Fuzzy Logic-based Incident Detection System using Loop Detectors Data

Rossi, R.; Gastaldi, Massimiliano; Gecchele, Gregorio; Barbaro, Valeria

doi:10.1016/j.trpro.2015.09.076

Cited by 21 publications

(20 citation statements)

References 17 publications

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“…They studied five factors related to highway safety level. Rossi et al [35] studied the RTAs based on real data in Tanzania and simulation data respectively using fuzzy algorithms. These researchers proved that the fuzzy algorithms proved high prediction accuracy for RTAs occurrence in the studied locations.…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis And Modeling of Rural Roads Traffic Safety Data

Gaber

Diab²,

Othman³

et al. 2023

Sohag Engineering Journal

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Traffic accidents threaten human's safety and properties as well as restrict the management of transportation systems. Therefore, traffic accidents prediction models could help stakeholders to understand accident causes toward controlling their effects. This paper aims at developing a prediction model for a rural road in Egypt using actual accident data with survey data for pavement conditions, traffic flow presented as average hourly traffic per lane, speed, minor access, traffic signs conditions and road width. The relationship between accident number and the influencing factors is modeled using fuzzy logic algorithm which deals with nonlinear relationships between parameters. The results show that the developed model produced accurate and stable traffic accident predictions (R 2 =0.79). The results indicated that average accident rate decreases as road width increases where, a reduction in accident rate of about 5.26 % is reached due to road width widening by 3.5 m. The results indicate that road accidents on the studied road can be reduced by 7.38% if the speed is limited to 75 km per hour. About 17.90% reduction is achieved by improving road surface condition. Also results indicate a positive relationship between traffic volume and accidents rate.

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Section: Literature Reviewmentioning

confidence: 99%

Analysis And Modeling of Rural Roads Traffic Safety Data

Gaber

Diab²,

Othman³

et al. 2023

Sohag Engineering Journal

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show abstract

“…Rossi et al [ 22 ] proposed two incident detection algorithms that utilized fuzzy inference systems (FISs). To detect incidents, FIS1 compared the real-time traffic conditions of a roadway section with the normal traffic conditions of an upstream roadway section, and FIS2 compared the traffic conditions of a roadway section with downstream traffic conditions.…”

Section: Introductionmentioning

confidence: 99%

An Automatic Incident Detection Method for a Vehicle-to-Infrastructure Communication Environment: Case Study of Interstate 64 in Missouri

Zhang

Kianfar

2022

Sensors

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Transportation agencies continuously and consistently work to improve the processes and systems for mitigating the impacts of roadway incidents. Such efforts include utilizing emerging technologies to reduce the detection and response time to roadway incidents. Vehicle-to-infrastructure (V2I) communication is an emerging transportation technology that enables communication between a vehicle and the infrastructure. This paper proposes an algorithm that utilizes V2I probe data to automatically detect roadway incidents. A simulation testbed was developed for a segment of Interstate 64 in St. Louis, Missouri to evaluate the performance of the V2I-based automatic incident detection algorithm. The proposed algorithm was assessed during peak and off-peak periods with various incident durations, under several market penetration rates for V2I technology, and with different spatial resolutions for incident detection. The performance of the proposed algorithm was assessed on the basis of the detection rate, time to detect, detection accuracy, and false alarm rate. The performance measures obtained for the V2I-based automatic incident detection algorithm were compared with California #7 algorithm performance measures. The California #7 algorithm is a traditional automatic incident detection algorithm that utilizes traffic sensors data, such as inductive loop detectors, to identify roadway events. The California #7 algorithm was implemented in the Interstate 64 simulation testbed. The case study results indicated that the proposed V2I-based algorithm outperformed the California #7 algorithm. The detection rate for the proposed V2I-based incident detection algorithm was 100% in market penetrations of 50%, 80%, and 100%. However, the California #7 algorithm’s detection rate was 71%.

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“…Sheu [9] used traffic flow and occupancy data of I-880 freeway in Oakland, California, for real-time incident detection using discrete-time, nonlinear, stochastic system modeling. Rossi and Gastaldi [10] used fuzzy logic for incident detection from loop detector data. Zhang and He [11] and Gu and Qian [12] developed a deep learning approach for detecting the incident through social media.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Incident Detection and Capacity Estimation Using Loop Detector Data

Rizvi

Ahmed

Shen

2020

Journal of Advanced Transportation

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Given the fact that the existing literature lacks the real-time estimation of road capacity and incident location using data from inductance loop detectors, a data-driven framework is proposed in this study for real-time incident detection, as well as road capacity and incident location estimation. The proposed algorithm for incident detection is developed based on the variation in traffic flow parameters acquired from inductance loop detectors. Threshold values of speed and occupancy are determined for incident detection based on the PeMS database. The detection of the incident is followed by the real-time road capacity and incident location estimation using a Cell Transmission Model (CTM) based approach. The data of several incidents were downloaded from PeMS and used for the development of the proposed framework presented in this study. Results show that the developed framework detects the incident and estimates the reduced capacity accurately. The location of the incident is estimated with an overall accuracy of 92.5%. The performance of the proposed framework can be further improved by incorporating the effect of the on-ramps, off-ramps, and high-occupancy lanes, as well as by modeling each lane separately.

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Fuzzy Logic-based Incident Detection System using Loop Detectors Data

Cited by 21 publications

References 17 publications

Analysis And Modeling of Rural Roads Traffic Safety Data

Analysis And Modeling of Rural Roads Traffic Safety Data

An Automatic Incident Detection Method for a Vehicle-to-Infrastructure Communication Environment: Case Study of Interstate 64 in Missouri

Real-Time Incident Detection and Capacity Estimation Using Loop Detector Data

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