An Empirical Study on the Segmentation of Potential Users of Shared Parking Spaces considering Individual Heterogeneity
Bibliographic record
Abstract
Shared parking has become the most effective way to utilize existing parking resources. Little attention has been focused on drivers’ intention to use shared parking spaces in residential areas considering individual heterogeneity. To fill this gap, this paper explores the influencing factors and mechanism of shared parking use intention (SPUI) and further studies the preferences for the shared parking of different types of drivers. Firstly, based on the extended unified theory of acceptance and use of technology that includes psychological factors, personal attributes, and travel characteristics, the multiple indicator multiple cause (MIMIC) model was employed for parameter estimation and model assessment. Secondly, using MIMIC’s output results as input variables, the segmentation method of the latent class model (LCM) was adopted to explore drivers’ preferences regarding SPUI. Finally, a quantitative study was carried out through questionnaire data. The empirical results show that: (a) the extended unified theory of acceptance and use of technology has good explanatory power for SPUI. SPUI is directly affected by perceived risk (PR), behavioral habit (BH), social influence (SI), facilitating conditions (FCs), and effort expectancy (EE), while performance expectancy (PE) have no significant effect on SPUI. In addition, some factors of personal attributes and travel characteristics affect SPUI through psychological factors. (b) According to individual heterogeneity, the surveyed driver groups are divided into four segments: sensitive type (36%), conservative type (29.6%), neutral type (24.5%), and approved type (9.9%), respectively. There are significant differences in psychological observation variables such as EE, PE, FC, and SI among the four segments of drivers. According to the influence mechanism of psychological factors and preferences analysis of different types of drivers, the shared parking promotion strategy can be formulated from the aspects of management, operation, and technology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".