A Novel Machine Learning-Based Approach to City Crime Sensor Placement Prediction
Bibliographic record
Abstract
The integration of advanced technologies can be up-and-coming for human-centered benefit. As the quality of life can be heavily affected by the safety standards of vehicular road traffic, the placement of appropriate traffic sensors can contribute to these standards. Collecting sensory traffic data involving location, speed, travel time, headways, and others is vital in designing traffic-resolving procedures. With such data, machine learning and data analysis tools are required to detect patterns and perform predictions. Through our research, we aim to investigate the potential of sensor data in predicting traffic crime hot spots using Machine Learning (ML) techniques, specifically, Random Forest, Long Short-Term Memory Network (LSTM), and Fourier Series Neural Networks (FSNN). Overall, we use three data sets, one with location codes, one with ticket outputs, and one demonstrating general crime rates in different locations within the City of Toronto. We run the models aiming for very low mean score error and the highest R2 score possible. Upon satisfaction with the initial evaluation, we run the model with only the unused portion of the crime rates data set to determine new traffic areas of interest for the sensors. This helps us develop sensory information to serve as traffic crime indicators and predictors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".