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Arterial Road incident detection Based on Time-Moving Average Method in Bluetooth-Based Wireless Vehicle Reidentification System

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JOURNAL OF TRANSPORTATION ENGINEERING 2015年第3期141卷 04014084-04014084页

作者： Yu, Wooyeon Park, SeJoon Kim, David S. Ko, Sung-Seok Myongji Univ Dept Ind & Management Engn Yongin 449728 Gyeonggi Do South Korea Oregon State Univ Sch Mech Ind & Mfg Engn Corvallis OR 97331 USA Konkuk Univ Dept Ind Engn Seoul 142701 South Korea

incident detection algorithms, which are an essential part of traffic management systems, have been studied for several decades, but the research focus has primarily been on algorithms for incident detection on freeways and other free-flowing roads. When applied on arterial roads, the achievement of stable performance and scalability are major challenges when developing an effective incident detection algorithm. In this research, the authors propose an incident detection algorithm that utilizes travel time and traffic volume samples generated from a Bluetooth-based wireless vehicle reidentification system that has been implemented on arterial roads. The proposed algorithm is based on a moving average over time, which can recognize sample travel time and traffic volume patterns resulting from incidents. The use of a moving average overcomes limitations resulting from sparse travel time sample data collected. Within the algorithm, a threshold strategy is applied that makes the algorithm easy to implement and transfer, which is an important requirement for practitioners. The proposed algorithm is evaluated using reported accident data and the insight of two traffic engineers, and provides a good balance between detection rate and false-alarm rate.

关键词： incident detection algorithm Time moving average Bluetooth-based wireless vehicle reidentification system Arterial road

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detection-delay-based freeway incident detection algorithms

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2003年第3-4期11卷 265-287页

作者： Teng, HL Qi, Y Polytech Univ Dept Civil Engn Urban ITS Ctr Brooklyn NY 11201 USA

Even though incident detection algorithms are designed and implemented for quickly detecting incidents, the criterion of mean detection delay has hardly been well defined and utilized in developing and evaluating incident detection algorithms. In addition, most incident detection algorithms do not have an optimal property in terms of detection delay with respect to false alarm rate. In the study presented in this paper, the incident detection problem was formulated as an optimization problem. To implement the algorithm, called the CUSUM algorithm that was derived from the optimization formulation of the incident detection problem, a simplified procedure was developed. Based on this procedure, three varieties of the CUSUM algorithm were developed and tested based on real incident data against a newly defined criterion for mean detection delay. Selected incident detection algorithms were also compared with the CUSUM algorithms. The comparison demonstrates the superiority of the CUSUM algorithms against other selected algorithms in reducing detection delay while maintaining an acceptable detection rate. (C) 2003 Elsevier Ltd. All rights reserved.

关键词： freeway incident management incident detection algorithm wavelet application alforithm evaluation neural network models

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Application of wavelet technique to freeway incident detection

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2003年第3-4期11卷 289-308页

作者： Teng, HL Qi, Y Polytech Univ Urban ITS Ctr Brooklyn NY 11201 USA

This paper presents an application of the wavelet technique to freeway incident detection because wavelet techniques have demonstrated superior performance in detecting changes in signals in electrical engineering. Unlike the existing wavelet incident detection algorithm, where the wavelet technique is utilized to denoise data before the data is input into an algorithm, this paper presents a different approach in the application of the wavelet technique to incident detection. In this approach, the features that are extracted from traffic measurements by using wavelet transformation are directly utilized in detecting changes in traffic flow. It is shown in the paper that the extracted features from traffic measurements in incident conditions are significantly different from those in normal conditions. This characteristic of the wavelet technique was used in developing the wavelet incident detection algorithm in this study. The algorithm was evaluated in comparison with the multi-layer feed-forward neural network, probabilistic neural network, radial basis function neural network, California and low-pass filtering algorithms. The test results indicate that the wavelet incident detection algorithm performs better than other algorithms, demonstrating its potential for practical application. (C) 2003 Elsevier Ltd. All rights reserved.

关键词： freeway incident management incident detection algorithm wavelet application algorithm evaluation neural network models

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Application of a dynamic model for arterial street incident detection

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ITS JOURNAL 1999年第1期5卷 53-70页

作者： Lee, JT Taylor, WC Michigan State Univ Dept Civil Engn E Lansing MI 48824 USA

Existing freeway and signalized arterial street incident detection algorithms were investigated to determine their merit for use on urban arterial streets. Based on this investigation, a Kalman filtering algorithm was modified to recursively filter and update aggregate traffic flow and speed data to estimate true values. A test using measured arterial street data at a signalized intersection shows good tracking ability on these traffic variables over time. A test using data from an incident on an arterial street also confirmed that this algorithm has good potential for arterial street incident detection.

关键词： incident detection algorithm Kalman filter urban arterial streets ITS recursive estimation real time operation

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