phd research (yuan)
DESCRIPTION
TIL/T&P Masterclass presentation by Yufei Yuan on his PhD research on traffic state estimation. November 2010.TRANSCRIPT
12-11-2010
Delft University of Technology MasterClass T&P / TIL
What does it mean to be a PhD? —Experience & Current Research
Yufei Yuan, PhD Candidate
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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
PhDTraffic state estimation of prediction for road network traffic control
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What does it mean to be a PhD?Research
e.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.)
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0.Research scope and current research
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Research Scope
real traffic systemreal traffic system
traffic sensorstraffic
sensors
traffic actuators
traffic actuators
State estimation / data fusion
State estimation / data fusion
DTM measures
input: OD matrices, capacity constraints, network specs, etc
optimize
initial
state
goals
(a) state estimation
(b) state prediction
(c) optimization State
prediction
• Monitoring / state estimation
• State estimation / state prediction
• State prediction / optimization
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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 studyComparing with Eulerian case
Conclusions and Further research
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1.Brief overview
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Eulerian formulation of LWREulerian Coordinates
Coordinates
Kinematic wave model
Variables
Fundametal diagrams
(Daganzo, Smulders)
Numerical solution
(Mode switching)
No. of Vehices
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Eulerian CoordinatesLagrangian Coordinates
Leonhard Euler Joseph Louis Lagrange
How about Lagrangian coordinates?
Rencent Studies…
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Lagrangian CoordinatesLagrangian formulation of LWR
Coordinates
Kinematic wave model
Variables
Numerical solution An upwind scheme…(Next)
Fundametal relations
Position of Vehices
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Lagrangian CoordinatesLagrangian formulation
Numerical solution An upwind scheme [less non-linear]
Traffic characteristics only move in the same (downstream) direction(increasing vehicle number instead of space)
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2.New state estimator and application
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Traffic state estimation based on EKFEulerian Coordinates
Fundametal relations Observation (measurement) model
Discretized LWR model Process model
01
1 =Δ−
+Δ− −
+
xqq
t
it
it
it
it ρρ
the process model is highlynon-linear, hard to solve;mode-switching,large error (wrong sign)…
However,
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A new model-based EKF state estimatorLagrangian Coordinates
Fundametal relations Observation (measurement) model
Both Eulerian and Lagrangian sensing data are considered
(Explicit) Discretized Lagrangian model Process model
01
1 =Δ−
+Δ− −
+
nvv
tss i
tit
it
it
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Application to Freeway Traffic State Estimation [Essence]
The essence : to reproduce the freeway traffic conditions based on limited measurement data
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Traffic state estimator based on EKFAdvantage in Lagrangian Coordinates
A natural observation equation for floating car data
Implementation: more straightforward
Linearization: more accurate , ‘same’ sign (Differentiability)
Challenge in Lagrangian CoordinatesFormulating proper observation models for spatially fixed observations (Loop data) Solved!
Exactness: ‘less non-linear’, more accurate, less numerical diffusion
Modelling network discontinuity (complex)
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Challenge:Network Discontinuity
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3.Empirical and simulation study
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Empirical Studycomparing with Eulerian Case
Upstream in-flow known
M42 motorway in UKFull individual data
Same (speed)observations1. Lagrangian: FCD2. Eulerian: Loops3. Ground truth data Study area: downstream of onramp
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Empirical 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 its Eulerian counterpart by up to 24% for density and 75% for speed.
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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) cellsCurved lines: trajectories of vehicle groups
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Simulation Studywith Network Discontinuity
Von-Neumann out-flow condition
Upstream in-flow known
On-ramp & off-ramp flow known
Inflow
Driving direction Off-RampOrigin Destination
On-Ramp
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Simulation Studywith Network Discontinuity
To do:Further compared with Eulerian approachFOSIM synthetic data realistic
Node models in Lagrangian state estimator
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4.Preliminary conclusion and further research
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Preliminary conclusion
• Lagrangian state estimation out-performs Eulerian state estimation 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
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Further research directions
Comparing the performance of Lagrangian model with itsEulerian 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)
Developing more advanced Node Models and application