The Impact of Ride-Hailing Services on Private Car Use in Urban Areas: An Examination in Chinese Cities
Bibliographic record
Abstract
The rapid development of internet-based ride-hailing services has contributed to transportation in cities and, at the same time, has significantly impacted existing travel modes in cities. A question has emerged as to whether and to what extent ride-hailing services replace private car use. Although the private car is convenient, comfortable, and flexible, it has low utilization rate and high maintenance and parking costs. Better understanding of the relationship between ride-hailing services and the use of private cars has been brought to the forefront for auto dealers and urban transportation policymakers. However, controversies remain regarding how ride-hailing services will impact the use of private cars in cities. Given this setting, our study applied a difference-in-differences method to analyze the impact of ride-hailing services on the use of private cars with balanced panel data from 109 prefecture-level cities in China from 2010 to 2016. Moreover, we employed some methods to verify the robustness of the preliminary results. The empirical results show that ride-hailing services had a negative impact on the use of private cars in urban areas. Over time, the negative impact initially strengthened and then weakened. Further studies showed that ride-hailing services had a more significant negative impact on private car use in eastern cities than in western cities. The results showed that the influence of ride-hailing services on private car use in urban areas is heterogeneous across time and cities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".