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Traffic&Transportation Journal

Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

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PUBLISHED
20.12.2023
DOI
https://doi.org/10.7307/ptt.v35i6.291
ISSUE
2023 (Vol 35), Issue 6
LICENSE
Copyright (c) 2024 Jelena SIMIĆEVIĆ, Nataša VIDOVIĆ, Vladimir ĐORIĆ
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This work is licensed under a
Creative Commons Attribution-NonCommercial-
NoDerivatives 4.0 International License.

Ordinal Regression Model of Parking Search Time

Authors:Jelena SIMIĆEVIĆ, Nataša VIDOVIĆ, Vladimir ĐORIĆ

Abstract

Parking search reduces the quality of parking service, as well as traffic network level of service, due to additionally generated traffic. Parking search also entails other negative effects, primarily ecological, social and economic. Even though the importance of this problem has been noted in the past, there is an impression that this issue has not been sufficiently researched and should be additionally analysed in order to properly understand this phenomenon. Therefore, the aim of this paper is to study the factors affecting parking search time that can be influenced through a set of parking management measures. In this paper, an ordinal regression model was developed to estimate these parameters and it was fitted using empirical data collected by interviewing drivers. Main model results show that parking occupancy has the highest impact upon the value of parking search time, indicating the significance of defining proper policies and measures aimed at reaching targeted parking occupancy. Parking frequency is the second parameter observed to be significant, demonstrating the importance of implementing proper parking information systems.

Keywords:parking search, parking policy, ordinal regression model

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