Value of incorporating geospatial information into the prediction of on-street parking occupancy – A case study

Abstract

In light of growing urban traffic, car parking becomes increasingly critical for cities to manage. As a result, the prediction of parking occupancy has sparked significant research interest in recent years. While many external data sources have been considered in the prediction models, the underlying geographic context has mostly been ignored. Thus, in order to study the contribution of geospatial information to parking occupancy prediction models, road network centrality, land use, and Point of Interest (POI) data were incorporated in Random Forest (RF) and Artificial Neural Network (ANN, specifically Feedforward Neural Network FFNN) prediction models in this work. Model performances were compared to a baseline, which only considers historical and temporal input data. Moreover, the influence of the amount of training data, the prediction horizon, and the spatial variation of the prediction were explored. The results show that the inclusion of geospatial information led to a performance improvement of up to 25% compared to the baseline. Besides, as the prediction horizon expanded, predictions became less reliable, while the relevance of geospatial data increased. In general, land use and POI data proved to be more beneficial than road network centrality. The amount of training data did not have a significant influence on the performance of the RF model. The ANN model, conversely, achieved optimal results on a training input of 5 days. Likely attributable to varying occupancy patterns, prediction performance disparities could be identified for different parking districts and street segments. Generally, the RF model outperformed the ANN model on all predictions.

A Multi-View Approach for Regional Parking Occupancy Prediction with Attention Mechanisms

“…Regional parking occupancy prediction is one of the foundations of parking management and guidance systems, which can be considered a typical spatial-temporal prediction problem. Reliable and accurate regional parking occupancy prediction can recommend suitable parking spaces for drivers and help city managers dynamically adjust parking management strategies to improve the utilization of parking resources [16,17].…”

Prioritization‐based subsampling quality assessment methodology for mobility‐related information systems

“…The latter can even help drivers decide whether it is wise to take their vehicles. The state-ofthe-art OSPI systems are mostly developed using complex machine learning techniques [7,8,10,[12][13][14][15][16][17][18]. The majority of models aim to achieve real-time prediction, but there has also been a study on estimating parking availability for a given time interval, like 10-20 min [19].…”

“…The main difference between state-of-the-art OSPI models available and how they are validated is the data gathered and the features considered for training, validating, and testing the models [19]. Data sources that have been used to validate parking prediction models are: smart parking meters [15,18,20,21], mobile payments [8,22,23], intelligent parking systems [24], real-time ground sensors [14,17,25,26] images captured by a camera mounted on a moving vehicle [7,27], crowd-sensing information by equipping probe vehicles (e.g. taxis) with onboard sensors, cameras, or ultrasonic sensors [28,29], or crowdsensing using GPS signals from smartphones [23,[29][30][31], and also manual observations [32].…”

Adaptation and Learning to Learn (ALL): An Integrated Approach for Small-Sample Parking Occupancy Prediction

“…To tackle the aforementioned challenges, several solutions are proposed. First and foremost, in order to address data shortages, the main focus of some solutions remains on data collection and analysis to better interpret and infer the current and future parking statuses [12,13,26]. Examples include: (1) CoPASample [27] and BATF [28], which utilize heuristics-based covariance and Bayesian augmented tensor factorization, respectively, to generate synthetic samples that look close to the original data; and (2) WoT-NNs [14], which leverages the techniques of Web of Things (WoT) to collect additional information and incorporate them into neural networks.…”