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

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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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Abbreviations

T :

Time

D :

Day of week

w :

Weather

h :

Holiday

T :

Temperature

l :

Location

D :

Distance

E :

Event

AFE:

Average forecast error

R 2 :

Coefficient of determination

P :

Parking price

Pc:

Parking capacity

Ro:

Rate of vehicles occupying

Rl:

Rate of vehicles leaving

Po:

Previous observations

idA:

Area name

idP:

Parking identifier

POR:

Parking occupancy rate

SDFE:

Standard deviation forecast error

NAP:

Number of available places

SP:

Survival probability

Dt:

Duration time

MAE:

Mean absolute error

MAPE:

Mean absolute percentage error

MSE:

Mean square error

MNE:

Mean normalized error

RMSE:

Root mean square error

RRSE:

Root relative squared error

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  1. Research Foundation for Development and Innovation in Science and Engineering, 8118, Casablanca, Morocco

    Hanae Errousso, Siham Benhadou & Hicham Medromi

  2. National and High School of Electricity and Mechanic, HASSAN II University, 8118, Casablanca, Morocco

    Hanae Errousso, Siham Benhadou & Hicham Medromi

  3. EIGSI, 20410, Casablanca, Morocco

    Hanae Errousso

  4. Ecole Normale Supérieure, Moulay Ismail University, 3104, Meknes, Morocco

    El Arbi Abdellaoui Alaoui

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  1. Hanae Errousso
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  2. El Arbi Abdellaoui Alaoui
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  3. Siham Benhadou
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  4. Hicham Medromi
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Correspondence to El Arbi Abdellaoui Alaoui.

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Errousso, H., Abdellaoui Alaoui, E.A., Benhadou, S. et al. Exploring how independent variables influence parking occupancy prediction: toward a model results explanation with SHAP values. Prog Artif Intell 11, 367–396 (2022). https://doi.org/10.1007/s13748-022-00291-5

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