Classification tree analysis of factors affecting parking choices in Qatar

Qatar has experienced a significant population growth in the past decade. The growth has been accompanied by an increase in automobile ownership rates leading to parking problems especially in the capital city of Doha. The objective of this study was to find the factors affecting people's choice of parking in this rich developing country when different parking options are available. Two commercial centers located in the city of Doha, Qatar were selected for this study; the City Center mall and the Souq Waqif shopping center. Each location has two different parking options available. Parking options vary in many features including distance to destination, paid/free, covered/open, paved/unpaved, and guarded/unguarded. In addition, the parking options also differed in the ITS infrastructure deployed in the form of intelligent parking space detection system to assist visitors to navigate to an available spot. A survey was handed out to randomly selected visitors at the main entrance of each of these shopping areas to obtain a random sample of study participants. Binary classification tree models were developed to understand the factors associated with binary parking choices at both of these commercial centers. In addition to the demographic factors associated with the parking choice; the reasons for choosing a particular parking option were also explored through the survey. The analysis of survey data presented herein provides an interesting insight into parking choices of the visitors that can be used in planning future parking facilities sand managing existing parking locations. Among the reasons cited by respondents for making their parking choices, “Intelligent Parking Space Detection” was chosen as one of the factors affecting people's choice of parking significantly more often than amenities such as “Wider parking spot”. The findings indicate that future parking investments may be better directed towards smart parking solutions.


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  • Accession Number: 01605471
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Jun 20 2016 11:32AM