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Education and qualifications (1)

Silesian University of Technology: Gliwice, PL

2019-03-30 to present | PhD (Transport and Aviation Engineering, Department of transport systems, traffic engineering and logistics)
Education
Source: Self-asserted source
Teresa Pamuła

Works (19)

Impact of Traffic Flow Rate on the Accuracy of Short-Term Prediction of Origin-Destination Matrix in Urban Transportation Networks

Remote Sensing
2024-03 | Journal article | Author
DOI: 10.3390/rs16071202
Contributors: Renata Żochowska; Teresa Pamuła
Source: check_circle
Multidisciplinary Digital Publishing Institute

Estimation and prediction of the OD matrix in uncongested urban road network based on traffic flows using deep learning

Engineering Applications of Artificial Intelligence
2023-01 | Journal article | Author
DOI: 10.1016/j.engappai.2022.105550
Part of ISSN: 0952-1976
Contributors: Teresa Pamuła; Renata Żochowska
Source: Self-asserted source
Teresa Pamuła

Prediction of Electric Buses Energy Consumption from Trip Parameters Using Deep Learning

Energies
2022-02 | Journal article | Author
DOI: 10.3390/en15051747
Contributors: Teresa Pamuła; Danuta Pamuła
Source: check_circle
Multidisciplinary Digital Publishing Institute

Electric Buses: A Review of Selected Concepts Solutions and Challenges

Electric Mobility in Public Transport—Driving Towards Cleaner Air
2021 | Book chapter
DOI: 10.1007/978-3-030-67431-1_5
Part of ISBN: 9783030674304
Part of ISBN: 9783030674311
Part of ISSN: 2523-3440
Part of ISSN: 2523-3459
Source: Self-asserted source
Teresa Pamuła

Detection of Safe Passage for Trains at Rail Level Crossings Using Deep Learning

Sensors
2021-09 | Journal article | Author
DOI: 10.3390/s21186281
Contributors: Teresa Pamuła; Wieslaw Pamula
Source: check_circle
Multidisciplinary Digital Publishing Institute

Estimation of the Energy Consumption of Battery Electric Buses for Public Transport Networks Using Real-World Data and Deep Learning

Energies
2020-05 | Journal article | Author
DOI: 10.3390/en13092340
Contributors: Teresa Pamuła; Wieslaw Pamula
Source: check_circle
Multidisciplinary Digital Publishing Institute

Application of deep learning object classifier to improve e-waste collection planning

Waste Management
2020-05-15 | Journal article
DOI: 10.1016/j.wasman.2020.04.041
Part of ISSN: 0956-053X
Source: Self-asserted source
Teresa Pamuła

Junction Traffic Prediction, Using Adjacent Junction Traffic Data, Based on Neural Networks

Advances in Intelligent Systems and Computing
2019 | Book
DOI: 10.1007/978-3-319-99477-2_5
EID:

2-s2.0-85053181990
Contributors: Pamuła, T.; Pamuła, W.
Source: Self-asserted source
Teresa Pamuła via Scopus - Elsevier

Impact of Data Loss for Prediction of Traffic Flow on an Urban Road Using Neural Networks

IEEE Transactions on Intelligent Transportation Systems
2018 | Journal article
DOI: 10.1109/TITS.2018.2836141
EID:

2-s2.0-85047837418
Contributors: Pamula, T.
Source: Self-asserted source
Teresa Pamuła via Scopus - Elsevier

Road Traffic Conditions Classification Based on Multilevel Filtering of Image Content Using Convolutional Neural Networks

IEEE Intelligent Transportation Systems Magazine
2018 | Journal article
DOI: 10.1109/MITS.2018.2842040
EID:

2-s2.0-85048559622
Contributors: Pamula, T.
Source: Self-asserted source
Teresa Pamuła via Scopus - Elsevier

Zastosowanie sztucznych sieci neuronowych do predykcji parametrów i klasyfikacji warunków ruchu drogowego w systemach transportowych

Gliwice : Wydaw. Politechniki Śląskiej,
2018-10-04 | Book
Source: Self-asserted source
Teresa Pamuła

Determination of congestion levels using texture analysis of road traffic images

2017 | Conference paper
DOI: 10.1007/978-3-319-43985-3_5
Source: Self-asserted source
Teresa Pamuła

Neural networks in transportation research-recent applications

Transport Problems
2016 | Journal article
DOI: 10.20858/tp.2016.11.2.3
EID:

2-s2.0-84975087309
Contributors: Pamuła, T.
Source: Self-asserted source
Teresa Pamuła via Scopus - Elsevier

The traffic flow prediction using Bayesian and neural networks

Studies in Systems, Decision and Control
2016 | Book
DOI: 10.1007/978-3-319-19150-8_4
EID:

2-s2.0-85007112454
Contributors: Pamuła, T.; Król, A.
Source: Self-asserted source
Teresa Pamuła via Scopus - Elsevier

CLASSIFICATION OF ROAD TRAFFIC CONDITIONS BASED ON TEXTURE FEATURES OF TRAFFIC IMAGES USING NEURAL NETWORKS

Scientific Journal of Silesian University of Technology. Series Transport
2016-09-01 | Journal article
DOI: 10.20858/sjsutst.2016.92.10
Part of ISSN: 0209-3324
Contributors: Teresa PAMUŁA
Source: Self-asserted source
Teresa Pamuła via Crossref Metadata Search

Segmentation of vehicle detector data for improved k-nearest neighbours-based traffic flow prediction

IET Intelligent Transport Systems
2015 | Journal article
DOI: 10.1049/iet-its.2013.0164
EID:

2-s2.0-84925371880
Contributors: Bernas̈, M.; Płaczek, B.; Porwik, P.; Pamuła, T.
Source: Self-asserted source
Teresa Pamuła via Scopus - Elsevier

Short-Term Traffic Flow Forecasting Method Based on the Data from Video Detectors Using a Neural Network

Communications in Computer and Information Science
2013 | Book
DOI: 10.1007/978-3-642-41647-7_19
EID:

2-s2.0-84904691338
Contributors: Pamuła, T.
Source: Self-asserted source
Teresa Pamuła via Scopus - Elsevier

Classification and prediction of traffic flow based on real data using neural networks

Archives of Transport
2012 | Journal article
DOI: 10.2478/v10174-012-0032-2
EID:

2-s2.0-84881505443
Contributors: Pamula, T.
Source: Self-asserted source
Teresa Pamuła via Scopus - Elsevier

Traffic flow analysis based on the real data using neural networks

Communications in Computer and Information Science
2012 | Book
DOI: 10.1007/978-3-642-34050-5_41
EID:

2-s2.0-84868244484
Contributors: Pamuła, T.
Source: Self-asserted source
Teresa Pamuła via Scopus - Elsevier

Peer review (24 reviews for 9 publications/grants)

Review activity for Algorithms. (2)
Review activity for Applied sciences. (3)
Review activity for Electronics. (1)
Review activity for Energies. (1)
Review activity for Infrastructures. (1)
Review activity for Metals. (1)
Review activity for Remote sensing. (9)
Review activity for Sensors. (3)
Review activity for Sustainability. (3)
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