Carsharing customer demand forecasting using causal, time series and neural network methods: a case study
Bibliographic record
Abstract
Carsharing services are becoming popular in recent times. Deploying right number of fleet at stations is a critical component in assuring high quality service for customers. This can be done efficiently if customer demand is predictable or known in advance. In this paper, we address the problem of customer demand forecasting for improving carsharing operations. Three categories of methods namely causal (regression forecast, regression forecast with seasonality adjustments), time series (exponential smoothing, moving average) and neural networks are evaluated for forecasting customer demand. An application of the proposed methods on demand data from a carsharing organisation called Communauto is provided. The results of our study show that neural network is the best method in this prediction. The proposed work has strong practical applicability. Having an accurate forecast of the customers' demands in different times of the year can help increase customer satisfaction and reach business performance targets. Especially if electric vehicles are used in carsharing companies, since they require special infrastructures.
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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 | 0.000 |
| 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".