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The research project “start2park” closes a research gap by precisely measuring parking search duration (cruising for parking) – especially the starting point of search – using a mobile app developed for this purpose. Complete journeys’ location data and durations are recorded, including driving until the start of the parking search, the parking search process, and the footpath from the parking spot to the final destination. Therefore, the causal effects of parking search on driving duration as well as journey duration can be estimated. Cruising for parking is traffic that results from car drivers looking for (free) kerb parking that meets their expectations (for example, free of charge or close to their destination point) and drivers being not (fully) informed about available kerb space parking locations. Parking search traffic causes external costs. Therefore, traffic-planning options should be designed to reduce unnecessary parking search traffic. However, this requires reliable data on urban cruising for parking traffic. Previous empirical results on the share of cruising traffic in total traffic, average parking search durations and average parking search distances differ widely. We show that the causal effect of parking search on driving duration and journey duration has not yet been validly estimated in empirical studies, and we explain how this is done in the research project.
Abstract English
Urban area tessellation is a crucial aspect in many spatial analyses. While regular tessellation methods, like square-grid or hexagon-grid, are suitable for addressing pure geometry problems, they cannot take the unique characteristics of different subareas into account. Irregular tessellation methods allow the border between the subareas to be defined more realistically based on the urban features like road network or POI data. This paper studies and compares five different tessellation methods: Squares, hexagons, adaptive squares, Voronoi diagrams, and city blocks. We explain how (open-source) POI data can be integrated into the tessellation process to build what we call “Local Geo-graphic Units” (POI-based tiles). These units are flexible and adaptable to the structure of the studied area and underlying data and could improve the performance of further analyses. The results of the various tessellation methods are demonstrated for the city of Frankfurt am Main in Germany. A simple clustering of Local Geographic Units for the studied city indicates that city blocks perform better than the other methods in the city segmentation in terms of reflecting the structure of this city.
Abstract Deutsch
Die Tessellierungen urbaner Gebiete ist ein entscheidender Aspekt bei räumlichen Analysen. Regelmäßige Tessellierungen, wie die Unterteilung in Quadrate oder Hexagons, eignen sich zwar für Probleme rein geometrischer Natur, berücksichtigen aber die Charakteristika der enthaltenen kleineren geographischen Einheiten nicht. Unregelmäßige Tessellierungen ermöglichen eine realitätsnahe Unterteilung basierend auf städtischen Merkmalen, wie dem Straßennetz oder POI-Daten. In diesem Beitrag werden fünf verschiedene Tessellierungsmethoden vorgestellt und verglichen: Quadrate, Hexagons, adaptive Quadrate, Voronoi-Diagramme und City-Blocks. Die Integration von (Open-Source) POI-Daten in den Tessellierungsprozess führt zu sogenannten „Lokalen Geographischen Einheiten“. Diese POI-basierten Einheiten sind flexibel und passen sich sowohl der Struktur des zu untersuchenden Gebiets, als auch der zugrundeliegenden Daten an und erlaube dadurch darauf aufbauende, detailliertere Analysen. Alle vorgestellten Tessellierungsmethoden werden an dem Beispiel Frankfurt am Main durchgeführt und präsentiert. Ein einfaches „Clustering“ der Lokalen Geographischen Einheiten zeigt, dass City-Blocks die Struktur der Stadt besser abbilden können, als die anderen vorgestellten Methoden.