Jianyang Zheng

Extracting Roadway Background Image: Mode-Based Approach

Traffic monitoring cameras are widely installed on streets and freeways in U.S. metropolitan areas. Video images captured from these video cameras can be used to extract many valuable traffic parameters through video image processing. A popular way to capture traffic data is to compare the current traffic images with the background image, which contains no vehicles or other moving objects, just background such as pavement. Once the moving vehicle images are separated from the background image, measurements of their number, speed, and so on can be obtained. Typically, background images are extracted from a video stream through image processing because it may be hard to find a frame without any vehicles for normal traffic streams on urban streets. This paper introduces a new method that can quickly extract the background image from traffic video streams for both freeways and intersections in a variety of prevailing traffic conditions. This method has been tested with field data, and the results are promising.

Model-Free Video Detection and Tracking of Pedestrians and Bicyclists

Pedestrian and bicycle monitoring is quickly becoming an avid area of interest as information regarding pedestrian and bicycle flow is needed not only for developing competent access to particular urban corridors and trails, but also for system optimization scenarios, such as transit system operations and intersection controls. In this paper, the authors present a simple, yet effective method for tracking pedestrian and bicycle objects in a relatively large surveillance area, using ordinary uncalibrated video images. Object extraction is accomplished via background subtraction, while tracking is accomplished through an inherent characteristic cost function. Composite objects are used as a means of dealing with occlusions. The algorithm is implemented using Microsoft Visual C# and was tested on numerous scenes of varying complexity, resulting in an average count rate of 92.7% at the specified checkpoints.

Quantitative evaluation of GPS performance under forest canopies

There is an increasing demand for use of the Global Positioning System (GPS) to navigate or track objects in the forest. However, objects near a GPS receiver antenna, such as tree leaves and branches, can reflect GPS signals and result in large position errors. Canopies in the forest will also block satellite signals and cause the GPS receiver to stop updating data. This is of practical significance for evaluating the performance of GPS in the forest environment. A field test was conducted to understand how large the position errors are and how long the position updates may be deferred under different levels of canopy densities. A digital camera was used to record the canopies over the test site. Image processing techniques, especially Otsus algorithm, were used and the canopy density was classified into three levels. The ANOVA was used to analyze the effect of canopy density on the GPS position errors. The result shows that the GPS position errors are significantly different under different canopy density levels. The GPS data-update frequency was also analyzed, and the result indicates that the scheduled position update intervals are lengthened due to the existence of forest canopies.

Using Precise Time Offset to Improve Freeway Vehicle Delay Estimates

Traffic congestion is getting worse and has resulted in increased travel delays and costs. In order to develop effective intelligent transportation systems (ITS) strategies to mitigate traffic congestion on freeways, a good understanding of its causes and impacts is vital but has not been achieved at a satisfactory level. Over the past several decades, deterministic queuing theory (DQT) has been widely used to evaluate freeway travel delays resulted from traffic congestion. However, several studies evaluated the accuracy of its delay estimates and claimed that the DQT method consistently underestimates vehicle delays. The reason for the underestimation, however, had not been clearly identified. This study aims at exploring the main cause of such underestimation problems and proposing a solution to fix it. Based on theoretical analysis and empirical justification, it was found the underestimation resulted primarily from the inappropriate estimates of the time offsets, that is, the travel times between the queue starting point and the immediate upstream and downstream traffic sensor locations. To address this issue, a microscopic approach was developed and implemented in a computer application to enhance the time offset estimation. This proposed approach was tested using the real vehicle delay data manually extracted from traffic surveillance video cameras. The test results indicated that the improved DQT-based vehicle delay estimates with appropriate time offset settings were very close to the ground-truth data. The underestimation problem associated with the traditional DQT method can be effectively addressed and fairly accurate estimates of vehicle delay can be achieved by the proposed method.

Numerical Examinations of Traffic Accident Characteristics Using Analytical Statistical Methods

This study applied analytical statistical approaches to investigate some characteristics of traffic accidents. The regression analysis of the injury severities of accidents, and the discrete choice model of different time periods of day when accidents likely occur have been developed. The Poisson regression and Logit model were used by considering the occurrence mechanism of accidents on freeways. Using the observed accident data in Washington State, the accident injury severity model was successfully estimated using a Passion regression. Three variables were found significant in the model. The findings of this study were encouraging. In the studies of Logit model the probability of occurrence of an accident in the different time periods of day including day, dawn, evening and night was expressed by the four utility functions. Both traffic flow and freeway characteristics were included in the model. Compared with most existing models, the new findings were obtained to describe some accidents characteristics.