Statistical Evaluation of Large-Scale Data Logistics System

Radovan Somplak; Zlata Smidova; Veronika Smejkalova; Vlastimir Nevrly

doi:10.13164/mendel.2018.2.009

Radovan Somplak Brno University of Technology, Faculty of Mechanical Engineering, Institute of Process Engineering
Zlata Smidova Brno University of Technology, Faculty of Mechanical Engineering, Institute of Process Engineering
Veronika Smejkalova Brno University of Technology, Faculty of Mechanical Engineering, Institute of Process Engineering
Vlastimir Nevrly Brno University of Technology, Faculty of Mechanical Engineering, Institute of Process Engineering

DOI: https://doi.org/10.13164/mendel.2018.2.009

Keywords: data reconciliation, GPS data, random errors, weight of data, transportation time

Abstract

Data recording is struggling with the occurrence of errors, which worsen the accuracy of follow-up calculations. Achievement of satisfactory results requires the data processing to eliminate the influence of errors. This paper applies a data reconciliation technique for mining of data from ecording movement vehicles. The database collects information about the start and end point of the route (GPS coordinates) and total duration.
The presented methodology smooths available data and allows to obtain an estimation of transportation time through individual parts of the entire recorded route. This process allows obtaining valuable information which can be used for further transportation planning. First, the proposed mathematical model is tested on simplifled example. The real data application requires necessary preprocessing within which anticipated routes are designed. Thus, the database is supplemented with information on the probable speed of the vehicle. The mathematical model is based on weighted least squares data reconciliation which is organized iteratively. Due to the time-consuming calculation, the linearised model is computed to initialize the values for a complex model. The attention is also paid to the weight setting. The weighing system is designed to reflect the quality of specific data and the dependence on the frequency of trafic. In this respect, the model is not strict, which leaves the possibility to adapt to the current data. The case study focuses on the GPS data of shipping vehicles in the particular city in the Czech Republic with several types of roads.

References

Bong, C.P.C, Lim, L.Y., Lee, C.T., Fan, Y.V., Klemes, J.J.: The role of smart waste management in smart agriculture. Chemical Engineering Transactions 70, 937–942 (2018).

Burian, F., Florian, T., Zalud, L.: The identication of drivers behaviour through the use of GPS and odometry. Mendel 17(1), 492–496 (2011).

Mazaré, P.-E., Tossavainen, O.-P., Bayen, A.M., Work D.B.: Trade-os between inductive loops and GPS probe vehicles for travel time estimation: A Mobile Century case study. TRB, Annual Meeting, (2012).

Patire, A.D, Wright, M., Prodhomme, B., Bayen A.M.: How much GPS data do we need? Transportation Research Part C: Emerging Technologies 58, 325–342 (2015). DOI: 10.1016/j.trc.2015.02.011.

Narasimhan, S., Jordache, C.: Data reconciliation and gross error detection: an intelligent use of process data. Houston: Gulf Publishing Company (2000). ISBN 0-88415-255-3.

Somplak, R., Nevrly, V., Smejkalova, V., Pavlas, M., Kudela, J.: Verication of Information in Large Databases by Mathematical Programming in Waste Management. Chemical Engineering Transactions 61, 985–990 (2017). DOI: 10.3303/CET1761162.

Fuente, M.J. , Gutierrez, G., Gomez, E., Sarabia, D., de Prada, C.: Gross error management in data reconciliation. IFAC-PapersOnLine 48(8), 623–628 (2015). DOI: 10.1016/j.ifacol.2015.09.037.

Navratilova, B., Hrdina J.: Multilateration in volumetry: Case study on demonstrator MCV 754 quick. Mendel 22(1), 295–300 (2016).

Jiang, X., Liu, P., Li, Z.: Data reconciliation and gross error detection for operational data in power plants. Energy 75, 14–23 (2014). DOI: 10.1016/j.energy.2014.03.024.

Cencic, O.: Nonlinear data reconciliation in material ow analysis with software STAN. Sustainable Environment Research 26(6), 291–298 (2016). DOI: 10.1016/j.serj.2016.06.002

Sun, S., Huang, D., Gong, Y.: Gross Error Detection and Data Reconciliation using Historical Data. Procedia Engineering 15, 55–59 (2011). DOI:10.1016/j.proeng.2011.08.012.

Isom, J.D., Stamps, A.T., Esmaili A., Mancilla, C.: Two methods of data reconciliation for pipeline networks. Computers & Chemical Engineering 115, 487–503 (2018). DOI:10.1016/j.compchemeng.2018.05.008.

Raee, A., Behrouzshad, F.: Data reconciliation with application to a natural gas processing plant. Journal of Natural Gas Science and Engineering 32, 538–545 (2016). DOI: 10.1016/j.jngse.2016.03.071.

Doubravsky, K., Dohnal, M.: Reconciliation of decision-making heuristics based on decicion trees topologies

and incomplete fuzzy probabilities sets. PLoS ONE 10(7) (2015). DOI: 10.1371/journal.pone.0131590.

Hrabec, D., Viktorin, A., Somplak, R., Pluhacek, M., Popela, P.: A heuristic approach to facility location problem for waste management: A case study. Mendel 22(1), 61–66 (2016).

Hrabec, D., Popela, P., Roupec, J., Jindra, P., Novotny, J.: Hybrid algorithm for wait-and-see transportation network design problem with linear pricing. Mendel 21(1), 183–188 (2015).

Kudela, J., Popela, P.: Warm-start cuts for generalized benders decomposition. Kybernetika 53(6), 1012–1025 (2017).

Hanna, G., Miskarik, K., Dobrovsky, L., Osmera, P.: An ecient two-level optimization method for optimal tunning of controllers. Mendel 21(1), 71–76 (2015).

Sima, J.: The computational power of neural networks and representations of numbers in non-integer bases. Mendel 23(1), 103–110 (2017).