Urban network-wide traffic volume estimation under sparse deployment of detectors

Sensing network-wide traffic information is fundamental for the sustainable development of urban planning and traffic management. However, owing to the limited budgets or device maintenance costs, detector deployment is usually sparse. Obtaining full-scale network volume using detectors is neither effective nor practical. Existing works primarily focus on improving the estimation accuracy using multi-correlation of networks and ignore the underlying challenges, particularly for these entire undetected road segments in sparse detector deployment scenarios. Here our study proposes a tailored transfer learning framework called the transfer learning-based least square support vector regression (TL-LSSVR) model. Network-wide volume can be estimated by fusing active detectors (taxi GPS data) and fixed passive detectors (license plate recognition data). Numerical experiments are carried out on a real-world road network in Nanjing, China. It is demonstrated that our approach achieves high performance even under sparse deployment of detectors and outperforms other baselines significantly.

Automated summary

Sensing network-wide traffic information is crucial for urban planning and traffic management, but sparse detector deployment poses challenges. To address this, a transfer learning-based least square support vector regression (TL-LSSVR) model is proposed, which utilizes active detectors (taxi GPS data) and fixed passive detectors (license plate recognition data) to estimate network-wide volume. Numerical experiments on a real-world road network in Nanjing, China demonstrate the high performance and superiority of the proposed approach compared to other baselines.