Skip to main content
SpringerLink
Log in

TAQE: A Data Modeling Framework for Traffic and Air Quality Applications in Smart Cities

  • Conference paper
  • First Online:
Graph-Based Representation and Reasoning (ICCS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13403))

Included in the following conference series:

Abstract

Air quality and traffic monitoring and prediction are critical problems in urban areas. Therefore, in the context of smart cities, many relevant conceptual models and ontologies have already been proposed. However, the lack of standardized solutions boost development costs and hinder data integration between different cities and with other application domains. This paper proposes a classification of existing models and ontologies related to Earth observation and modeling and smart cities in four levels of abstraction, which range from completely general-purpose frameworks to application-specific solutions. Based on such classification and requirements extracted from a comprehensive set of state-of-the-art applications, TAQE, a new data modeling framework for air quality and traffic data is defined. The effectiveness of TAQE is evaluated both by comparing its expressiveness with the state-of-the-art of the same application domain and by its application in the “TRAFAIR – Understanding traffic flows to improve air quality” EU project.

This research was funded by the TRAFAIR project (2017-EU-IA-0167), co-financed by the Connecting Europe Facility of the European Union.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 44.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 59.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    W3C RDF Prime: http://www.w3.org/TR/2004/REC-rdf-primer-20040210/.

  2. 2.

    W3C RDF Schema: http://www.w3.org/TR/rdf-schema/.

  3. 3.

    W3C OWL 2: https://www.w3.org/TR/owl2-overview/.

  4. 4.

    PROV Data Model: https://www.w3.org/TR/prov-dm/.

  5. 5.

    Real-time Air Quality Index: https://waqi.info/.

  6. 6.

    Air quality statistics by EEA: https://www.eea.europa.eu/data-and-maps/dashboards/air-quality-statistics.

  7. 7.

    http://vocab.linkeddata.es/datosabiertos/def/transporte/trafico.

  8. 8.

    open511 specification: http://www.open511.org/.

  9. 9.

    Road Accident Ontology: https://www.w3.org/2012/06/rao.html.

  10. 10.

    OGC Simple Feature Access: https://www.opengeospatial.org/standards/sfa.

  11. 11.

    OGC Coverage Implementation Schema: http://docs.opengeospatial.org/is/09-146r6/09-146r6.html.

References

  1. Bachechi, C., Desimoni, F., Po, L., Casas, D.M.: Visual analytics for spatio-temporal air quality data. In: 24th International Conference on Information Visualisation, IV 2020, Melbourne, Australia, 7–11 September 2020, pp. 460–466. IEEE (2020)

    Google Scholar 

  2. Bachechi, C., Po, L.: Implementing an urban dynamic traffic model. In: Barnaghi, P.M., Gottlob, G., Manolopoulos, Y., Tzouramanis, T., Vakali, A. (eds.) 2019 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2019, Thessaloniki, Greece, 14–17 October 2019, pp. 312–316. ACM (2019)

    Google Scholar 

  3. Bachechi, C., Po, L.: Traffic analysis in a smart city. In: Proceedings - 2019 IEEE/WIC/ACM International Conference on Web Intelligence Workshops, WI 2019 Companion, pp. 275–282. Association for Computing Machinery Inc. (2019)

    Google Scholar 

  4. Bachechi, C., Po, L., Rollo, F.: Big data analytics and visualization in traffic monitoring. Big Data Res. 27, 100292 (2022)

    Article  Google Scholar 

  5. Bachechi, C., Rollo, F., Desimoni, F., Po, L.: Using real sensors data to calibrate a traffic model for the city of Modena. Adv. Intell. Syst. Comput. 1131(AISC), 468–473 (2020)

    Google Scholar 

  6. Bachechi, C., Rollo, F., Po, L.: Real-time data cleaning in traffic sensor networks. In: 17th IEEE/ACS International Conference on Computer Systems and Applications, AICCSA 2020, Antalya, Turkey, 2–5 November 2020, pp. 1–8. IEEE (2020)

    Google Scholar 

  7. Bachechi, C., Rollo, F., Po, L.: Detection and classification of sensor anomalies for simulating urban traffic scenarios. Cluster Comput., 1–25 (2021). https://doi.org/10.1007/s10586-021-03445-7

  8. Bigi, A., Veratti, G., Fabbi, S., Po, L., Ghermandi, G.: Forecast of the impact by local emissions at an urban micro scale by the combination of Lagrangian modelling and low cost sensing technology: the TRAFAIR project. In: 19th International Conference on Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes, Harmo 2019 (2019)

    Google Scholar 

  9. Bizer, C., Heath, T., Berners-Lee, T.: Linked data - the story so far. Int. J. Semant. Web Inf. Syst. 5(3), 1–22 (2009)

    Article  Google Scholar 

  10. Compton, M., et al.: The SSN ontology of the W3C semantic sensor network incubator group. Web Semant. Sci. Serv. Agents World Wide Web 17, 25–32 (2012)

    Google Scholar 

  11. Cox, S.: ISO 19156:2011 - Geographic Information - Observations and Measurements. International Organization for Standardization, January 2011

    Google Scholar 

  12. Desimoni, F., Ilarri, S., Po, L., Rollo, F., Trillo-Lado, R.: Semantic traffic sensor data: the TRAFAIR experience. Appl. Sci. (Switzerland) 10(17), 5882 (2020)

    Google Scholar 

  13. Djahel, S., Doolan, R., Muntean, G.-M., Murphy, J.: A communications-oriented perspective on traffic management systems for smart cities: challenges & innovative approaches. IEEE Commun. Surv. Tutorials 17(1), 125–151 (2015)

    Article  Google Scholar 

  14. Dombalyan, A., Kocherga, V., Semchugova, E., Negrov, N.: Traffic forecasting model for a road section. Transp. Res. Procedia 20, 159–165. 12th International Conference on Organization and Traffic Safety Management in large cities, SPbOTSIC-2016, 28–30 September 2016, St. Petersburg, Russia (2017)

    Google Scholar 

  15. Elloumi, M., Dhaou, R., Escrig, B., Idoudi, H., Saidane, L.A.: Monitoring road traffic with a UAV-based system. In: 2018 IEEE Wireless Communications and Networking Conference (WCNC), pp. 1–6 (2018)

    Google Scholar 

  16. Fernandez, S., Hadfi, R., Ito, T., Marsa-Maestre, I., Velasco, J.: Ontology-based architecture for intelligent transportation systems using a traffic sensor network. Sensors 16(8), 1287 (2016)

    Article  Google Scholar 

  17. Galárraga, L., Mathiassen, K.A.M., Hose, K.: QBOAirbase: the European air quality database as an RDF cube. In: International Semantic Web Conference (2017)

    Google Scholar 

  18. Gomez-Perez, A., Fernández-López, M., Corcho, O.: Ontological Engineering: with Examples from the Areas of Knowledge Management, E-Commerce and the Semantic Web. Springer, London, January 2004. https://doi.org/10.1007/b97353

  19. Hou, Y., Edara, P., Sun, C.: Traffic flow forecasting for urban work zones. IEEE Trans. Intell. Transp. Syst. 16(4), 1761–1770 (2015)

    Article  Google Scholar 

  20. Ilarri, S., Wolfson, O., Delot, T.: Collaborative sensing for urban transportation. IEEE Data Eng. Bull. 37(4), 3–14 (2014)

    Google Scholar 

  21. Janowicz, K., Haller, A., Cox, S.J., Phuoc, D.L., Lefrançois, M.: SOSA: a lightweight ontology for sensors, observations, samples, and actuators. J. Web Seman. 56, 1–10 (2019)

    Article  Google Scholar 

  22. Kaivonen, S., Ngai, E.C.-H.: Real-time air pollution monitoring with sensors on city bus. Digit. Commun. Netw. 6(1), 23–30 (2020)

    Article  Google Scholar 

  23. Martin, R.V.: Satellite remote sensing of surface air quality. Atmos. Environ. 42(34), 7823–7843 (2008)

    Article  Google Scholar 

  24. Martín, J., Khatib, E.J., Lázaro, P., Barco, R.: Traffic monitoring via mobile device location. Sensors 19(20), 4505 (2019)

    Article  Google Scholar 

  25. Métral, C., Falquet, G., Karatzas, K.D.: Ontologies for the integration of air quality models and 3D city models. ArXiv, abs/1201.6511 (2012)

    Google Scholar 

  26. Nesi, P., Po, L., Viqueira, J.R.R., Trillo-Lado, R.: An integrated smart city platform. In: Szymański, J., Velegrakis, Y. (eds.) IKC 2017. LNCS, vol. 10546, pp. 171–176. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-74497-1_17

  27. Oprea, M.M.: Air_POLLUTION_ONTO: an ontology for air pollution analysis and control. In: Iliadis, M., Tsoumakasis, V., Bramer (eds.) Artificial Intelligence Applications and Innovations III, pp. 135–143. Springer, Boston (2009). https://doi.org/10.1007/978-1-4419-0221-4_17

  28. Po, L., Rollo, F., Bachechi, C., Corni, A.: From sensors data to urban traffic flow analysis. In: 2019 IEEE International Smart Cities Conference, ISC2 2019, Casablanca, Morocco, 14–17 October 2019, pp. 478–485. IEEE (2019)

    Google Scholar 

  29. Po, L., et al.: TRAFAIR: understanding traffic flow to improve air quality. In: 2019 IEEE International Smart Cities Conference, ISC2, pp. 36–43 (2019)

    Google Scholar 

  30. Regueiro, M.A., Viqueira, J.R., Stasch, C., Taboada, J.A.: Semantic mediation of observation datasets through sensor observation services. Future Gener. Comput. Syst. 67, 47–56 (2017)

    Article  Google Scholar 

  31. Regueiro, M.A., Viqueira, J.R., Taboada, J.A., Cotos, J.M.: Virtual integration of sensor observation data. Comput. Geosci. 81, 12–19 (2015)

    Article  Google Scholar 

  32. Riga, M., Moumtzidou, A., Vrochidis, S., Kompatsiaris, I., Syropoulou, P.: D4.2: semantic integration and reasoning of environmental data. Technical report, CERTH (2017)

    Google Scholar 

  33. Rohi, G., Ejofodomi, O., Ofualagba, G.: Autonomous monitoring, analysis, and countering of air pollution using environmental drones. Heliyon 6, e03252 (2020)

    Article  Google Scholar 

  34. Rollo, F., Po, L.: SenseBoard: sensor monitoring for air quality experts. In: Costa, C., Pitoura, E. (eds.) Proceedings of the Workshops of the EDBT/ICDT 2021 Joint Conference, Nicosia, Cyprus, 23 March 2021, vol. 2841. CEUR Workshop Proceedings. CEUR-WS.org (2021)

    Google Scholar 

  35. Rollo, F., Sudharsan, B., Po, L., Breslin, J.G.: Air quality sensor network data acquisition, cleaning, visualization, and analytics: a real-world IoT use case. In: Doryab, A., Lv, Q., Beigl, M. (eds.) UbiComp/ISWC 2021: 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and 2021 ACM International Symposium on Wearable Computers, Virtual Event, 21–25 September 2021, pp. 67–68. ACM (2021)

    Google Scholar 

  36. Sharif, A., Li, J., Khalil, M., Kumar, R., Sharif, M.I., Sharif, A.: Internet of things–smart traffic management system for smart cities using big data analytics. In: 14th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP 2017). IEEE, December 2017

    Google Scholar 

Download references

Acknowledgement

This research was supported by the TRAFAIR project (2017-EU-IA-0167), co-financed by the Connecting Europe Facility of the European Union, and by the NEAT-Ambience project (Next-gEnerATion dAta Management to foster suitable Behaviors and the resilience of cItizens against modErN ChallEnges PID2020-113037RB-I00 / AEI / 10.13039/501100011033). We thank reviewers who provided insight to improve the final version of this paper.

Author information

Authors and Affiliations

  1. CiTIUS, University of Santiago de Compostela, Santiago de Compostela, Spain

    David Martínez & José R. R. Viqueira

  2. University of Modena and Reggio Emilia, Modena, Italy

    Laura Po

  3. I3A, University of Zaragoza, Zaragoza, Spain

    Raquel Trillo-Lado

Authors
  1. David Martínez
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Laura Po
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Raquel Trillo-Lado
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. José R. R. Viqueira
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Laura Po .

Editor information

Editors and Affiliations

  1. University of Münster, Münster, Germany

    Tanya Braun

  2. Babes-Bolyai University, Cluj-Napoca, Romania

    Diana Cristea

  3. Humboldt-Universität zu Berlin, Berlin, Germany

    Robert Jäschke

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Martínez, D., Po, L., Trillo-Lado, R., Viqueira, J.R.R. (2022). TAQE: A Data Modeling Framework for Traffic and Air Quality Applications in Smart Cities. In: Braun, T., Cristea, D., Jäschke, R. (eds) Graph-Based Representation and Reasoning. ICCS 2022. Lecture Notes in Computer Science(), vol 13403. Springer, Cham. https://doi.org/10.1007/978-3-031-16663-1_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-16663-1_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-16662-4

  • Online ISBN: 978-3-031-16663-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation