Low-Cost and Data Anonymised City Traffic Flow Data Collection to Support Intelligent Traffic System

Journal article


Handscombe, J. and Yu, H. 2019. Low-Cost and Data Anonymised City Traffic Flow Data Collection to Support Intelligent Traffic System. Sensors. 19 (2), p. 347. https://doi.org/10.3390/s19020347
AuthorsHandscombe, J. and Yu, H.
Abstract

There are many methods of collecting traffic flow data, especially using smart phone apps. However, few current solutions balance the need for collecting full route data whilst respecting privacy and remaining low-cost. This project looks into the creation of a wireless sensor network (WSN) that can balance these requirements in an attempt to negate some of the concerns that come with this type of technology. Our proposed system only collects location data within a defined city area. This data is collected with a randomized identifier, which limits repeated identification of the source vehicle and its occupants. Data collected is shared between vehicle and roadside base stations when the two are in range. To deal with the fluid nature of this scenario, a purposely designed Media Access Control (MAC) protocol was designed and implemented using the beacon-slotted ALOHA (Advocates of Linux Open-source Hawaii Association) mechanism.

Keywordstraffic flow; big data; media access control; MAC
Year2019
JournalSensors
Journal citation19 (2), p. 347
PublisherMDPI
ISSN1424-8220
Digital Object Identifier (DOI)https://doi.org/10.3390/s19020347
Web address (URL)http://dx.doi.org/10.3390/s19020347
Output statusPublished
Publication dates16 Jan 2019
Publication process dates
Accepted13 Jan 2019
Deposited24 Jun 2022
Permalink -

https://repository.derby.ac.uk/item/97561/low-cost-and-data-anonymised-city-traffic-flow-data-collection-to-support-intelligent-traffic-system

  • 22
    total views
  • 0
    total downloads
  • 0
    views this month
  • 0
    downloads this month

Export as

Related outputs

Explainable DCNN Decision Framework for Breast Lesion Classification from Ultrasound Images Based on Cancer Characteristics
AlZoubi, A., Eskandari, A., Yu, H. and Du, H. 2024. Explainable DCNN Decision Framework for Breast Lesion Classification from Ultrasound Images Based on Cancer Characteristics . Bioengineering. 11 (5), pp. 1-23. https://doi.org/10.3390/bioengineering11050453
Deep Recognition of Chinese Herbal Medicines Based on a Caputo Fractional Order Convolutional Neural Network
Tao Li, Jiawei Yang, Chenxi Li, Lulu Lv, Kang Liu, Zhipeng Yuan, Youyong Li, Hongqing Yu and Yu, H. 2024. Deep Recognition of Chinese Herbal Medicines Based on a Caputo Fractional Order Convolutional Neural Network. International Workshop on Internet of Things of Big Data for Healthcare. Springer. https://doi.org/10.1007/978-3-031-52216-1_4
Evaluation of Integrated XAI Frameworks for Explaining Disease Prediction Models in Healthcare
Yu, H., Adebola Alaba and Ebere Eziefuna 2024. Evaluation of Integrated XAI Frameworks for Explaining Disease Prediction Models in Healthcare. International Workshop on Internet of Things of Big Data for Healthcare. Springer. https://doi.org/10.1007/978-3-031-52216-1_2
Attention Enhanced Siamese Neural Network for Face Validation
Yu, H. 2023. Attention Enhanced Siamese Neural Network for Face Validation. Artificial Intelligence and Applications. 2 (1), pp. 21-27. https://doi.org/10.47852/bonviewAIA32021018
IoTBDH-2023: The 5th International Workshop on Internet of Things of Big Data for Healthcare
Qi, J., Yu, H., Yang, P., Yang, Y. and Pang, Z. 2023. IoTBDH-2023: The 5th International Workshop on Internet of Things of Big Data for Healthcare. 32nd ACM International Conference on Information and Knowledge Management (CIKM’23), Birmingham, UK. ACM. https://doi.org/10.1145/3583780.3615299
AIMS: An Automatic Semantic Machine Learning Microservice Framework to Support Biomedical and Bioengineering Research
Yu, H., O'Neill, S. and Kermanizadeh, A. 2023. AIMS: An Automatic Semantic Machine Learning Microservice Framework to Support Biomedical and Bioengineering Research. Bioengineering. 10 (10), pp. 1-18. https://doi.org/10.3390/bioengineering10101134
Learning Disease Causality Knowledge from Web of Health Data
Yu, H. and Reiff-Marganiec, S. 2022. Learning Disease Causality Knowledge from Web of Health Data. International journal on semantic web and information systems. 18 (1), pp. 1-19. https://doi.org/10.4018/IJSWIS.297145
Experimental Disease Prediction Research on Combining Natural Language Processing and Machine Learning
Yu, H. 2020. Experimental Disease Prediction Research on Combining Natural Language Processing and Machine Learning. IEEE 7th International Conference on Computer Science and Network Technology (ICCSNT). IEEE Xplore. https://doi.org/10.1109/iccsnt47585.2019.8962507
Dynamic Causality Knowledge Graph Generation for Supporting the Chatbot Healthcare System
Yu, H. 2020. Dynamic Causality Knowledge Graph Generation for Supporting the Chatbot Healthcare System. in: Arai, Kohei, Kapoor, Supriya and Bhatia, Rahul (ed.) Proceedings of the Future Technologies Conference (FTC) 2020, Volume 3 New York Springer.
Semantic Lifting and Reasoning on the Personalised Activity Big Data Repository for Healthcare Research
Yu, H. and Dong, F. 2019. Semantic Lifting and Reasoning on the Personalised Activity Big Data Repository for Healthcare Research. International Journal of Web Engineering and Technology. 14 (2), pp. 103 - 121.
Mining Symptom and Disease Web Data with NLP and Open Linked Data
Yu, H. 2019. Mining Symptom and Disease Web Data with NLP and Open Linked Data. 5th World Congress on Electrical Engineering and Computer Systems and Sciences (EECSS’19) Lisbon, Portugal – August, 2019. https://doi.org/10.11159/mvml19.108
A linear logic approach to the composition of RESTful web services
Zhao, X., Liu, E., Yu, H. and Clapworthy, G.J. 2015. A linear logic approach to the composition of RESTful web services. International Journal of Web Engineering and Technology. 10 (3), pp. 245-271. https://doi.org/10.1504/ijwet.2015.072348
Socio-semantic Integration of Educational Resources - the Case of the mEducator Project
Dietze, Stefan, Kaldoudi, Eleni, Dovrolis, Nikolas, Giordano, Daniela, Spampinato, Concetto, Hendrix, Maurice, Protopsaltis, Aristidis, Taibi, v and Yu, H. 2013. Socio-semantic Integration of Educational Resources - the Case of the mEducator Project. Journal of Universal Computer Science. 19 (11), pp. 1-27. https://doi.org/10.3217/jucs-019-11-1543
Interlinking educational resources and the web of data
Dietze, S., Sanchez‐Alonso, S., Ebner, H., Yu, H., Giordano, D., Marenzi, I. and Pereira Nunes, B. 2013. Interlinking educational resources and the web of data. Program. 47 (1). https://doi.org/10.1108/00330331211296312
Using Linked Data to Annotate and Search Educational Video Resources for Supporting Distance Learning
Yu, H., Pedrinaci, C., Dietze, S. and Domingue, J. 2012. Using Linked Data to Annotate and Search Educational Video Resources for Supporting Distance Learning. IEEE Transactions on Learning Technologies. 5 (2), pp. 130-142. https://doi.org/10.1109/tlt.2012.1
An automated approach to Semantic Web Services Mediation
Dietze, S., Gugliotta, A., Domingue, J., Yu, H. and Mrissa, M. 2010. An automated approach to Semantic Web Services Mediation. Service Oriented Computing and Applications. 4, p. 261–275. https://doi.org/10.1007/s11761-010-0070-7