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A Floating Car Data Application to Estimate the Origin-Destination Car Trips Before and During the COVID-19 Pandemic

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Advanced Information Networking and Applications (AINA 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 451))

Abstract

In March 2020, the World Health Organization declared a global pandemic due to an unprecedented health crisis by COVID-19. In the first stage, all the Countries applied strict policies to limit the spreading of the virus, significantly reducing the mobility trips (reduction over than the 90% for the public transport modes), and possibly by structurally modifying mobility habits of citizens. With respect to the study of how (and how much) mobility habits are changing during the pandemic, the new technologies and the real-time traffic data for monitoring the travel demand can play a significant role, and this research tries to contribute in this sense. Within this issue, the aim of this research was twice: i) verify the applicability of the Floating Car Data (FCD) for the origin-destination (OD) car trips (OD matrices) estimation, proposing an ad-hoc methodology for the scope; ii) estimating and comparing the OD car trips before and during COVID-19 pandemic within the Campania Region in south of Italy, investigating both seasonal and yearly impacts of the pandemic on the mobility habits (e.g. lock-down periods vs. recovery periods; summer vs. winter periods). Estimated results confirm the ability of FCDs to reproduce OD car trips. With respect to the impacts produced by the pandemic on car mobility, the estimation results underline that during the periods of the main mobility restrictions, the structure of the regional demand has significantly changed with respect to a pre-pandemic period: extra-provincial car trips have decreased (between 23% and 42%) than the intra-provincial ones, which have even increased (up to +5%); the distance travelled was reduced up to the 24%.

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References

  1. Bontempi, E.: The Europe second wave of COVID-19 infection and the Italy “strange” situation. Environ. Res. 193, 110476 (2021)

    Article  Google Scholar 

  2. Islam, N., et al.: Physical distancing interventions and incidence of coronavirus disease 2019: natural experiment in 149 countries. BMJ 370, m2743 (2020)

    Article  Google Scholar 

  3. McGrail, D.J., Dai, J., McAndrews, K.M., Kalluri, R.: Enacting national social distancing policies corresponds with dramatic reduction in COVID19 infection rates. PLoS ONE 15(7), e0236619 (2020)

    Article  Google Scholar 

  4. Fang, H., Wang, L., Yang, Y.: Human mobility restrictions and the spread of the novel coronavirus (2019-ncov) in China. National Bureau of Economic Research (2020)

    Google Scholar 

  5. Muller, S.A., Balmer, M., Neumann, A., Nagel, K.: Mobility traces and spreading of COVID-19. MedRxiv (2020)

    Google Scholar 

  6. Cartenì, A., Di Francesco, L., Martino, M.: How mobility habits influenced the spread of the COVID-19 pandemic: results from the Italian case study. Sci. Total Environ. 741, 140489 (2020)

    Article  Google Scholar 

  7. Cartenì, A., Di Francesco, L., Martino, M.: The role of transport accessibility within the spread of the coronavirus pandemic in Italy. Saf. Sci. 133, 104999 (2021B)

    Article  Google Scholar 

  8. Cartenì, A., Di Francesco, L., Henke, I., Marino, T.V., Falanga, A.: The role of public transport during the second covid-19 wave in Italy. Sustainability 13(21), 11905 (2021)

    Article  Google Scholar 

  9. Lee, H., et al.: The relationship between trends in COVID-19 prevalence and traffic levels in South Korea. Int. J. Infect. Dis. 96, 399–407 (2020)

    Article  Google Scholar 

  10. Kraemer, M.U., et al.: The effect of human mobility and control measures on the COVID-19 epidemic in China. Science 368(6490), 493–497 (2020)

    Article  Google Scholar 

  11. ISFORT, Istituto Superiore di Formazione e Ricerca per i Trasporti. 17° Rapporto sulla mobilità degli italiani (2020)

    Google Scholar 

  12. Abu-Rayash, A., Dincer, I.: Analysis of mobility trends during the COVID-19 coronavirus pandemic: exploring the impacts on global aviation and travel in selected cities. Energy Res. Soc. Sci. 68, 101693 (2020)

    Article  Google Scholar 

  13. Long, J.A., Ren, C.: Associations between mobility and socio-economic indicators vary across the timeline of the Covid-19 pandemic. Comput. Environ. Urban Syst. 91, 101710 (2022)

    Article  Google Scholar 

  14. Cartenì, A.: Updating demand vectors using traffic counts on congested networks: a real case application. WIT Trans. Built Environ. 96, 211–221 (2007)

    Article  Google Scholar 

  15. Carteni’, A.: A cost-benefit analysis based on the carbon footprint derived from plug-in hybrid electric buses for urban public transport services. WSEAS Trans. Environ. Develop. 14, 125–135 (2018)

    Google Scholar 

  16. Cartenì, A.: Urban sustainable mobility. Part 1: rationality in transport planning. Transp. Probl. 9(4), 39–48 (2014)

    Google Scholar 

  17. Cartenì, A.: Accessibility indicators for freight transport terminals. Arab. J. Sci. Eng. 39(11), 7647–7660 (2014B)

    Article  MathSciNet  Google Scholar 

  18. Cascetta, E., Cartenì, A., Henke, I.: Stations quality, aesthetics and attractiveness of rail transport: empirical evidence and mathematical models [Qualità delle stazioni, estetica e attrattività del trasporto ferroviario: evidenze empiriche e modelli matematici]. Ingegneria Ferroviaria 69(4), 307–324 (2014)

    Google Scholar 

  19. D’Acierno, L., Botte, M.: A passenger-oriented optimization model for implementing energy-saving strategies in railway contexts. Energies 11(11), 2946 (2018)

    Article  Google Scholar 

  20. D’Acierno, L., Gallo, M., Montella, B., Placido, A.: Analysis of the interaction between travel demand and rail capacity constraints. WIT Trans. Built Environ. 128, 197–207 (2012)

    Article  Google Scholar 

  21. Altintasi, O., Tuydes-Yaman, H., Tuncay, K.: Detection of urban traffic patterns from Floating Car Data (FCD). Transp. Res. Procedia 22, 382–391 (2017)

    Article  Google Scholar 

  22. Kong, X., Xu, Z., Shen, G., Wang, J., Yang, Q., Zhang, B.: Urban traffic congestion estimation and prediction based on floating car trajectory data. Fut. Gener. Comput. Syst. 61, 97–107 (2016)

    Article  Google Scholar 

  23. Nuzzolo, A., Comi, A., Polimeni, A.: Urban freight vehicle flows: an analysis of freight delivery patterns through floating car data. Transp. Res. Procedia 47, 409–416 (2020)

    Article  Google Scholar 

  24. Ruppe, S., Junghans, M., Haberjahn, M., Troppenz, C.: Augmenting the floating car data approach by dynamic indirect traffic detection. Procedia Soc. Behav. Sci. 48, 1525–1534 (2012)

    Article  Google Scholar 

  25. Rahmani, M., Jenelius, E., Koutsopoulos, H.N.: Non-parametric estimation of route travel time distributions from low-frequency floating car data. Transp. Res. Part C Emerg. Technol. 58, 343–362 (2015)

    Article  Google Scholar 

  26. Shi, C., Chen, B.Y., Li, Q.: Estimation of travel time distributions in urban road networks using low-frequency floating car data. ISPRS Int. J. Geo Inf. 6(8), 253 (2017)

    Article  Google Scholar 

  27. Rahmani, M., Koutsopoulos, H.N., Jenelius, E.: Travel time estimation from sparse floating car data with consistent path inference: a fixed point approach. Transp. Res. Part C Emerg. Technol. 85, 628–643 (2017)

    Article  Google Scholar 

  28. Kong, X., et al.: Mobility dataset generation for vehicular social networks based on floating car data. IEEE Trans. Veh. Technol. 67(5), 3874–3886 (2018)

    Article  Google Scholar 

  29. Chen, Y., Chen, C., Wu, Q., Ma, J., Zhang, G., Milton, J.: Spatial-temporal traffic congestion identification and correlation extraction using floating car data. J. Intel. Transp. Syst. 25(3), 263–280 (2021)

    Article  Google Scholar 

  30. Erdelić, T., Carić, T., Erdelić, M., Tišljarić, L., Turković, A., Jelušić, N.: Estimating congestion zones and travel time indexes based on the floating car data. Comput. Environ. Urban Syst. 87, 101604 (2021)

    Article  Google Scholar 

  31. Nigro, M., Cipriani, E., del Giudice, A.: Exploiting floating car data for time-dependent origin–destination matrices estimation. J. Intel. Transp. Syst. 22(2), 159–174 (2018)

    Article  Google Scholar 

  32. Jahnke, M., Ding, L., Karja, K., Wang, S.: Identifying origin/destination hotspots in floating car data for visual analysis of traveling behavior. In: Gartner, G., Huang, H. (eds.) Progress in Location-Based Services 2016. Lecture Notes in Geoinformation and Cartography. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-47289-8_13

  33. Botte, M., Pariota, L., D’Acierno, L., Bifulco, G.N.: An overview of cooperative driving in the European Union: policies and practices. Electronics 8(6), 1–25 (2019)

    Article  Google Scholar 

  34. Cartenì, A.: The acceptability value of autonomous vehicles: a quantitative analysis of the willingness to pay for shared autonomous vehicles (SAVs) mobility services. Transp. Res. Interdisc. Perspect. 8, 100224 (2020)

    Google Scholar 

  35. Cartenì, A.: A new look in designing sustainable city logistics road pricing schemes. WIT Trans. Ecol. Environ. 223, 171–181 (2017)

    Article  Google Scholar 

  36. Cartenì, A.: Urban sustainable mobility. Part 2: Simulation models and impacts estimation. Transp. Probl. 10(1), 5–16 (2015)

    Article  Google Scholar 

  37. Carteni, A., Henke, I.: External costs estimation in a cost-benefit analysis: the new Formia-Gaeta tourist railway line in Italy. In: 2017 17th IEEE International Conference on Environment and Electrical Engineering and 2017 1st IEEE Industrial and Commercial Power Systems Europe, EEEIC/I and CPS Europe 2017 (2017). Art. no. 7977614

    Google Scholar 

  38. Henke, I., Cartenì, A., Molitierno, C., Errico, A.: Decision-making in the transport sector: a sustainable evaluation method for road infrastructure. Sustainability 12(3), 764 (2020)

    Article  Google Scholar 

  39. D’Acierno, L., Gallo, M., Montella, B., Placido A.: The definition of a model framework for managing rail systems in the case of breakdowns. In: Proceedings of 16th International IEEE Annual Conference on Intelligent Transportation Systems, IEEE ITSC 2013, The Hague, The Netherlands, 2013 October, pp. 1059–1064 (2013). Art. no. 6728372

    Google Scholar 

  40. Cantelmo, G., Viti, F.: A big data demand estimation model for urban congested networks. Transp. Telecommun. 21(4), 245–254 (2020)

    Google Scholar 

  41. Croce, A.I., Musolino, G., Rindone, C., Vitetta, A.: Estimation of travel demand models with limited information: floating car data for parameters’ calibration. Sustain. (Switz.) 13(16), 8838 (2021)

    Article  Google Scholar 

  42. Mitra, A., Attanasi, A., Meschini, L., Gentile, G.: Methodology for O-D matrix estimation using the revealed paths of floating car data on large-scale networks. IET Intell. Transp. Syst. 14(12), 1704–1711 (2020)

    Article  Google Scholar 

  43. Cascetta, E.: Transportation Systems Analysis. Springer, Boston (2009). https://doi.org/10.1007/978-0-387-75857-2

    Book  MATH  Google Scholar 

  44. ISTAT (2021). [WWW Document]. https://www.istat.it/it/archivio/222527. Accessed 2 Feb 2020

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Acknowledgments

Research carried out within the funding program VALERE: VAnviteLli pEr la RicErca; SEND research project, University of Campania “Luigi Vanvitelli”, Italy and VEM Solutions S.p.A. within the company Viasat Group, Italy.

Author information

Authors and Affiliations

  1. Department of Engineering, University of Campania “Luigi Vanvitelli”, Via Roma 29, 81031, Aversa, Caserta, Italy

    Armando Cartenì, Luigi Di Francesco & Antonella Falanga

  2. Department of Civil, Architectural and Environmental Engineering, University of Naples “Federico II”, 80125, Napoli, Italy

    Ilaria Henke

  3. Department of Agriculture, University of Naples, 80055, Naples, Italy

    Assunta Errico

  4. VEM Solutions S.p.A., Venaria Reale, Italy

    Mario Bellotti, Fabiola Filardo & Giuseppe Cutrupi

Authors
  1. Armando Cartenì
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  2. Ilaria Henke
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  3. Assunta Errico
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  4. Luigi Di Francesco
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  5. Antonella Falanga
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  6. Mario Bellotti
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  7. Fabiola Filardo
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  8. Giuseppe Cutrupi
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Corresponding author

Correspondence to Ilaria Henke .

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Editors and Affiliations

  1. Department of Information and Communication Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. University of Technology Sydney, Sydney, NSW, Australia

    Farookh Hussain

  3. Faculty of Bussiness Administration, Rissho University, Tokyo, Japan

    Tomoya Enokido

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Cartenì, A. et al. (2022). A Floating Car Data Application to Estimate the Origin-Destination Car Trips Before and During the COVID-19 Pandemic. In: Barolli, L., Hussain, F., Enokido, T. (eds) Advanced Information Networking and Applications. AINA 2022. Lecture Notes in Networks and Systems, vol 451. Springer, Cham. https://doi.org/10.1007/978-3-030-99619-2_60

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