Comparative analysis of the usage of free-floating carsharing between Berlin and Calgary
Bibliographic record
Abstract
Objectives: The purpose of this paper is to investigate possible reasons, based on economic and city characteristics, for the different usage of free-floating carsharing between a car-dependent city (Calgary) and one non-car-dependent city (Berlin). This paper identifies factors that help a free-floating carsharing system to be successful in a city that scores poorly on com-monly known success factors of carsharing. Methods: Various factors were evaluated, namely, geographic and demographic market char-acteristics, the available transport systems and the costs and household spending of both cities. A dataset which describes the usage of free-floating carsharing in Berlin and Calgary from Au-gust 2016 to November 2016 was analyzed in this study. Results: Calgary’s car2go system has fewer rentals and fewer members than Berlin. Possible reasons lie in the different city characteristics and different cost structures. Both 85th percentile of the travel distance is approximately as long as the radius of the respective home area in both cities. Thus, the median travel distance and the median reservation/rental duration is shorter in Calgary than in Berlin. The fact that more than 70 % of rentals in Calgary arrive in, depart from or travel within areas with active parking management could be due to the fact that free-floating carsharing users do not need to pay extra for parking fees. The carsharing bookings in Calgary peak at midnight when the public transportation service shuts down. The peak could also be the result of the high number of 3-minute long rentals at this time. Neither the high number of 3-minute bookings, the midnight peak, nor the public transport service close down during night, can be observed in Berlin. Given that employees in downtown Calgary may prefer to use free-floating carsharing to run errands during lunchbreak, the carsharing bookings do not plummet during midday, in contrast to Berlin, which only has a limited number of short distance rentals and where the free-floating carsharing bookings follow a similar pattern to the two-humped car traffic volume graph. Conclusion: Given the focus of the departures and arrivals in Calgary in areas where parking fees are charged, active parking management could be a success factor for free-floating car-sharing in car-dependent cities. However, it is not advisable to solely enforce parking fees within select parts of the home area as individuals generally prefer to use the less expensive mode of transport; which is free-floating carsharing to travel from and to areas with active parking man-agement and their own car for any other trip. As a result, the city would not gain the benefits free-floating carsharing could provide. Recommendations: Based on the results of this study, it is advisable to investigate whether home area wide or city wide parking management and surcharges for trips to downtown could encourage Calgary’s members in to use the car2go in a way that it provides the most benefits from a city perspective.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".