Travel-time prediction model of ready-mixed concrete trucks for improving transportation efficiency

Article Sidebar

PDF
Published: Nov 2, 2023
Keywords:
Travel time prediction Multiple linear regression Ready-mixed-concrete truck

Main Article Content

Kittipong Thawongklang
Department of Civil Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, Thailand
Ladda Tanwanichkul
Department of Civil Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, Thailand

Abstract

Ready-mixed concrete transportation planning is regarded as a core aspect of the construction industry, for which the key success factor is its effective time management. Travel time is the primary indicator of increased efficiency in advanced delivery scheduling. Therefore, this study aims to create a travel-time prediction model for ready-mixed concrete business that have, insufficient knowledge of transportation, have recently started a business, or are expanding their factories to new locations. We prioritize the model calculation speed while the accuracy within acceptable ranges becomes secondary. Data were collected, -on the travel history of trucks within the area of the bypass ring road in, Udon Thani province, Thailand, from GPS devices installed in the trucks that transmitted signal every minute. Multiple linear regression (MLR) was selected for this model because it is reliable, widely accepted, and consistent with instant decisions made within business constraints. The obtained result was the optimal travel-time prediction model with confidence interval for an adjusted R2 of 81.35. The model was validated by using the remaining data, which demonstrate that RMSE was equal to 0.0868 hours or 5.208 minutes.

Article Details

How to Cite
Thawongklang, K., & Tanwanichkul, L. . (2023). Travel-time prediction model of ready-mixed concrete trucks for improving transportation efficiency. Engineering and Applied Science Research, 50(6), 619–625. Retrieved from https://ph01.tci-thaijo.org/index.php/easr/article/view/250985
Issue
Vol. 50 No. 6 (2023)
Section
ORIGINAL RESEARCH
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

References

Wu CH, Ho JM, Lee DT. Travel-time prediction with support vector regression. IEEE Trans Intell Transp Syst. 2004;5(4):276-81.

Cristóbal T, Padrón G, Quesada-Arencibia A, Alayón F, de Blasio G, García CR. Bus travel time prediction model based on profile similarity. Sensors. 2019; 19(13):2869.

LIN HE, Zito R, Taylor MA. A review of travel-time prediction in transport and logistics. Proc East Asia Soc Transp Stud. 2005;5:1433-48.

van der Spoel S, Amrit C, Hillegersberg J. Predictive analytics for truck arrival time estimation: a field study at a European distribution center. Int J Prod Res. 2017;55:5062-78.

Mackie PJ, Jara-Dı́az S, Fowkes AS. The value of travel time savings in evaluation. Transp Res E Logist Transp Rev. 2001;37(2-3):91-106.

Long K, Yao W, Gu J, Wu W, Han LD. Predicting freeway travel time using multiple- source heterogeneous data integration. Appl Sci. 2019;9(1):104.

Dimitriou L, Gkania V. Dynamic short-term projections of travel time distributions in urban signalized networks utilizing composite information of traffic characteristics. IFAC-Pap. 2016;49(3):237-42.

Gaweł P, Jaszkiewicz A. Improving short-term travel time prediction for on-line car navigation by linearly transforming historical traffic patterns to fit the current traffic conditions. Procedia Soc Behav Sci. 2011;20:638-47.

Krause CM, Zhang L. Short-term travel behavior prediction with GPS, land use, and point of interest data. Transp Res B Methodol. 2019;123:349-61.

Li Y, Gunopulos D, Lu C, Guibas L. Urban travel time prediction using a small number of GPS floating cars. Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems; 2017 Nov 7-10; Redondo Beach, USA. USA: ACM; 2017. p. 1-10.

Sheng Z, Lv Z, Li J, Xu Z, Sun H, Liu X, et al. Taxi travel time prediction based on fusion of traffic condition features. Comput Electr Eng. 2023;105:108530.

Gal A, Mandelbaum A, Schnitzler F, Senderovich A, Weidlich M. Traveling time prediction in scheduled transportation with journey segments. Inf Syst. 2017;64:266-80.

Parslov A, Petersen NC, Rodrigues F. Short-term bus travel time prediction for transfer synchronization with intelligent uncertainty handling. Expert Syst Appl. 2023;232:120751.

Jo Y, Kim J, Oh C, Kim I, Lee G. Benefits of travel time savings by truck platooning in Korean freeway networks. Transp Policy. 2019;83:37-45.

Li N, Wu Y, Wang Q, Ye H, Wang L, Jia M, et al. Underground mine truck travel time prediction based on stacking integrated learning. Eng Appl Artif Intell. 2023;120:105873.

Jiang G, Zhang R. Travel-time prediction for urban arterial road: a case on China. Proceedings of the IEEE International Vehicle Electronics Conference 2001. IVEC 2001 (Cat. No.01EX522); 2001 Sep 25-28; Tottori, Japan. USA: IEEE; 2001. p. 255-60.

Kisgyörgy L, Rilett LR. Travel time prediction by advanced neural network. Period Polytech Civ Eng. 2002;46(1):15-32.

Zong F, Lin H, Yu B, Pan X. Daily commute time prediction based on genetic algorithm. Math Probl Eng. 2012;2012:1-20.

Das S, Kalava RN, Kumar KK, Kandregula A, Suhaas K, Bhattacharya S, et al. Map enhanced route travel time prediction using deep neural networks. arXiv.1911.02623. 2019:1-5.

Servos N, Liu X, Teucke M, Freitag M. Travel time prediction in a multimodal freight transport relation using machine learning algorithms. Logistics. 2020;4(1):1.

Paterson D, Rose G. Dynamic travel time estimation on instrumented freeways [Internet]. 1999 [cited 2022 Nov 1]. Available from: https://trid.trb.org/view/714227.

van Grol HJM, Danech-Pajouh M, Manfredi S, Whittaker J. DACCORD: On-line travel time prediction [Internet]. 1999 [cited 2022 Nov 1]. Available from: https://trid.trb.org/view/639593.

Zhang X, Rice JA. Short-term travel time prediction. Transp Res Part C Emerg Technol. 2003;11(3-4):187-210.

Jammula JK, Bera R, Ravishankar KVR. Travel time prediction modelling in mixed traffic conditions. Int J Traffic Transp Eng. 2018;8(1):135-47.

Smith AE, Mason AK. Cost estimation predictive modeling: regression versus neural network. Eng Econ. 1997;42(2):137-61.

Chien SIJ, Kuchipudi CM. Dynamic travel time prediction with real-time and historic data. J Transp Eng. 2003;129(6):608-16.

Karl CA, Trayford RS. Delivery of real-time and predictive travel time information: experiences from a Melbourne trial [Internet]. 2000 [cited 2022 Nov 1]. Available from: https://trid.trb.org/view/723582.

Li Y, McDonald M. Link travel time estimation using single GPS equipped probe vehicle. The IEEE 5th International Conference on Intelligent Transportation Systems; 2002 Sep 6; Singapore. USA: IEEE; 2002. p. 932-7.

Rice J, van Zwet E. A simple and effective method for predicting travel times on freeways. IEEE Trans Intell Transp Syst. 2004; 5(3):200-7.

Rupnik J, Davies J, Fortuna B, Duke A, Clarke SS. Travel time prediction on highways. 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing; 2015 Oct 26-28; Liverpool, UK. USA: IEEE; 2015. p. 1435-42.

Ajasa AA, Ajayi II, Akinwande KD, Ajayi TO. Prediction of travel time using fuzzy logic paradigm. Trans Netw Commun. 2019;7(3):1-10.