Modeling Car Drivers' On-Street Parking Decisions using the Integrated Hierarchical Information Integration Approach

Abstract

Rising issues in urbanization and transportation urge municipalities to optimize the use of on-street parking spaces in order to meet local needs and complement the role of available off-street parking. In this paper car drivers' parking decisions have been investigated using a stated choice experiment, based on the method of integrated hierarchical information integration. According to this approach, a large set of parking related attributes and attribute levels are grouped into two higher order decision constructs, which are presented as hypothetical street segments. The respondents were asked if they would park their car in the street segment with the listed attributes. The collected data is used to estimate the parameters of a standard multinomial logit model. This study differs from previous studies as a large range of attributes is examined, including the parking situation and the road conditions in a street segment along with some features of off-street parking facilities present in the vicinity of the street segment. The results indicate that the contextual variables such as 'walking distance to destination' and 'parking cost' are key attributes that car drivers consider while making on-street parking decisions, while street-level attributes such as 'occupancy,' 'security,' and 'surrounding activities' seem to have only a minor impact. The study concludes with an outlook of how these insights into car drivers' parking choice process can be used by local authorities to reduce cruising in urban areas. Moreover, these findings can be integrated in multi-agent systems to investigate car drivers' movements in urban areas.