Predicting Parking Availability in Singapore with Cross-Domain Data: A New Dataset and A Data-Driven Approach

TL;DR

This paper introduces a new dataset and a deep learning framework called DeepPA for predicting parking availability across Singapore, significantly improving accuracy and enabling real-time predictions to assist drivers and urban planners.

Contribution

The paper presents the SINPA dataset and a novel deep-learning approach, DeepPA, for collective and accurate parking availability prediction in a densely populated city.

Findings

DeepPA reduces prediction error by 9.2% for 3-hour forecasts.

The SINPA dataset includes a year's data from 1,687 parking lots with diverse factors.

DeepPA is deployed in a web platform for real-time parking predictions.

Abstract

The increasing number of vehicles highlights the need for efficient parking space management. Predicting real-time Parking Availability (PA) can help mitigate traffic congestion and the corresponding social problems, which is a pressing issue in densely populated cities like Singapore. In this study, we aim to collectively predict future PA across Singapore with complex factors from various domains. The contributions in this paper are listed as follows: (1) A New Dataset: We introduce the \texttt{SINPA} dataset, containing a year's worth of PA data from 1,687 parking lots in Singapore, enriched with various spatial and temporal factors. (2) A Data-Driven Approach: We present DeepPA, a novel deep-learning framework, to collectively and efficiently predict future PA across thousands of parking lots. (3) Extensive Experiments and Deployment: DeepPA demonstrates a 9.2% reduction in prediction error for up to 3-hour forecasts compared to existing advanced models. Furthermore, we implement DeepPA in a practical web-based platform to provide real-time PA predictions to aid drivers and inform urban planning for the governors in Singapore. We release the dataset and source code at https://github.com/yoshall/SINPA.