Application of Fuzzy Logic and Neural Networks to Automatically Detect Freeway Traffic Incidents

Hsiao, Chien‐Hua; Lin, Chin‐Teng; Cassidy, Michael J.

doi:10.1061/(asce)0733-947x(1994)120:5(753)

Cited by 64 publications

(31 citation statements)

References 12 publications

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“…The artificial intelligence techniques applied in automatic incident detection include neural networks (Ritchie and Cheu, 1993;Cheu and Ritchie, 1995;Stephanedes and Liu, 1995;Dia and Rose, 1997;Abdulhai and Ritchie, 1999;Adeli andSamant, 2000, Srinivasan et al, 2007), fuzzy logic (Chang and Wang, 1994;Lin and Chang, 1998), and a combination of these two techniques (Hsiao et al, 1994;Ishak and Al-Deek, 1998).…”

Section: Related Workmentioning

confidence: 99%

Fuzzy Logic-based Incident Detection System using Loop Detectors Data

Rossi

Gastaldi

Gecchele

et al. 2015

Transportation Research Procedia

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Vehicle loop detectors or other equipment installed on cross-sections are commonly used for monitoring traffic flow conditions on road network. For operational analysis it is crucial to distinguish between low level of service related to oversaturated conditions and generated by extraordinary events as incidents. In case of incident it is fundamental to have a prompt response in order to activate any requested countermeasure, such as rescue activation and traffic detour. This paper introduces a control system which recognizes incidents from vehicle loop detectors data (system control), and identifies the optimal position of loop detectors (system design).The system was developed using fuzzy logic concepts and calibrated using data from micro simulation experiments. Micro simulation approach is justified from the impossibility to get the requested data from on-field observations. The analysis has been focused on a two-way four-lane freeway basic segment; traffic flow variables (Density, Space Mean Speed and Flow Rate) were estimated with reference to the set of consecutive time intervals (one-minute long) belonging to the whole observation time period (3 hours). Simulated data were obtained running the model several times (10 runs) for each traffic volume class adopted in the analysis (1,000, 2,000, 3,000, 3,500 vehicles/hour), with different random number seeds. Calibration dataset was used to determine the knowledge base of each FIS using the open-source software FisPro, and the remaining data (validation dataset) to evaluate the performance of the system. The main finding of the study is that the detection system, despite its simplicity, shows excellent False Alarm Rate and satisfactory Mean Time To Detection

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Section: Related Workmentioning

confidence: 99%

Fuzzy Logic-based Incident Detection System using Loop Detectors Data

Rossi

Gastaldi

Gecchele

et al. 2015

Transportation Research Procedia

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“…And from Dubois's theory [12] , the addition of fuzzy set S 1 (L 1 ,M 1 ,R 1 ) and S 2 (L 2 ,M 2 ,R 2 ) will be:…”

Section: Fuzzy Sets Of Perceived Path Travel Timementioning

confidence: 99%

Application of Fuzzy Logic in Transport Planning

Sarkar¹,

Sahoo²,

Sahoo³

2012

IJSC

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Fuzzy logic is shown to be a very promising mathematical approach for modelling traffic and transportation processes characterized by subjectivity, ambiguity, uncertainty and imprecision. The basic premises of fuzzy logic systems are presented as well as a detailed analysis of fuzzy logic systems developed to solve various traffic and transportation planning problems. Emphasis is put on the importance of fuzzy logic systems as universal approximators in solving traffic and transportation problems. This paper presents an analysis of the results achieved using fuzzy logic to model complex traffic and transportation processes.

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“…Samant and Adeli (2001) show that the performance of a fuzzy neural network algorithm can be improved through preprocessing of data using a wavelet based feature extraction model. In particular, the discrete wavelet transform (DWT) de-noising and feature extraction model proposed by Samant and Adeli (2000) is combined with the fuzzy-neural network approach presented by Hsiao et al (1994). It is shown that substantial improvement can be achieved using the data filtered by DWT.…”

Section: Samant and Adelimentioning

confidence: 99%

Automatic detection of traffic incidents using data obtained from sensors embedded in intelligent freeways

Adeli

2002

Sensor Review

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This paper reviews innovative research done during the past few years on automatic detection of traffic incidents by the author and his associates using data obtained from sensors embedded in intelligent freeways. A multiparadigm intelligent system approach is employed to solve the complicated and chaotic pattern recognition problem using neural networks, fuzzy logic, and wavelets. Waveletbased de-noising and feature extraction techniques are employed to eliminate undesirable fluctuations in observed data from traffic sensors. The result is reliable algorithms with high incident detection and very low false alarm rates. Electronic accessThe research register for this journal is available at

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Application of Fuzzy Logic and Neural Networks to Automatically Detect Freeway Traffic Incidents

Cited by 64 publications

References 12 publications

Fuzzy Logic-based Incident Detection System using Loop Detectors Data

Fuzzy Logic-based Incident Detection System using Loop Detectors Data

Application of Fuzzy Logic in Transport Planning

Automatic detection of traffic incidents using data obtained from sensors embedded in intelligent freeways

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