carlo simulation to identify the required performance of the surveillance system. The second experiment involves performing a microscopic traffic simulation with actual surveillance algorithms and synthetic algorithm inputs. Paramics capable of adding various external modeling routines realized by advanced programming interfaces was used for both simulation experiments. As an application of the proposed methodology, an inductive signature-based anonymous vehicle tracking system was designed via origin-destination flow estimation problem. The outcomes of this study can serve as a logical and necessar...