Abstract
The great abundance of multi-sensor traffic data (traditional traffic data sources - loops, cameras and radars accompanied or even replaced by the most recent - Bluetooth detectors, GPS enabled floating car data) although offering the chance to exploit Big Data advantages in traffic planning, management and monitoring, has also opened the debate on data cleaning, fusion and interpretation techniques. The current paper concentrates on floating taxi data in the case of a Greek city, Thessaloniki city, and proposes the use of advanced spatiotemporal dynamics identification techniques among urban road paths for gaining a deep understanding of complex relations among them. The visualizations deriving from the advanced time series analysis proposed (hereinafter referred also as knowledge graphs) facilitate the understanding of the relations and the potential future reactions/outcomes of urban traffic management and calming interventions, enhances communication potentials (useful and consumable by any target group) and therefore add on the acceptability and effectiveness of decision making. The paper concludes in the proposal of an abstract Decision Support System to forecast, predict or potentially preempt any negative outcomes that could come from not looking directly to long datasets.
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Notes
- 1.
The Hellenic Institute of Transport (HIT) is part of the Centre for Research and Technology Hellas (CERTH) which is a non-profit organization that directly reports to the General Secretariat for Research and Technology (GSRT), of the Greek Ministry of Culture, Education and Religious Affairs. http://www.imet.gr/index.php/en/.
References
Cai, L., Zhu, Y.: The challenges of data quality and data quality assessment in the big data era. Data Sci. J. 14, 1–10 (2015). https://doi.org/10.5334/dsj-2015-002
Hall, D.L., McMullen, S.A.H.: Mathematical Techniques in Multisensor Data Fusion. Artech House, Norwood (2004). ISBN 1580533353
Zhang, L., et al.: Visual analytics for the big data era – a comparative review of state-of-the-art commercial systems. In: 2012 IEEE Conference on Visual Analytics Science and Technology (VAST), pp. 173–182 (2012)
Antoniou, C., Balakrishna, R., Koutsopoulos, H.N.: A synthesis of emerging data collection technologies and their impact on traffic management applications. Eur. Transp. Res. Rev. 3, 139–148 (2011). https://doi.org/10.1007/s12544-011-0058-1
Leduc, G.: Road Traffic Data: Collection Methods and Applications. JRC 47967 – Joint Research Centre – Institute for Prospective Technological Studies. Office for Official Publications of the European Communities, Luxembourg (2008)
Myrovali, G., Tsaples, G., Morfoulaki, M., Aifadopoulou, G., Papathanasiou, J.: An interactive learning environment based on system dynamics methodology for sustainable mobility challenges communication & citizens’ engagement. In: Dargam, F., Delias, P., Linden, I., Mareschal, B. (eds.) ICDSST 2018. LNBIP, vol. 313, pp. 88–99. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-90315-6_8
Patire, A.D., Wright, M., Prodhomme, B., Bayen, A.M.: How much GPS data do we need? Transp. Res. Part C 58, 325–342 (2015)
Hall, D.L., Llinas, J.: An introduction to multisensor data fusion. Proc. IEEE 85, 6–23 (1997)
Varshney, P.K.: Multisensor data fusion. Electron. Commun. Eng. J. 9, 245–253 (1997)
Faouzi, N.-E.E., Leung, H., Kurian, A.: Data fusion in intelligent transportation systems: progress and challenges a survey. Inform. Fusion 12, 4–10 (2011). Special Issue on Intelligent Transportation Systems
Ranjan, R., et al.: City data fusion: sensor data fusion in the Internet of Things. Int. J. Distrib. Syst. Technol. 7(1), 15–36 (2016)
Qing, O.: Fusing Heterogeneous Traffic Data: Parsimonious Approaches Using Data-Data Consistency. T2011/5, TRAIL Thesis Series, The Netherlands (2011)
Khaleghi, B., Khamis, A., Karray, F.O., Razavi, S.N.: Multisensor data fusion: a review of the state-of-the-art. Inf. Fusion 14(1), 28–44 (2013)
Mitsakis, E., Stamos, I., Salanova Grau, J.M., Chrysochoou, E., Iordanopoulos, P., Aifadopoulou, G.: Urban mobility indicators for Thessaloniki. J. Traffic Logistics Eng. 1(2), 148–152 (2013)
Stamos, I., Salanova Grau, J.M., Mitsakis, E.: Modeling Effects of Precipitation on Vehicle Speed: Floating-Car Data Approach. TRB 2016 Annual Meeting (2016)
Chien, S.I.J., Kuchipudi, C.M.: Dynamic travel time prediction with real-time and historic data. J. Transp. Eng. 129(6), 608–616 (2003)
Mitsakis, E., Salanova Grau, J.M., Chrysohoou, E., Aifadopoulou, G.: A robust method for real time estimation of travel times for dense urban road networks using point-to-point detectors. Transport 30(3), 264–272 (2015). https://doi.org/10.3846/16484142.2015.1078845
Charakopoulos, A.K., Katsouli, G.A., Karakasidis, T.E.: Dynamics and causalities of atmospheric and oceanic data identified by complex networks and Granger causality analysis. Physica A 495, 436–453 (2018)
Gao, Z.K., Small, M., Kurths, J.: Complex network analysis of time series. Europhy. Lett. 116(5), 50001 (2016). https://doi.org/10.1209/0295-5075/116/50001
Chatfield, C.: Time-Series Forecasting. Chapman & Hall/CRC, Boca Raton (2000). ISBN 1-58488-063-5
STAT 510 – Applied Time Series Analysis, Lesson 8: Regression with ARIMA errors, Cross correlation functions, and Relationships between 2 Time Series, 8.2 Cross Correlation Functions and Lagged Regressions. https://newonlinecourses.science.psu.edu/stat510/node/74/
Granger, C.W.J.: Investigating causal relations by econometric models and cross-spectral methods. Econometrica 37(3), 424–438 (1969). https://doi.org/10.2307/1912791
Roebroeck, A., Formisano, E., Goebel, R.: Mapping directed influence over the brain using Granger causality and fMRI. NeuroImage 25(1), 230–242 (2005). https://doi.org/10.1016/j.neuroimage.2004.11.017
Attanasio, A.: Testing for linear Granger causality from natural/anthropogenic forcings to global temperature anomalies. Theoret. Appl. Climatol. 110, 281–289 (2012)
Charakopoulos, A.K., Karakasidis, T.E., Liakopoulos, A.: Spatiotemporal analysis of seawatch buoy meteorological observations. Environ. Process. 2(1), 23–39 (2015)
Barnett, L., Seth, A.K.: The MVGC multivariate Granger causality toolbox: a new approach to Granger-causal inference. J. Neurosci. Methods 223, 50–68 (2014)
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The authors wish to acknowledge the Hellenic Institute of Transport for the access to the traffic data.
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Myrovali, G., Karakasidis, T., Charakopoulos, A., Tzenos, P., Morfoulaki, M., Aifadopoulou, G. (2019). Exploiting the Knowledge of Dynamics, Correlations and Causalities in the Performance of Different Road Paths for Enhancing Urban Transport Management. In: Freitas, P., Dargam, F., Moreno, J. (eds) Decision Support Systems IX: Main Developments and Future Trends. EmC-ICDSST 2019. Lecture Notes in Business Information Processing, vol 348. Springer, Cham. https://doi.org/10.1007/978-3-030-18819-1_3
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