Prediction of the traffic incident duration using statistical and machine-learning methods
A systematic literature review
Bibliographic Data
| ID | 21402546 |
|---|---|
| Authors | Hüseyin Korkmaz (0000-0001-6345-7655, Istanbul University, corresponding author), Mehmet Ali Erturk (0000-0002-4030-1110, Istanbul University) |
| Year | 2024 |
| Volume | 207 |
| Pages | 123621 |
| Publication date | 2024-10-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Technological Forecasting and Social Change (JOURNAL) |
| Journal identifiers | ISSN: 0040-1625 • E-ISSN: 1873-5509 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.techfore.2024.123621 |
| OpenAlex | W4401477385 |
| Language | EN |
| References cited | 126 |
Duration (music) · Machine learning · Anomaly Detection Techniques and Applications · Artificial Intelligence · Computer Science · Traffic control and management · Traffic Prediction and Management Techniques
Support-Vector Networks
Induction of decision trees
A Fast Learning Algorithm for Deep Belief Nets
Bagging predictors
A new look at the statistical model identification
Comparing and Contrasting the Impacts of Macro-Level Factors on Crash Duration and Frequency
Prediction of Duration of Traffic Incidents by Hybrid Deep Learning Based on Multi-Source Incomplete Data
| Highly cited | No |
|---|