Using Machine Learning to Predict Freight Vehicles' Demand for Loading Zones in Urban Environments
Journal article, 2023

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.

machine learning (artificial intelligence)

intelligent transportation systems

data and data science

urban freight transportation

information systems and technology

freight systems

artificial intelligence and advanced computing applications

freight transportation data

Author

Andres Regal Ludowieg

Universidad del Pacífico

Ivan Sanchez-Diaz

Chalmers, Technology Management and Economics, Service Management and Logistics

Lokesh Kumar Kalahasthi

Chalmers, Technology Management and Economics, Service Management and Logistics

Transportation Research Record

0361-1981 (ISSN) 21694052 (eISSN)

Vol. 2677 1 829-842

Subject Categories

Other Computer and Information Science

Transport Systems and Logistics

Bioinformatics (Computational Biology)

DOI

10.1177/03611981221101893

More information

Latest update

1/12/2023