Evaluation of Geospatial Features for Forecasting Parking Occupancy Using Social Media Data

Johannes Riedel, University of Rhode Island


Urbanization and growing individual mobility are globally active trends that intensify the needs for transportation in cities. In this context, parking space has become a scarce resource. Drivers searching for open parking spots cause about one third of the total traffic in urban areas. This creates significant fuel consumption, greenhouse gas emissions and time loss. Intelligent Transportation Systems with particular focus on parking are a promising approach to overcome the information asymmetry and lead drivers directly to available parking spots. This requires highly accurate occupancy data for parking areas on a geographically extended scale. The ultimate goal of this thesis is to improve the modeling of parking occupancy by extraction of meaningful features from raw data in social media. The research focus is set to points of interest and public events in urban areas. First, robust methodologies are developed for the acquisition and benchmarking of largescale social media data. This includes exploratory data analysis and testing of Facebook as a leading platform against alternative online data sources. Here, a multistage approach for the identification of duplicates in heterogeneous data sources is applied. Secondly, a diverse set of feature extraction methodologies is developed that integrates a variety of secondary data sources and findings in the literature. This comprises the adjustment of online popularity attributes for social media objects based on external data and the extraction of parking-related attributes based on text mining. Additionally, historical parking events from Floating Car Data are cross-referenced to thematic similarities among objects and adequate feature sets are derived. This includes the category-specific transformation of historical parking patterns into characteristic time- and object-dependent features. Also, text-based topic modeling using Latent Dirichlet Allocation is applied on social media data to extract thematic object similarities as probabilistic input features for parking demand modeling. In the final evaluation phase, ground truth occupancy data for a selection of off- and on-street locations is used to compare machine learning models trained with varying input feature sets. A baseline and extended set are compared while the latter includes extracted social media features. These models account for the prediction of parking occupancy over different timeframes. Random forest learning machines that include social media features are found to outperform the tested baseline models for both off- and on-street parking demand modeling. Particularly event topic probabilities and category-specific parking events on an hourly basis are identified to be valuable.

Subject Area

Geographic information science|Transportation|Artificial intelligence

Recommended Citation

Johannes Riedel, "Evaluation of Geospatial Features for Forecasting Parking Occupancy Using Social Media Data" (2017). Dissertations and Master's Theses (Campus Access). Paper AAI10615497.