Exploration of Traffic Area Segmentation on Aerial Imagery to Address the Parking Data Requirements of Travel Demand Models

Kurzfassung

Travel Demand Models (TDMs) are essential tools for predicting travel behaviour, helping decision-makers to evaluate transportation policies by simulating how people travel, where they go, and the modes and routes they choose. Although extensively studied, large-scale TDMs often lack a model for parking, or have a limited consideration of parking due to the absence of comprehensive parking data, despite its significant impact on travel behavior and traffic flow. This thesis addresses this gap by identifying the necessary data for integrating parking into TDMs to explore how aerial image datasets can fulfill these needs by serving as an additional input data source. The methodology includes a thorough literature review and interviews with transport modelers to identify the data demand, along with discussions with data providers in major German speaking cities to assess data supply, with the goal of identifying key parking data gaps. Using Berlin as a case study, the study attempts to extract the identified parking data from a traffic area segmentation dataset based on aerial imagery, developed by DLR, through geospatial analyses. The results reveal that modelers require more detailed parking data, including parking location, type, capacity, cost, occupancy, search time, and egress distance. Among these requirements, the aerial image dataset proved particularly useful for providing detailed information on parking location, capacity, and type of parking based on access. Through geospatial analysis, the study estimates 1.3 million parking spaces in Berlin, 55% of which are on-street and 45% off-street, marking the first statewide estimate of off-street parking capacity. Ultimately, this study contributes to making remote sensing data more accessible to a broader community, demonstrating its potential benefits for applications in the transportation domain.