Browsing by Author "Yang, Jiann-Shiou"

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    Development of an Innovative Prototype Lane Departure Warning System
    (Intelligent Transportation Systems Institute, Center for Transportation Studies, University of Minnesota, 2013-03) Yang, Jiann-Shiou
    Development of various techniques such as lane departure warning (LDW) systems can improve traffic safety significantly. An LDW system should be able to detect when the driver is in danger of departing the road and then trigger an alarm to warn the driver early enough to take corrective action. This report presents the development of a new prototype LDW system. It is mainly an image-based approach to find the vehicle's lateral characteristics and then uses that information to establish an operation algorithm to determine whether a warning signal should be issued based on the status of the vehicle deviating from its heading lane. The system developed takes a mixed approach by integrating the Lucas-Kanade (L-K) optical flow and the Hough transform-based lane detection methods in its implementation. The L-K point tracking is used when the lane boundaries cannot be detected, while the lane detection technique is used when they become available. Even though both techniques are used in the system, only one method is activated at any given time because each technique has its own advantages and also disadvantages. The developed LDW system was road tested on I-35, US-53, Rice Lake Road, Martin Road, and Jean Duluth Road. Overall, the system operates correctly as expected, with a false alarm occurring only roughly 1.18% of the operation time. This report presents the system implementation together with findings. Factors that could affect the system performance are also discussed.
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    Duluth Entertainment Convention Center (DECC) Special Events Traffic Flow Study
    (2003-06-01) Yang, Jiann-Shiou
    Following special events at the Duluth Entertainment Convention Center (DECC) (e.g., conventions, concerts, graduation ceremonies), high volumes of traffic exiting the DECC create substantial congestion at adjacent intersections. The purpose of this research is to develop an improved signal timing control plan for the high volume traffic movements associated with DECC special events so that progression through the downtown Duluth and I-35 is as efficient as possible. Our research mainly contains the following four parts: (1) identify the project study area which includes adjacent and key intersections near the DECC; (2) collect traffic data at the selected intersections; (3) develop an efficient signal timing control to improve after event traffic flow; and (4) perform an evaluation study using both the existing and the newly developed timing plans. We use a practical approach for signal timing control that eliminates the need of using a traffic dynamic model to optimize the intersection split times following DECC special events. Our approach is based on neural networks (NNs) with the weight estimation (i.e., the training process) via the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm.
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    Duluth Entertainment Convention Center (DECC) Special Events Traffic Flow Study Phase II: Mobility Monitoring and Performance Measure via Dynamic Travel Time Prediction
    (2005-08-01) Yang, Jiann-Shiou
    Researchers tested the use of an on-board Global Positioning System (GPS) to collect travel-time data after special events at the Duluth Entertainment Convention Center in Duluth, Minnesota. The report also studies travel-time prediction via the Kalman filter algorithm which provides estimates of existing values, predicts future values of prescribed variables and improves estimates of earlier variables.

    The study was conducted to assist Mn/DOT District One and the City of Duluth Traffic Service Center in the performance monitoring, planning and management of the traffic flow following special events at the Duluth Entertainment Convention Center (DECC). The report focuses on travel-time predictions on the arterial roads adjacent to the DECC. To project travel times that are both accurate and timely, the authors combined the use of test vehicles equipped with an on-board GPS and the application of the Kalman filtering technique to the resulting data. The Kalman filtering technique is a set of mathematical equations that provides an efficient computational (recursive) means of estimating the state of a process in a way that minimizes the mean of the squared error.

    The integration of these collection and assessment tools has the potential for providing valuable travel-time information to motorists making route choices.

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    Estimation of Vehicle's Lateral Position via the Lucas-Kanade Optical Flow Method
    (Intelligent Transportation Systems Institute, Center for Transportation Studies, University of Minnesota, 2012-09) Yang, Jiann-Shiou
    The use of rumble strips on roads has proven to be an effective means of providing drivers lane departure warning (LDW). However, rumble strips require an infrastructure and do not exist on a majority of roadways. Furthermore, rumble strips present a difficult issue of where to establish the rumble-strip distance threshold. To develop an effective virtual rumble-strip LDW system where the rumble-strip threshold is allowed to vary according to the risk of the vehicle departing the road, it is essential to know the vehicle’s lateral characteristics; in particular, the vehicle’s lateral position and speed. In this report, we use image processing via an in-vehicle camera to estimate the vehicle’s lateral position and speed. The lateral position is estimated by determining the vehicle’s heading angle via a homography and the Lucas-Kanade optical flow techniques; while the lateral speed is determined via the heading angle and the vehicle’s On Board Diagnostic (OBD)-II forward speed data access. The detail of our approach is presented in this report together with our findings. Our approach will only need the minimal set of information to characterize the vehicle lateral characteristics, and therefore, makes it more feasible in a vehicle application.
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    A Nonlinear State Space Approach to Arterial Travel Time Prediction
    (2006-02-01) Yang, Jiann-Shiou
    The study uses time series and the Kalman prediction techniques along with modern technology such as the Global Positioning System (GPS) for accurate data collection and analysis. A greater understanding of travel time will help facilitate traffic system performance monitoring, control, planning, and informed route decisions for motorists accessing information from changeable message sings (CMS). The models used for estimations include the autoregressive integrated moving average (ARIMA) and the autoregressive moving average (ARMA). The study collects travel data for the peak hours of travel (3:30-5:00 p.m.) over an eight-month period on the busiest section of Highway 194 in Duluth, Minnesota. The predictions were conducted over two weeks during the summer of 2005. Observed and predicted travel times are charted carefully and report evaluations determine the success of the study.