Multisource Data Integration and Comparative Analysis of Machine Learning Models for On-Street Parking Prediction

Saba Inam, Azhar Mahmood, Shaheen Khatoon, Majed Alshamari and Nazia Nawaz
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Saba Inam: Department of Computer Science, Shaheed Zulfikar Ali Bhutto Institute of Science and Technology (SZABIST), Islamabad 44000, Pakistan
Azhar Mahmood: Faculty of Computing, Capital University of Science & Technology (CUST), Islamabad 44000, Pakistan
Shaheen Khatoon: School of AI and Advanced Computing, Xi’an Jiaotong Liverpool University, Suzhou 215000, China
Majed Alshamari: College of Computer Science and Information Technology, King Faisal University, Al-Ahsa 31982, Saudi Arabia
Nazia Nawaz: Department of Computer Science, Shaheed Zulfikar Ali Bhutto Institute of Science and Technology (SZABIST), Islamabad 44000, Pakistan

Sustainability, 2022, vol. 14, issue 12, 1-21

Abstract: Searching for a free parking space can lead to traffic congestion, increasing fuel consumption, and greenhouse gas pollution in urban areas. With an efficient parking infrastructure, the cities can reduce carbon emissions caused by additional fuel combustion, waiting time, and traffic congestion while looking for a free parking slot. A potential solution to mitigating parking search is the provision of parking-related data and prediction. Previously many external data sources have been considered in prediction models; however, the underlying impact of contextual data points and prediction has not received due attention. In this work, we integrated parking occupancy, pedestrian, weather, and traffic data to analyze the impact of external factors on on-street parking prediction. A comparative analysis of well-known Machine (ML) Learning and Deep Learning (DL) techniques, including Multilayer Perceptron (MLP), Random Forest (RF), Decision Trees (DT), K-Nearest Neighbors (KNN), Gradient Boosting (GA), Adaptive Boosting (AB), and linear SVC for the prediction of OnStreet parking space availability has been conducted. The results show that RF outperformed other techniques evaluated with an average accuracy of 81% and an AUC of 0.18. The comparative analysis shows that less complex algorithms like RF, DT, and KNN outperform complex algorithms like MLP in terms of prediction accuracy. All four data sources have positively impacted the prediction, and the proposed solution can determine the best possible parking slot based on weather conditions, traffic flow, and pedestrian volume. The experiments on live prediction showed an ingest rate of 0.1 and throughput of 0.3 events per second, demonstrating a fast and reliable prediction approach for available slots within a 5–10 min time frame. The study is scalable for larger time frames and faster predictions that can be implemented for IoT-based big data-driven environments for on-street and off-street parking.

Keywords: smart city applications; Internet of Things; predictive analytics; on-street parking prediction (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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