Energy‐Efficient Resource Allocation for Urban Traffic Flow Prediction in Edge‐Cloud Computing
Shenzhen University · Shenzhen Technology University · +6 more institutions
Abstract
Understanding complex traffic patterns has become more challenging in the context of rapidly growing city road networks, especially with the rise of Internet of Vehicles (IoV) systems that add further dynamics to traffic flow management. This involves understanding spatial relationships and nonlinear temporal associations. Accurately predicting traffic in these scenarios, particularly for long‐term sequences, is challenging due to the complexity of the data involved in smart city contexts. Traditional ways of predicting traffic flow use a single fixed graph structure based on the location. This structure does not consider possible correlations and cannot fully capture long‐term temporal relationships among…
Citation impact
- FWCI
- 42.36
- Percentile
- 100%
- References
- 39
Authors
7- AAAhmad Ali
Shenzhen University, Shenzhen Technology University
- IUInam Ullah
Gachon University, Tashkent State University of Economics
- SKSushil Kumar Singh
Marwadi University
- ASAmin Sharafian
Shenzhen University, Shenzhen Technology University
- WJWeiwei Jiang
Beijing University of Posts and Telecommunications
Topics & keywords
- Cloud computing
- Computer science
- Enhanced Data Rates for GSM Evolution
- Resource allocation
- Resource (disambiguation)
- Traffic flow (computer networking)
- Edge computing
- Distributed computing