a dynamic traffic assignment model for the gta...2014/12/11 · a dynamic traffic assignment model...
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A Dynamic Traffic Assignment Model for the GTA
Hossam Abdelgawad, Ph.D., P.Eng
Islam Taha, PhD Student
Aya Aboudina, PhD Candidate
Baher Abdulhai, Ph.D., P.Eng.
STA and DTA
In a network with many OD zones and a time period of interest, for each OD pair and departure time, all used routes have equal and lowest experienced travel time (generalized cost). No user may lower his experienced travel time through unilateral action (deterministic DTA).
In a network with many OD zones, for each OD pair, all used routes have equal and lowest travel time (generalized cost). No user may lower his travel time through unilateral action (deterministic STA).
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Experienced vs Instantaneous Travel Time
3
2
2
1
1
4
3
3
2
1
4
6
5
3
1
Link travel time
when entering the
link at each minute
Travel time for departing at min 1 = 3 min
Travel time for departing at min 2 = 6 min
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(a) Instantaneous travel time calculation
3
2
2
1
1
4
3
3
2
1
4
6
5
3
1
Link travel time
when entering the
link at each minute
Travel time for departing at min 1 = 9 min
Travel time for departing at min 2 = 8 min
4
(b) Experienced travel time calculation
3
Why / When do you need Dynamic ?
Captures the onset and spread of congestion over space and time
Essential to design control strategies:
– Ramp Metering
– Dynamic Congestion Pricing
– Traffic Signal Control
– Evacuation Optimization
– ….etc.
4
14 16 18 20
Time of day (hour)
Speed (
km
/hr)
Base Case (Jameson Closed)
20
40
60
80
100
Applications – Mesoscopic Level Evacuation of City of Toronto
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Optimized Evacuation of Toronto 2.25 M People
6
Avg Evacuation Time = 2 Hrs Network Clearance Time= 8 Hrs Avg Stop Time = 0.5 Hrs
Avg Evacuation Time = 7 Hrs Network Clearance Time= 30 Hrs Avg Stop Time = 6 Hrs
Op
tim
al
Pla
n (
OS
TE
)
Do
-No
thin
g
Calibration
Verification
Overall Modelling Approach
Input Data
….
….
……
DTA
Model
Parameters
Validation
Outputs Observed
Data
7
Verification of Input Data
Consistency, volume balance (multiple sources of data)
Volumes and Speeds (congested or uncongested)
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A D
B
C
E
F
D = A+B+C D= E+F
Speed
Flow Capacity
Field Data
Model Output
High
Low
Notes on Demand Data
Capacity vs Demand
Time-dependent demand (demand profile)
9
Flo
w
Time
Capacity S
tart
poin
t o
f q
ue
ue
En
d p
oin
t o
f q
ue
ue
Analysis Period (say AM Peak from 7 am to 9 am)
Warm-Up
Period
Demand profile
Demand
Calibration: Iterative Process
10
Parameter Tweaking
Error Checking
Calibration
Behavior
Model
Demand
Observation and
Feedback
Model Capacity
Model Scope: GTA
11
Demand Shifting from GTHA to GTA
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Without GTHA time shifting demand
Calibration Criteria
13
Example:
a) v= 10000, c = 9000 % Diff =10%
b) v= 1000, c=900 % Diff =10%
Example:
a) v= 10000, c = 9000 GEH=10.3
b) v= 1000, c=900 GEH=3.2
GEH < 5 (Perfect)
5 < GEH < 10 (acceptable)
10 < GEH < 15 (question data quality or model performance)
Calibration Results (Morning Peak)
14
0
1000
2000
3000
4000
5000
6000
7000
8000
Ho
url
y V
olu
me (
veh
/ h
r)
Volumes along HWY401-EB-Col Observed (lower limits) Observed (upper limits) Simulated
Calibration Results (Morning Peak)
15
0
20
40
60
80
100
120
140
Ho
url
y V
olu
me (
veh
/ h
r)
Speeds along HWY401-EB-Col Observed (lower limits) Observed (upper limits) Simulated
Calibration Results (Morning Peak)
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within limits
Avg GEH = 12.8
HWY 401
QEW
DVP
HWY 404
HWY 400
HWY 410
HWY 403
Gardiner Exp
LakeShore Blvd
