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Progress in Artificial Intelligence

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25-09-2022 | Regular Paper

Exploring how independent variables influence parking occupancy prediction: toward a model results explanation with SHAP values

Authors: Hanae Errousso, El Arbi Abdellaoui Alaoui, Siham Benhadou, Hicham Medromi

Published in: Progress in Artificial Intelligence | Issue 4/2022

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Abstract

Finding a parking space is a difficult challenge that drivers face on a daily basis in urban neighborhoods around the world. They often report that desirable spaces near to their destination are either unavailable or very expensive, extending further the search time and congesting even more city centers. Intelligent parking solutions can integrally solve this ongoing problem by better managing existing resources. They allow drivers to access real-time information on parking space availability, collected with different detection techniques (crowdsourcing, parking meters, sensors). Some of these systems also encompass opportunistic services, such as forecasting, needed to adapt to unforeseen dynamic situations. Hence, we presented, in this paper, a methodology for predicting car park occupancy rates using four different machine learning algorithms. Each of these methods is trained with four feature sets to exemplify how information quality impacts prediction accuracy. In addition to achieving high accuracy, it is absolutely crucial to interpret model outputs and analyze each individual feature’s importance. That's why we developed an explanation model based on SHAP values. We implemented our proposal exploiting five months of real-time parking data broadcast by Aarhus City Council. Results show that the best-obtained predictions are by far very accurate with a coefficient of determination (R²) that achieves 0.988 and a mean absolute error that doesn't exceed 2.021%, while requiring a very low computing time that is only 5 s.

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Title: Exploring how independent variables influence parking occupancy prediction: toward a model results explanation with SHAP values
Authors: Hanae Errousso
El Arbi Abdellaoui Alaoui
Siham Benhadou
Hicham Medromi
Publication date: 25-09-2022
Publisher: Springer Berlin Heidelberg
Published in: Progress in Artificial Intelligence / Issue 4/2022
Print ISSN: 2192-6352
Electronic ISSN: 2192-6360
DOI: https://doi.org/10.1007/s13748-022-00291-5

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Hybrid optimization search-based ensemble model for portfolio optimization and return prediction in business investment

A hybrid machine learning approach for early mortality prediction of ICU patients

Deep multiple instance learning for automatic glaucoma prevention and auto-annotation using color fundus photography

Robust appearance modeling for object detection and tracking: a survey of deep learning approaches

A novel chaotic flower pollination algorithm for modelling an optimized low-complexity neural network-based NAV predictor model

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