Traffic Estimation for Large Urban Road Network with High Missing Data Ratio
Abstract
:1. Introduction
2. Related Work
3. Model Formulation
3.1. Stochastic Compositional Traffic Flow Model
3.2. Measurement Model
4. Random Sets, Covariances and Variograms
4.1. Random Set
4.2. Covariance
5. Recursive Bayesian Estimation
5.1. Bayesian Estimation
5.2. Particle Filter
5.3. Missing Measurement Interpolation and Improved Likelihood Computation
Algorithm 1 Particle Filter for Traffic State Estimation with Kriging Estimated Measurements [24] |
|
5.4. Missing Data Estimation via Kriging Models
6. Performance Evaluation
6.1. Simulation Design
6.2. Results and Discussion
7. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Car | Bus | |
---|---|---|
Max speed | 25 m/s | 20 m/s |
Acceleration | 1.0 m/s | 0.8 m/s |
Deceleration | 4.5 m/s | 4.5 m/s |
Sigma (driver perfection) | 0.5 | 0.5 |
Length | 5 m | 10 m |
Minimum Separation | 2.5 m | 3 m |
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Offor, K.J.; Vaci, L.; Mihaylova, L.S. Traffic Estimation for Large Urban Road Network with High Missing Data Ratio. Sensors 2019, 19, 2813. https://doi.org/10.3390/s19122813
Offor KJ, Vaci L, Mihaylova LS. Traffic Estimation for Large Urban Road Network with High Missing Data Ratio. Sensors. 2019; 19(12):2813. https://doi.org/10.3390/s19122813
Chicago/Turabian StyleOffor, Kennedy John, Lubos Vaci, and Lyudmila S. Mihaylova. 2019. "Traffic Estimation for Large Urban Road Network with High Missing Data Ratio" Sensors 19, no. 12: 2813. https://doi.org/10.3390/s19122813
APA StyleOffor, K. J., Vaci, L., & Mihaylova, L. S. (2019). Traffic Estimation for Large Urban Road Network with High Missing Data Ratio. Sensors, 19(12), 2813. https://doi.org/10.3390/s19122813