Mapping Bus and Stream Travel Time Using Machine Learning Approaches
Bibliographic record
Abstract
Collection of travel time data has always been a strenuous task, especially on Indian roads, due to the highly mixed traffic conditions and the absence of rigid driving characteristics. Travel time data collection methods such as on-board GPS devices and Wi-Fi scanners have their own feasibility issues. The GPS devices cannot be installed on all private vehicles, and Wi-Fi scanners cannot be set up at each and every road corridor. However, many transit agencies of Indian cities are installing GPS units in their public transport buses, making them a rich source of data. These buses travel all over the network and record the travel time and location information of vehicles at a certain interval, which can become the best and reliable source of traffic data under such conditions. The only limitation with this data is that the buses differ greatly from the rest of the vehicles in terms of vehicle characteristics, freedom of driving, effect of bus-stops, etc. So, a viable solution is to model and link the stream and bus travel times. Based on this, the present study proposes a method to map bus travel times with stream travel times using machine learning techniques, namely, Gradient Boosting Method and Support Vector Machines. Preliminary analysis showed that a nonlinear relationship exists between the travel times, and other factors like peak/off-peak hours, day of the week, etc. Results show that the proposed methods can efficiently map bus travel times with stream travel times with an average MAPE of 20% and perform better than existing approaches such as linear regression and artificial neural networks.
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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 | 0.000 |
| 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".