Towards Sustainable Urban Car-Parking Solutions: Exploring Effects of Parking Policies Using Spatial Regression Analysis

Abstract

In urban policy development and transport planning, car parking has become a crucial topic, as an ill-designed system could lead to traffic congestion, safety problems, increased air pollution and other challenges, particularly in densely populated urban areas. Adjusting parking fees has been deemed the most effective tool to control parking behaviour for selected locations, in order to attract or repel drivers. However, in order to select target areas efficiently, and before any recommendation for fee adjustment is made, an analysis of the current parking situation and the identification of variables influencing parking behaviour are needed. We present a spatial analysis targeted at parking demand and supply data gathered via road surveys by local authorities in Taipei City. The spatial analysis is complemented by a regression analysis to identify variables that influence parking behaviour. Our approach examines the relationship between potential controlling and contributing factors, and we show the influence of variables in a specific example. Our aim is to provide a starting point for future policy development and pricing adjustment at a local level. This initial framework might provide a conceptual core for wider discussion and a tool for integrating other different scenarios in the future.