Method for travel time prediction in emerging markets based on anonymous truck GPS data

Carlos Mario Pérez-González (), Jaime Mora-Vargas (), Jared Piña-Barcenas () and Miguel Gaston Cedillo-Campos ()
Additional contact information
Carlos Mario Pérez-González: IMT-National Laboratory For Transportation Systems and Logistics
Jaime Mora-Vargas: School of Engineering and Science
Jared Piña-Barcenas: IMT-National Laboratory For Transportation Systems and Logistics
Miguel Gaston Cedillo-Campos: IMT-National Laboratory For Transportation Systems and Logistics

Annals of Operations Research, 2024, vol. 342, issue 1, No 18, 585-620

Abstract: Abstract Travel time pattern analysis and prediction are essential for achieving better logistics performance in the supply chain. Solid theoretical assumptions based on reliable historical information must be established to analyze travel time; however, access to such information in emerging markets is challenging. Neural networks can learn historical data patterns and are proposed in this study as an artificial intelligence tool to calculate and forecast travel times to develop reliability measurements. Thus, the following measures and indices were used: the percentiles of travel time and the mean, amplitude, skew, buffer, and indices of fluidity and planning time regarding the mean. The obtained data were compared, and a small variation was found between the control and prediction sets. Furthermore, the model did not generate large prediction errors based on the root-mean-square error (RMSE) values. According to the mean difference test results, the hypothesis that the real and forecasted datasets have the same mean was not rejected. Overall, the possibility of predicting travel times using neural networks allows modeling the transportation segments where information is unavailable, thus, preserving travel data anonymity. Finally, helpful conclusions and a proposal for future research are presented.

Keywords: Travel time prediction; Discrete-time neural networks; Forecasting accuracy; Transportation; Emerging markets. (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10479-023-05354-x Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:annopr:v:342:y:2024:i:1:d:10.1007_s10479-023-05354-x

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10479

DOI: 10.1007/s10479-023-05354-x

Access Statistics for this article

Annals of Operations Research is currently edited by Endre Boros

More articles in Annals of Operations Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().