What makes TEPs different !
Inner data transferability
Prediction even with scarce data
Prediction even with scarce data
A key for better prediction. TEPs feeds information from permanent monitoring sites to temporary sites for better traffic prediction
Prediction even with scarce data
Prediction even with scarce data
Prediction even with scarce data
TEPs uses a network-based interpolation technique to predict traffic volume for all roads, even for locations without monitoring sites.
Generalizability
Prediction even with scarce data
Make a spatial variability visible
TEPs can be used at any location and scale with minimum data requirement.
Make a spatial variability visible
Make a spatial variability visible
Make a spatial variability visible
TEPs generates maps of traffic volumes across an urban road network
Easy to use
Make a spatial variability visible
Fast and Reliable
TEPs has a very user friendly interface.
Fast and Reliable
Make a spatial variability visible
Fast and Reliable
TEPs can predict 10 years of traffic volumes for all roads in a metropolitan area in a single day
Overall Structure
TEPs structure for traffic volume prediction
TEPs-I is a chain of models for traffic volume prediction and consists of six sub-models including novel spatio-temporal disaggregation and aggregation sub-models (PECOUNT-I and PECOUNT-II), data pattern recognition model (PRTCS), advanced interpolation model (KCOUNTS), machine learning-based model (Local-SVR), and optimization model (OPTIM-STATIONS). Models at the top of chain feed information to lower level models.
TEPs performance
TEPs-I was used to predict periodic and annual volumes at the level of each individual roadway (more than 50000 roads) from 2007 to 2016 in the City of Toronto. Outputs of sub-models (PECOUNT-I & II; PRTCS; KCOUNTS and Local-SVR) were validated against observed traffic counts, indicating robust model performance.