  Parking is an inevitable issue in the fast-growing developing countries.
Increasing number of vehicles require more and more urban land to be allocated
for parking. However, a little attention has been conferred to the parking
issues in developing countries like India. This study proposes a model for
analysing the influence of car users' socioeconomic and travel characteristics
on parking duration. Specifically, artificial neural networks (ANNs) is
deployed to capture the interrelationship between driver characteristics and
parking duration. ANNs are highly efficient in learning and recognizing
connections between parameters for best prediction of an outcome. Since,
utility of ANNs has been critically limited due to its Black Box nature, the
study involves the use of Garson algorithm and Local interpretable
model-agnostic explanations (LIME) for model interpretations. LIME shows the
prediction for any classification, by approximating it locally with the
developed interpretable model. This study is based on microdata collected
on-site through interview surveys considering two land-uses: office-business
and market/shopping. Results revealed the higher probability of prediction
through LIME and therefore, the methodology can be adopted ubiquitously.
Further, the policy implications are discussed based on the results for both
land-uses. This unique study could lead to enhanced parking policy and
management to achieve the sustainability goals.
