Muhamad Azfar Ramli
Biographic Data
| ID | 8886189 |
|---|---|
| NAME | Muhamad Azfar Ramli |
| GIVEN NAMES | Muhamad Azfar |
| FAMILY NAME | Ramli |
| SIGNATURE | RAMLI M A |
| AFFILIATIONS | Agency for Science, Technology and Research |
| ORCID | 0000-0002-6321-0828 |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 4 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2021 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Flow count data-driven static traffic assignment models through network modularity partitioning
Accurate static traffic assignment models are important tools for the assessment of strategic transportation policies. In this article we present a novel approach to partition road networks through network modularity to produce data-driven static traffic assignment models from loop detector data on large road systems. The use of partitioning allows the estimation of the key model input of Origin–Destination demand matrices from flow counts alone.…
Inferring unable-to-board commuters for overcrowded buses using smart card data
A spatiotemporal dynamic analyses approach for dockless bike-share system
Derivation of train arrival timings through correlations from individual passenger farecard data
No prominent works on this page.
A spatiotemporal dynamic analyses approach for dockless bike-share system
Derivation of train arrival timings through correlations from individual passenger farecard data
Inferring unable-to-board commuters for overcrowded buses using smart card data
Flow count data-driven static traffic assignment models through network modularity partitioning
Accurate static traffic assignment models are important tools for the assessment of strategic transportation policies. In this article we present a novel approach to partition road networks through network modularity to produce data-driven static traffic assignment models from loop detector data on large road systems. The use of partitioning allows the estimation of the key model input of Origin–Destination demand matrices from flow counts alone.…
Computer Science (4 works) · Transportation Planning and Optimization (4 works) · Engineering (3 works) · Human Mobility and Location-Based Analysis (3 works) · Transport engineering (3 works) · Mathematics (2 works) · Real-time computing (2 works) · Traffic Prediction and Management Techniques (2 works) · Urban Transport and Accessibility (2 works) · Algorithm (1 work)