Using machine learning to predict freight vehicles’ demand for loading zones in urban environments

Andres Regal Ludowieg, Ivan Sanchez-Diaz, Lokesh Kumar Kalahasthi

Producción científica: Contribución a una revistaArtículo de revista revisión exhaustiva

5 Citas (Scopus)


This paper studies demand for public loading zones in urban environments and seeks to develop a machine learning algorithm to predict their demand. Understanding and predicting demand for public loading zones can: (i) support better management of the loading zones and (ii) provide better pre-advice so that transport operators can plan their routes in an optimal way. The methods used are linear regression analysis and neural networks. Six months of parking data from the city of Vic in Spain are used to calibrate and test the models, where the parking data is transformed into a time-series format with forecasting targets. For each loading zone, a different model is calibrated to test which model has the best performance for the loading zone’s particular demand pattern. To evaluate each model’s performance, both root mean square error and mean absolute error are computed. The results show that, for different loading zone demand patterns, different models are better suited. As the prediction horizon increases, predicting further into the future, the neural network approaches start to give better predictions than linear models.

Idioma originalInglés
Páginas (desde-hasta)829-842
Número de páginas14
PublicaciónTransportation Research Record
Fecha en línea anticipada1 ago. 2022
EstadoPublicada - ene. 2023
Publicado de forma externa

Nota bibliográfica

Funding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was made possible through the financial support from the Fordonsstrategisk Forgsking och Innovation (FFI) from VINNOVA and the Urban Freight Platform from the Volvo Research and Education Foundation (VREF) under project 2019-03093: Using data analysis for smart management of loading zones in cities.

Publisher Copyright:
© National Academy of Sciences: Transportation Research Board 2022.


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