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Parking Survey Design Using Gamification

EasyChair Preprint no. 7563

17 pagesDate: March 13, 2022


We present a survey methodology to collect parking behavior data. The survey is a game that exposes respondents to a wide range of hypothetical parking scenarios including information availability (on parking spots) and the possibility of illegal parking. The respondents make parking choices by playing the game. We use incentive mechanisms from the gamification literature to replicate real-world decisions. The survey can overcome choice set deficiencies of conventional methods of revealed and state preference surveys (e.g., unavailable choice sets in revealed preference and large choice sets in stated preference). We record detailed travel decisions throughout the parking process and use the data to develop choice models. The Mixed logit model demonstrates the significance of parking preference heterogeneity. According to the model, respondents are more likely to park legally when they are provided full information about the status of parking spots. Moreover, male respondents are more likely to park illegally.

Keyphrases: discrete choice model, ITS, mixed logit model, multinomial logit model, nested logit model, on-street parking, Parking, Parking choice model, Parking Information System, parking policy, Parking Reservation System, simulation survey, stated preference survey

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Bo Wang and Mehdi Nourinejad and Orlando Bascuñán and Matthew Roorda},
  title = {Parking Survey Design Using Gamification},
  howpublished = {EasyChair Preprint no. 7563},

  year = {EasyChair, 2022}}
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