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.
Original language | English |
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Pages (from-to) | 829-842 |
Number of pages | 14 |
Journal | Transportation Research Record |
Volume | 2677 |
Issue number | 1 |
Early online date | 1 Aug 2022 |
DOIs | |
State | Published - Jan 2023 |
Externally published | Yes |
Bibliographical note
Publisher Copyright:© National Academy of Sciences: Transportation Research Board 2022.
Keywords
- artificial intelligence and advanced computing applications
- data and data science
- freight systems
- freight transportation data
- information systems and technology
- intelligent transportation systems
- machine learning (artificial intelligence)
- urban freight transportation