Upload
nakia
View
18
Download
0
Tags:
Embed Size (px)
DESCRIPTION
Dynamic Origin-Destination Demand Flow Estimation under Congested Traffic Conditions. Xuesong Zhou (Univ. of Utah) Chung-Cheng Lu ( National Taipei University of Technology ) Kuilin Zhang ( Argonne National Lab ) Presented at INFORMS 2011 Annual Meeting. Motivation. - PowerPoint PPT Presentation
Citation preview
Dynamic Origin-Destination Demand Flow Estimation under Congested
Traffic Conditions
Xuesong Zhou (Univ. of Utah)Chung-Cheng Lu (National Taipei University of Technology)
Kuilin Zhang (Argonne National Lab)
Presented at INFORMS 2011 Annual Meeting
1
Motivation
Why existing dynamic OD estimation methods are difficult to produce desirable results under congested conditions, when using link-flow proportions.
3 Difficulties in the past our new methods:
1. Partial derivatives with respect to path flow perturbation2. Single-level path flow estimation framework with gap
function term
2
Literature Review
• Bi-level framework – Yang et al. (1992); Florian and Chen (1995)
• Solution algorithm – Iterative estimation-assignment (IEA) algorithms– Sensitivity-analysis based algorithms (SAB)
3
Iterative Estimation-Assignment Method
• Upper level Constrained ordinary least-squares problem
s.t. non-negativity constraints Lower level:– Link flow proportion = Dynamic traffic assignment ( ))– Solution procedure
),,(),,(ˆ jihlp ),,( jid
tLl
tlji
jijitllc
cdpZ,
2),(
,,),,(),,(),,( ]ˆ[min
Dynamic OD Demand
Dynamic Traffic Assignment
T im e
Dem
andFlow Pattern
Dynamic OD Demand Estimation
T im e
Link
flow
Link Proportions
Measurement Equations cl,t=Σi,j,tp(l,t)(i,j,t) × di,j,t+
Difficulty in IEA Algorithms
Upper-level optimization model does not consider the dependence of link-flow proportions on the OD flows. = function(d)
5
Optimization problem
Traffic observations
Dynamic Network Loading/
DTA Simulator
Dynamic OD demand matrix
Convergence
Link-flow proportions
No
Yes
Consistent dynamic OD demand matrix
),,(),,(ˆ jitlp
Sensitivity-Analysis Based (SAB) Algorithms
• Approximate the derivatives through simulation • for each OD pair and each time interval in every iteration (Tavana, 2001)• Gradient approximation methods
– Simultaneous Perturbation Stochastic Approximation (SPSA) framework by Balakrishna et al. (2008); Cipriani et al. (2011)
• Difficulty: Computationally Intensive– Does not simultaneously achieve user equilibrium
and minimize measurement deviations
6
Difficulty 3: How to Utilize Density/Travel Time Measurements
Automatic Vehicle Identification Automatic Vehicle LocationLoop Detector Video Image Processing
Point Point-to-pointSemi-continuous path trajectory
Continuous path trajectory
Our Approach: Use Spatial Queue Model to evaluate partial
derivatives with respect to path flow perturbation
8Inspired by study by Ghali and Smith (1995)
Case 1: Partially Congested Link
9
Link inflow and outflow increase by 1 at two time stamps:entering time and end of queue duration, respectively.
Case 1: Partially Congested Link
10
Link density (number of vehicles) increases by 1 between two timestamps: entering time, end of queue duration.
Case 1: Partially Congested Link
11
The flows arriving between two time stamps experience the additional delay 1/c, because it takes 1/c to discharge this perturbation flow (similar to the results by Qian and Zhang 2011)
Case 2: Free-flow Conditions
12
Number of vehicles (i.e., link density) increases by 1 from entering time to leaving time.
Case 2: Free-flow Conditions
13
Link inflow and outflow increase by 1 at entering time and leaving time, respectively.
Case 2: Free-flow Conditions
14
Individual travel times are not changed (= free flow travel time, FFTT)
Case 3: Two Partially Congested Links
The perturbation flow on the second link starts at the end of queue duration of the first link; rather than the vehicle entering time on second link
15
Here!
Not Here!
Similar work by Shen, Nie and Zhang (2007) for path marginal cost analysis
Case 4: Queue Spillback
16
Individual extra delay depends on when the vehicle/perturbation flow joins in the queue.
Beyond A/D Curves: How to Model Queue Spillback?
• Forward and backward wave representation in Newell’s simplified kinematic model
17
Time axis
( )( )b
length bBWTT bw
( )( )f
length aFFTT av
Time t-1
Spac
e axi
s
Link b
Link a
A(b,t-1)
D(b,t-BWTT(b)-1)
Our Method to Overcome for Difficulty 1
• Derive analytical, local gradients of different measurement types, with respect to flow perturbation – link flow, density and travel time
• Valuable gradient information considers the dependences of link flow/density/travel time changes on OD flows
18
1. Path flow adjustment Min
(1) deviation between measured and estimated traffic states
(2) the deviation between aggregated path flows and target OD flows
S.T. dynamic user equilibrium (DUE) constraint
2. Aggregate path flows over all paths demand flows
Now move to Challenge 2:Path flow Estimation Framework
19
Demand flow target demand path flow target demand
20
Quick Review: Single-level OD Estimation
• Linear programming PFE by Sherali et al. (1994)
• Nonlinear programming PFE by Bell et al. (1997) on estimating stochastic UE path flows
• Nie and Zhang (2008): single-level formulation based on variational inequalities– Qian and Zhang (2011) further incorporated the travel time gradients
• Nie and Zhang (2010), and Shen and Wynter (2011) integrated the integral term in Beckmann’s UE formulation (1956) with the measurement deviations
Step 1: Lagrangian Reformulation
• Describe the DUE constraint based on a gap function– DUE Gap
• Dualize DUE constraint to the measurement deviation function with a non-negative (Lagrange) parameter – Measurement deviation function Z(r), including link flow,
density, and travel time
21
g(r, ) = wp{r(w,,p)[c(w,,p)(w,)]}.
Minr, , L(r, , ) = z(r) + [g(r, ) ]
Step 2: Gradient Based Algorithm
Individual gradients with respect to path flow adjustment
22
Adjust path flow on each path based on generalized gradient/Cost
Calculated based on the spatial queue model
Flowchart of the Algorithm
23
Path flow adjustment based on all gradients
Our Contribution for Challenge 2• New path flow-based optimization model for jointly
solving the complex OD demand estimation and UE DTA problems
• Simultaneous route and departure time user equilibrium (SRDUE) problem with elastic demand
• Final solution is a set of path flow patterns satisfying “tolled user equilibrium” (Lawphongpanich and Hearn, 2004)
24
Numerical Experiment No.1
Path FFTT (min)
Capacity (vhc/hr)
Assigned Flow (vhc/hr)
Travel Time (min)
Path 1 20 3000 5400 56
Path 2 30 3000 2600 56
25
Congested two-link Corridor: Total capacity = 6000 vhc/hourTotal demand = 8000 vhc/hour
Upper Bound and Lower Bound of Objective Function
26
0 1 2 3 4 5 6 7 8 9 10 11300000
320000
340000
360000
380000
400000
420000
d=7334
d=7335d=7336 d=7338 d=7347
d=7350d=7353 d=7436
Upper BoundLower Bound
Lagrangian multipler
Obj
ectiv
e fu
nctio
n
Path Flow Volume Convergence Pattern
27
0 2 4 6 8 10 12 14 16 18 200
1000
2000
3000
4000
5000
6000
7000
8000
9000
Ground-truth total demand
Estimated total demand
Ground-truth flow on route 1
Estimated Flow on route 1
Estimated Flow on route 2
Ground-truth flow on route 1
Iteration
Flow
Vol
ume
Path Travel Time Convergence Pattern
28
0 5 10 15 2040
45
50
55
60
65
70
Estimated travel time on route 1Estimated travel time on route 2User equilibrium travel time
Iteration
Tra
vel T
ime
(min
)
Robustness of Our Algorithm under Different Input Conditions
Information Availability Estimation ResultVolume observations on path 1 only
Volume observations on path 2 only
Error-free target demand,8000vhc/hr
Error-free travel time on path 1
Flow on path 1
Flow on path 2
Total estimated demand
Equilibrium travel time (min)
X 5051.7 2367.8 7419.5 53.7X 4967.7 2311.8 7279.4 53.1
X X 5011.8 2341.2 7353.0 53.4X X X 5387.9 2592.0 7979.9 55.9X X X X 5401.1 2600.7 8001.8 56.0
29
Experiment 2• A 2-mile section of I-210 Westbound, located in Los
Angeles, CA
30
33.049 ml 32.199 ml
761342
718206
764146
717107
32.019 ml
717669
716604 717668
30.999 ml
717664a
on on onoff
Sensor ID
Postmile
b c d
e f h i
0 10 20 30 40 50 60 70 80 90 100 110 120 1300
200400600800
100012001400160018002000
Density (vhc/ml/lane)
Flow
vol
ue (v
hc/h
our/
lane
)
This is a Congested Corridor…
31
6:00 AM 7:00 AM 8:00 AM 9:00 AM 10:00 AM0
102030405060708090
D C
B
Time
Spee
d (m
ph)
33.049 ml 32.199 ml
761342
718206
764146
717107
32.019 ml
717669
716604 717668
30.999 ml
717664a
on on onoff
Sensor ID
Postmile
b c d
e f h i
Observed Lane Volume vs. Estimated lane Volume on Entrance Link
32
6:00 AM 7:00 AM 8:00 AM 9:00 AM 10:00 AM0
200400600800
100012001400160018002000
Observed lane volume
Estimated lane volume
Time
Lan
e vo
lum
e (v
hc/h
our)
Observed vs. Estimated Speed on Link from Off-ramp h to Station c
33
6:00 AM 7:00 AM 8:00 AM 9:00 AM 10:00 AM0
10
20
30
40
50
60
70
Observed speedEstimated speed
Time
Spee
d (m
ph)
Preliminary Experiment: A Real-world Traffic Network
34
858 nodes2,000 links208 zones
0 2 4 6 8 10 12 14 16 18 200
5
10
15
20
25
30
Iterations of Path Flow Adjustment
Link
Den
sity
Est
imat
ion
Err
or
(veh
icle
s/m
iles/
lane
)
Conclusions
1. Single-level, time-dependent OD demand estimation formulation, without using link proportions
2. A Lagrangian relaxation solution framework
3. Gradient-projection-based path flow adjustment process
4. Derive theoretically sound partial derivatives of link flow, density and travel time with respect to path flow perturbations
35
36
Historical OD demand
Path flow decomposition
Traffic Link CountOccupancy profileSpeed profileBluetooth records
Path flow vector 1
Path flow vector 2
Path flow vector 2
…
Measurement deviation based rapid gradient calculation generation
Gradient-based path flow adjustment
Gap function-based equilibration New path flow vectors
Convergence detection