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Using Machine Learning to Predict Freight Vehicles’ Demand for Loading Zones in Urban Environments

Using Machine Learning to Predict Freight Vehicles’ Demand for Loading Zones in Urban Environments

Authors :
Ludowieg, Andres Regal
Sanchez-Diaz, Ivan
Kalahasthi, Lokesh Kumar
Source :
Transportation Research Record; January 2023, Vol. 2677 Issue: 1 p829-842, 14p
Publication Year :
2023

Abstract

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.

Details

Language :
English
ISSN :
03611981 and 21694052
Volume :
2677
Issue :
1
Database :
Supplemental Index
Journal :
Transportation Research Record
Publication Type :
Periodical
Accession number :
ejs61530988
Full Text :
https://doi.org/10.1177/03611981221101893