Performance and Challenges in Utilizing Non-Intrusive Sensors for Traffic Data Collection

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Advances in Remote Sensing

ISSN Print: 2169-267X
ISSN Online: 2169-2688
www.scirp.org/journal/ars
E-mail: ars@scirp.org

Google-based Impact Factor: 1.5
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"Performance and Challenges in Utilizing Non-Intrusive Sensors for Traffic Data Collection"
written by Xin Yu, Panos D. Prevedouros,
published by Advances in Remote Sensing, Vol.2 No.2, 2013

has been cited by the following article(s):

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[19]	Prediction of Road Traffic using Naive Bayes Algorithm
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[20]	Role of Data Fusion Prediction of Road Traffic Speed
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[22]	Characterizing Driving Behavior by Using LIDAR and GPS Through Traffic Stream Models
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[26]	Evaluation of Accuracy of the Infra-Red Traffic Logger Under Mix Traffic Condition
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[27]	Developing an algorithm to assess the rear-end collision risk under fog conditions using real-time data
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[31]	Crowdsourced traffic information in traffic management: Evaluation of traffic information from Waze
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[32]	An Improved Fuzzy Neural Network for Traffic Speed Prediction Considering Periodic Characteristic
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[33]	Short-Term Speed Prediction Using Remote Microwave Sensor Data: Machine Learning versus Statistical Model
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[34]	Information-Aided Smart Schemes for Vehicle Flow Detection Enhancements of Traffic Microwave Radar Detectors
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[35]	Virtual Reality (VR)–A Better View for Visualizing the Flats
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[36]	MOŽNOST UVEDBE SODOBNIH TEHNOLOGIJ V IZREDNO ŠTETJE PROMETA
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[37]	Data Mining of Social Media for Traffic Monitoring
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[38]	Long short-term memory neural network for traffic speed prediction using remote microwave sensor data
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[39]	The Study of Vehicle Classification Equipment with Solutions to Improve Accuracy in Oklahoma
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[40]	Vehicle classification accuracy of AVC and WIM sites in Oklahoma
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[41]	Road Traffic Congestion Monitoring in Social Media with Hinge-Loss Markov Random Fields
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[44]	The study of vehicle classification equipment with solutions to improve accuracy in Oklahoma.
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[4]	An improved tucker decomposition‐based imputation method for recovering lane‐level missing values in traffic data IET Intelligent Transport Systems, 2022 DOI:10.1049/itr2.12148

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[12]	Data Fusion for Multi-Source Sensors Using GA-PSO-BP Neural Network IEEE Transactions on Intelligent Transportation Systems, 2021 DOI:10.1109/TITS.2020.3010296

[13]	A Novel Hybrid Model for Predicting Traffic Flow via Improved Ensemble Learning Combined with Deep Belief Networks Mathematical Problems in Engineering, 2021 DOI:10.1155/2021/7328056

[14]	Freeway Short-Term Travel Speed Prediction Based on Data Collection Time-Horizons: A Fast Forest Quantile Regression Approach Sustainability, 2020 DOI:10.3390/su12020646

[15]	Evaluation of Short-Term Freeway Speed Prediction Based on Periodic Analysis Using Statistical Models and Machine Learning Models Journal of Advanced Transportation, 2020 DOI:10.1155/2020/9628957

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[18]	Short-term prediction of lane-level traffic speeds: A fusion deep learning model Transportation Research Part C: Emerging Technologies, 2019 DOI:10.1016/j.trc.2019.07.003

[19]	Evaluation of Accuracy of the Infra-Red Traffic Logger Under Mix Traffic Condition 2019 Moratuwa Engineering Research Conference (MERCon), 2019 DOI:10.1109/MERCon.2019.8818936

[20]	Developing an algorithm to assess the rear-end collision risk under fog conditions using real-time data Transportation Research Part C: Emerging Technologies, 2018 DOI:10.1016/j.trc.2017.12.012

[21]	On the Deployment and Noise Filtering of Vehicular Radar Application for Detection Enhancement in Roads and Tunnels Sensors, 2018 DOI:10.3390/s18030837

[22]	Road Traffic Speed Prediction: A Probabilistic Model Fusing Multi-Source Data IEEE Transactions on Knowledge and Data Engineering, 2018 DOI:10.1109/TKDE.2017.2718525

[23]	An Improved Fuzzy Neural Network for Traffic Speed Prediction Considering Periodic Characteristic IEEE Transactions on Intelligent Transportation Systems, 2017 DOI:10.1109/TITS.2016.2643005

[24]	Short-Term Speed Prediction Using Remote Microwave Sensor Data: Machine Learning versus Statistical Model Mathematical Problems in Engineering, 2016 DOI:10.1155/2016/9236156

[25]	Information-Aided Smart Schemes for Vehicle Flow Detection Enhancements of Traffic Microwave Radar Detectors Applied Sciences, 2016 DOI:10.3390/app6070196

[26]	Long short-term memory neural network for traffic speed prediction using remote microwave sensor data Transportation Research Part C: Emerging Technologies, 2015 DOI:10.1016/j.trc.2015.03.014

[27]	Road Traffic Congestion Monitoring in Social Media with Hinge-Loss Markov Random Fields 2014 IEEE International Conference on Data Mining, 2014 DOI:10.1109/ICDM.2014.139

[28]	Vehicle classification accuracy of AVC and WIM sites in Oklahoma 2014 IEEE Intelligent Vehicles Symposium Proceedings, 2014 DOI:10.1109/IVS.2014.6856438

Follow SCIRP

	+1 323-425-8868
	customer@scirp.org
	+86 18163351462(WhatsApp)
	1655362766

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