Freight rate and demand forecasting in road freight transportation using econometric and artificial intelligence methods
Abstract
The digitisation of the transportation sector and data availability have opened up new opportunities to implement data-driven methods for improving company performance. This article analyses demand and freight rate forecasting techniques in the context of the road freight transportation company. The European market was analysed in this research, and direction from the Netherlands to Italy was selected for the case study. Performed investigation showed that econometric models such as Auto-Regressive Integrated Moving Average (ARIMA) used for demand prognosis provide good results. Freight rate forecasting is different; econometric models, including multivariate models ARIMA with exogenous variables (ARIMAX) and Seasonal ARIMAX (SARIMAX), do not perform satisfactorily under specified time intervals, therefore MultiLayer Perceptron (MLP) was used as a solution. It can be seen that Artificial Intelligence (AI) based methods provide better results. Despite its success, the AI-based approach alone is not recommended for practical implementation since forecasted input parameters are necessary. Lastly, the study uncovers a valuable insight. A strong correlation (0.86) between spot and contract rates was found, and the article shows how current spot rates can be used for contract rate forecasting.
First published online 7 February 2024
Keyword : transportation, road freight transport, freight rate forecasting, demand forecasting, econometric models, artificial neural networks
How to Cite
Liachovičius, E., Šabanovič, E., & Skrickij, V. (2023). Freight rate and demand forecasting in road freight transportation using econometric and artificial intelligence methods. Transport, 38(4), 231–242. https://doi.org/10.3846/transport.2023.20932
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Liu, H.; Li, C.; Shao, Y.; Zhang, X.; Zhai, Z.; Wang, X.; Qi, X.; Wang, J.; Hao, Y.; Wu, Q.; Jiao, M. 2020. Forecast of the trend in incidence of acute hemorrhagic conjunctivitis in China from 2011–2019 using the seasonal autoregressive integrated moving average (SARIMA) and exponential smoothing (ETS) models, Journal of Infection and Public Health 13(2): 287–294. https://doi.org/10.1016/j.jiph.2019.12.008
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Retek, M. 2021. Scenario building in an interactive environment and online communication, Technological Forecasting and Social Change 162: 120395. https://doi.org/10.1016/j.techfore.2020.120395
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Truant, E.; Broccardo, L.; Dana, L.-P. 2021. Digitalisation boosts company performance: an overview of Italian listed companies, Technological Forecasting and Social Change 173: 121173. https://doi.org/10.1016/j.techfore.2021.121173
Vilutienė, T.; Podvezko, V.; Ambrasas, G.; Šarka, V. 2014. Forecasting the demand for blue-collar workers in the construction sector in 2020: the case of Lithuania, Economic Research – Ekonomska Istraživanja 27(1): 442–462. https://doi.org/10.1080/1331677X.2014.966972
Wang, S.; Chaovalitwongse, W. A. 2011. Evaluating and comparing forecasting models, in J. J. Cochran, L. A. Cox, P. Keskinocak, J. P. Kharoufeh, J. C. Smith (Eds.). Wiley Encyclopedia of Operations Research and Management Science, eorms0307. https://doi.org/10.1002/9780470400531.eorms0307
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Zhao, Y.; Van Delft, S.; Morgan-Thomas, A.; Buck, T. 2020. The evolution of platform business models: exploring competitive battles in the world of platforms, Long Range Planning 53(4): 101892. https://doi.org/10.1016/j.lrp.2019.101892
ArunKumar, K. E.; Kalaga, D. V.; Kumar, C. M. S.; Chilkoor, G.; Kawaji, M.; Brenza, T. M. 2021. Forecasting the dynamics of cumulative COVID-19 cases (confirmed, recovered and deaths) for top-16 countries using statistical machine learning models: auto-regressive integrated moving average (ARIMA) and seasonal auto-regressive integrated moving average (SARIMA), Applied Soft Computing 103: 107161. https://doi.org/10.1016/j.asoc.2021.107161
Batchelor, R.; Alizadeh, A.; Visvikis, I. 2007. Forecasting spot and forward prices in the international freight market, International Journal of Forecasting 23(1): 101–114. https://doi.org/10.1016/j.ijforecast.2006.07.004
Brownlee, J. 2020. Introduction to Time Series Forecasting with Python: How to Prepare Data and Develop Models to Predict Future. Machine Learning Mastery. 367 p.
Chen, X.-W.; Lin, X. 2014. Big data deep learning: challenges and perspectives, IEEE Access 2: 514–525. https://doi.org/10.1109/ACCESS.2014.2325029
CL. 2020. 2020 Recession Outlook: 5 Key Indicators Driving Truckload Rates. Coyote Logistics (CL). Available from Internet: https://coyotelogistics.medium.com/2020-recession-outlook-5-key-indicators-driving-truckload-rates-4a92aa6b0b91
CL. 2024. Contract vs. Spot Rates: What’s the Difference in Truckload Freight Shipping? Coyote Logistics (CL). Available from Internet: https://resources.coyote.com/source/contract-vs-spot-rates
Claveria, O.; Monte, E.; Torra, S. 2017. Using survey data to forecast real activity with evolutionary algorithms. A cross-country analysis, Journal of Applied Economics 20(2): 329–349. https://doi.org/10.1016/S1514-0326(17)30015-6
Dekker, M.; Van Donselaar, K.; Ouwehand, P. 2004. How to use aggregation and combined forecasting to improve seasonal demand forecasts, International Journal of Production Economics 90(2): 151–167. https://doi.org/10.1016/j.ijpe.2004.02.004
ERTRAC. 2021. Carbon-Neutral Road Transport 2050: a Technical Study From a Well-to-Wheels Perspective. European Road Transport Research Advisory Council (ERTRAC). 37 p. Available from Internet: https://www.ertrac.org/wp-content/uploads/2022/12/ERTRAC-PPT-Carbon-Neutral-Road-Transport-2050_Workshop_April_29.pdf
Falatouri, T.; Darbanian, F.; Brandtner, P.; Udokwu, C. 2022. Predictive analytics for demand forecasting – a comparison of SARIMA and LSTM in retail SCM, Procedia Computer Science 200: 993–1003. https://doi.org/10.1016/j.procs.2022.01.298
Fernández-Portillo, A.; Almodóvar-González, M.; Sánchez-Escobedo, M. C.; Coca-Pérez, J. L. 2022. The role of innovation in the relationship between digitalisation and economic and financial performance. A company-level research, European Research on Management and Business Economics 28(3): 100190. https://doi.org/10.1016/j.iedeen.2021.100190
Glenn, J. C.; Gordon, T. J. 2009. Futures Research Methodology – Version 3.0. The Millennium Project. 1300 p.
Goodfellow, I.; Bengio, Y.; Courville, A. 2016. Deep Learning. MIT Press. 800 p.
He, Y.; Henze, J.; Sick, B. 2020. Continuous learning of deep neural networks to improve forecasts for regional energy markets, IFAC-PapersOnLine 53(2): 12175–12182. https://doi.org/10.1016/j.ifacol.2020.12.1017
Jierula, A.; Wang, S.; OH, T.-M.; Wang, P. 2021. Study on accuracy metrics for evaluating the predictions of damage locations in deep piles using artificial neural networks with acoustic emission data, Applied Sciences 11(5): 2314. https://doi.org/10.3390/app11052314
Kauko, K.; Palmroos, P. 2014. The Delphi method in forecasting financial markets – an experimental study, International Journal of Forecasting 30(2): 313–327. https://doi.org/10.1016/j.ijforecast.2013.09.007
Kavussanos, M. G.; Visvikis, I. D.; Batchelor, R. 2004a. Over-the-counter forward contracts and spot price volatility in shipping, Transportation Research Part E: Logistics and Transportation Review 40(4): 273−296. https://doi.org/10.1016/j.tre.2003.08.007
Kavussanos, M. G.; Visvikis, I. D.; Menachof, D. A. 2004b. The unbiasedness hypothesis in the freight forward market: evidence from cointegration tests, Review of Derivatives Research 7(3): 241−266. https://doi.org/10.1007/s11147-004-4811-7
Klujsza, K. 2024. Q1 2024 Truckload Market Forecast: Spot & Contract Freight Rate Trends. Coyote Logistics (CL). Available from Internet: https://resources.coyote.com/source/us-truckload-market-guide
LeCun, Y.; Bengio, Y.; Hinton, G. 2015. Deep learning, Nature 521(7553): 436–444. https://doi.org/10.1038/nature14539
Liachovičius, E.; Skrickij, V.; Podviezko, A. 2020. MCDM evaluation of asset-based road freight transport companies using key drivers that influence the enterprise value, Sustainability 12(18): 7259. https://doi.org/10.3390/su12187259
Liu, H.; Li, C.; Shao, Y.; Zhang, X.; Zhai, Z.; Wang, X.; Qi, X.; Wang, J.; Hao, Y.; Wu, Q.; Jiao, M. 2020. Forecast of the trend in incidence of acute hemorrhagic conjunctivitis in China from 2011–2019 using the seasonal autoregressive integrated moving average (SARIMA) and exponential smoothing (ETS) models, Journal of Infection and Public Health 13(2): 287–294. https://doi.org/10.1016/j.jiph.2019.12.008
Mahlamäki, T.; Storbacka, K.; Pylkkönen, S.; Ojala, M. 2020. Adoption of digital sales force automation tools in supply chain: customers’ acceptance of sales configurators, Industrial Marketing Management 91: 162–173. https://doi.org/10.1016/j.indmarman.2020.08.024
Markevičiūtė, J.; Bernatavičienė, J.; Levulienė, R.; Medvedev, V.; Treigys, P.; Venskus, J. 2022. Attention-based and time series models for short-term forecasting of COVID-19 spread, Computers, Materials & Continua 70(1): 695–714. https://doi.org/10.32604/cmc.2022.018735
Miller, J. W.; Scott, A.; Williams, B. D. 2021. Pricing dynamics in the truckload sector: the moderating role of the electronic logging device mandate, Journal of Business Logistics 42(4): 388–405. https://doi.org/10.1111/jbl.12256
Nwokike, C. C.; Offorha, B. C.; Obubu, M.; Ugoala, C. B.; Ukomah, H. I. 2020. Comparing SANN and SARIMA for forecasting frequency of monthly rainfall in Umuahia, Scientific African 10: e00621. https://doi.org/10.1016/j.sciaf.2020.e00621
Retek, M. 2021. Scenario building in an interactive environment and online communication, Technological Forecasting and Social Change 162: 120395. https://doi.org/10.1016/j.techfore.2020.120395
Ruggieri, R.; Savastano, M.; Scalingi, A.; Bala, D.; D’Ascenzo, F. 2018. The impact of digital platforms on business models: an empirical investigation on innovative start-ups, Management & Marketing 13(4): 1210–1225. https://doi.org/10.2478/mmcks-2018-0032
Ruiz-Aguilar, J. J.; Turias, I. J.; Jiménez-Come, M. J. 2014. Hybrid approaches based on SARIMA and artificial neural networks for inspection time series forecasting, Transportation Research Part E: Logistics and Transportation Review 67: 1–13. https://doi.org/10.1016/j.tre.2014.03.009
Schramm, H.-J.; Munim, Z. H. 2021. Container freight rate forecasting with improved accuracy by integrating soft facts from practitioners, Research in Transportation Business & Management 41: 100662. https://doi.org/10.1016/j.rtbm.2021.100662
Shukur, O. B.; Lee, M. H. 2015. Daily wind speed forecasting through hybrid KF-ANN model based on ARIMA, Renewable Energy 76: 637–647. https://doi.org/10.1016/j.renene.2014.11.084
Tauscher, K.; Kietzmann, J. 2017. Learning from failures in the sharing economy, MIS Quarterly Executive 16(4): 2. Available from Internet: https://aisel.aisnet.org/misqe/vol16/iss4/2/
TI. 2024. European Road Freight Transport 2023. Report ID 1459895. Transport Intelligence (TI). 193 p.
Truant, E.; Broccardo, L.; Dana, L.-P. 2021. Digitalisation boosts company performance: an overview of Italian listed companies, Technological Forecasting and Social Change 173: 121173. https://doi.org/10.1016/j.techfore.2021.121173
Vilutienė, T.; Podvezko, V.; Ambrasas, G.; Šarka, V. 2014. Forecasting the demand for blue-collar workers in the construction sector in 2020: the case of Lithuania, Economic Research – Ekonomska Istraživanja 27(1): 442–462. https://doi.org/10.1080/1331677X.2014.966972
Wang, S.; Chaovalitwongse, W. A. 2011. Evaluating and comparing forecasting models, in J. J. Cochran, L. A. Cox, P. Keskinocak, J. P. Kharoufeh, J. C. Smith (Eds.). Wiley Encyclopedia of Operations Research and Management Science, eorms0307. https://doi.org/10.1002/9780470400531.eorms0307
Wen, D.; Liu, L.; Wang, Y.; Zhang, Y. 2022. Forecasting crude oil market returns: enhanced moving average technical indicators, Resources Policy 76: 102570. https://doi.org/10.1016/j.resourpol.2022.102570
Zhao, Y.; Van Delft, S.; Morgan-Thomas, A.; Buck, T. 2020. The evolution of platform business models: exploring competitive battles in the world of platforms, Long Range Planning 53(4): 101892. https://doi.org/10.1016/j.lrp.2019.101892