PhD research (Yuan)

413 views

Published on

TIL/T&P Masterclass presentation by Yufei Yuan on his PhD research on traffic state estimation. November 2010.

Published in: Education, Business, Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
413
On SlideShare
0
From Embeds
0
Number of Embeds
2
Actions
Shares
0
Downloads
10
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

PhD research (Yuan)

  1. 1. What does it mean to be a PhD?—Experience & Current Research12-11-2010 Yufei Yuan, PhD Candidate Delft University of Technology MasterClass T&P / TIL Leading to Doctoral Research… Master — T & P Internship: Urban & Inter-Urban traffic control scenario management Master Thesis: Coordination of ramp metering control in motorway networks PhD Traffic state estimation of prediction for road network traffic control MasterClass T&P / TIL 2 | 26
  2. 2. What does it mean to be a PhD? Researche.g. (in my case)Traffic state estimation and prediction for road network control Publishing results Papers Conferences/Journals Give or follow courses/workshops (TUD or TRAIL) Contract projects with other parties (B.V. or Gov.) MasterClass T&P / TIL 3 | 260.Research scope and current research MasterClass T&P / TIL 4 | 26
  3. 3. Research Scope real traffic system real traffic system traffic traffic (a) state estimation traffic traffic actuators actuators• Monitoring / (b) state prediction sensors sensors state estimation (c) optimization State State State initial prediction estimation / estimation /• State estimation / state data fusion data fusion goals state prediction optimize• State prediction / DTM input: OD matrices, capacity constraints, measures optimization network specs, etc MasterClass T&P / TIL 5 | 26 Current research & results — Lagrangian Traffic State Estimation for Freeways Brief overview Eulerian/Lagrangian formulation of LWR (first-order traffic flow model) Lagrangian state estimator and application Empirical and simulation study Comparing with Eulerian case Conclusions and Further research MasterClass T&P / TIL 6 | 26
  4. 4. 1.Brief overview MasterClass T&P / TIL 7 | 26Eulerian formulation of LWR Eulerian CoordinatesCoordinatesVariablesKinematic wave model No. of VehicesFundametal diagrams(Daganzo, Smulders)Numerical solution(Mode switching) MasterClass T&P / TIL 8 | 26
  5. 5. Eulerian Coordinates Lagrangian Coordinates How about Lagrangian coordinates? Rencent Studies… Leonhard Euler Joseph Louis Lagrange MasterClass T&P / TIL 9 | 26Lagrangian formulation of LWR Lagrangian CoordinatesCoordinatesVariablesKinematic wave model Position of VehicesFundametal relationsNumerical solution An upwind scheme…(Next) MasterClass T&P / TIL 10 | 26
  6. 6. Lagrangian formulation Lagrangian CoordinatesNumerical solution An upwind scheme [less non-linear] Traffic characteristics only move in the same (downstream) direction (increasing vehicle number instead of space) MasterClass T&P / TIL 11 | 262.New state estimator and application MasterClass T&P / TIL 12 | 26
  7. 7. Traffic state estimation based on EKF Eulerian Coordinates Discretized LWR model Process model ρ ti+1 − ρ ti qti − qti −1 + =0 Δt Δx Fundametal relations Observation (measurement) model However, the process model is highly non-linear, hard to solve; mode-switching, large error (wrong sign)… MasterClass T&P / TIL 13 | 26A new model-based EKF state estimator Lagrangian Coordinates (Explicit) Discretized Lagrangian model Process model s ti+1 − s ti vti − vti −1 + =0 Δt Δn Fundametal relations Observation (measurement) model Both Eulerian and Lagrangian sensing data are considered MasterClass T&P / TIL 14 | 26
  8. 8. Application to Freeway Traffic State Estimation [Essence]The essence : to reproduce the freeway traffic conditionsbased on limited measurement data MasterClass T&P / TIL 15 | 26 Traffic state estimator based on EKF Advantage in Lagrangian Coordinates Exactness: ‘less non-linear’, more accurate, less numerical diffusion Implementation: more straightforward Linearization: more accurate , ‘same’ sign (Differentiability) A natural observation equation for floating car data Challenge in Lagrangian Coordinates Formulating proper observation models for spatially fixed observations (Loop data) Solved! Modelling network discontinuity (complex) MasterClass T&P / TIL 16 | 26
  9. 9. Challenge: Network Discontinuity MasterClass T&P / TIL 17 | 263.Empirical and simulation study MasterClass T&P / TIL 18 | 26
  10. 10. Empirical Study comparing with Eulerian Case Upstream in-flow known M42 motorway in UK Full individual data Same (speed) observations 1. Lagrangian: FCD 2. Eulerian: Loops 3. Ground truth data Study area: downstream of onramp MasterClass T&P / TIL 19 | 26Empirical Study Figure: RMSE comparison between two methods for 8 simulation runs of scenario 200m-loop. Blue(E) Red(L)The most important observation:in all scenarios the Lagrangian state estimator out-performs itsEulerian counterpart by up to 24% for density and 75% for speed. MasterClass T&P / TIL 20 | 26
  11. 11. Empirical Study Figure: Snapshots of a small region from the whole x-t speed map for the Eulerian estimation (Left) and the Lagrangian estimation (right) Rectangles: discritized (calculation) cells Curved lines: trajectories of vehicle groups MasterClass T&P / TIL 21 | 26 Simulation Study with Network Discontinuity Off-Ramp On-Ramp Origin Driving direction Destination InflowVon-Neumann out-flow conditionUpstream in-flow knownOn-ramp & off-ramp flow known MasterClass T&P / TIL 22 | 26
  12. 12. Simulation Study with Network DiscontinuityNode models in Lagrangian state estimatorTo do:Further compared with Eulerian approachFOSIM synthetic data realistic MasterClass T&P / TIL 23 | 264.Preliminary conclusion andfurther research MasterClass T&P / TIL 24 | 26
  13. 13. Preliminary conclusion• Lagrangian state estimation out-performs Eulerian stateestimation more accurate estimates.• Both Eulerian & Lagrangian sensing data are well incorporated• Promotes the application of EKF (Solution to the mode-switching problem[upwind or downwind])• Validates the (elementary) node models MasterClass T&P / TIL 25 | 26Further research directions Developing more advanced Node Models and application Comparing the performance of Lagrangian model with its Eulerian counterpart at network levels (on/off ramp) Using different combinations of data sources Realistic data at network levels Implementing the method in a real traffic network (A10) MasterClass T&P / TIL 26 | 26

×