IMPACT STUDY OF GAP MANAGEMENT ON TRAFFIC
OPERATION THROUGH CONNECTED/AUTOMATED VEHICLES
Dr. Ping Yi, Professor, P.E., Director
Director, Center for Transportation Research
The University of Akron
ITITS 2015, Xi’an, China
December 12, 2015
Connected/Automated Vehicles
2
NHTSA Connected/Automated Vehicle Levels
(USDOT 2014)
3
CONNECTED/AUTOMATED VEHIECLES
• Driver control
– The driver maintains longitudinal and lateral control, with automated
assistance on other tasks.
• Partial/high automation
– The system controls longitudinal and lateral control; the level of
required driver monitoring and interfering changes from high to low.
• Full automation
– The system takes over longitudinal and lateral control. The vehicle is
capable of fully carrying out all tasks, including risk minimization,
without driver monitoring.
4
Technology Challenges (Hoogendorn, 2015)
5
Connected/Automated Vehicles
In the US -
6
Safety vs. Efficiency
Safety
No/minimum control;
slow, low volume
Automated vehicles for high
safety and high efficiency
Complex control;
Fast, high volume
Efficiency
Improving Traffic Operation
at Intersections
Safety Improvements –
• Emergency vehicle preemption
•
•
•
•
Pedestrian cross safety
Railroad crossing
Intersection collision avoidance
...
Operation Efficiency Improvements • Car following and headway control
8
Car-following/vehicle platooning
9
Improving Traffic Operation
at Intersections
Safety Improvements –
• Emergency vehicle preemption
•
•
•
•
Pedestrian cross safety
Railroad crossing
Intersection collision avoidance
...
Operation Efficiency Improvements • Car following headway control
• Start-up and change of phase delay reduction
10
Delays due to Signal Change
Arrivals/Departures
Change of
phase delay
Start-up
delay
Time
11
Improving Traffic Operation
at Intersections
Safety Improvements –
• Emergency vehicle preemption
•
•
•
•
Pedestrian cross safety
Railroad crossing
Intersection collision avoidance
...
Operation Efficiency Improvements • Car following headway reduction
• Start-up and change of phase delay reduction
• Side street entry optimization
12
Side street entry optimization
13
Improving Traffic Operation
at Intersections
Safety Improvements –
• Emergency vehicle preemption
•
•
•
•
Pedestrian cross safety
Railroad crossing
Intersection collision avoidance
...
Operation Efficiency Improvements •
•
•
•
Car following headway reduction
Start-up and change of phase delay reduction
Side street entry optimization
Dilemma zone minimization
14
Dilemma Zone Minimization
15
Improving Traffic Operation
at Intersections
Safety Improvements –
• Emergency vehicle preemption
•
•
•
•
Pedestrian cross safety
Railroad crossing
Intersection collision avoidance
...
Operation Efficiency Improvements •
•
•
•
•
•
Car following headway reduction
Start-up and change of phase delay reduction
Side street entry optimization
Dilemma zone minimization
Signal optimization and adaptive priority control
…
16
Priority Control and System Optimization
17
Improving Traffic Operation
at Intersections
Safety Improvements –
• Emergency vehicle preemption
•
•
•
•
Pedestrian cross safety
Railroad crossing
Intersection collision avoidance
...
Little work has been done
Operation Efficiency Improvements •
•
•
•
•
•
Car following headway reduction
Start-up and change of phase delay reduction
Side street entry optimization
Dilemma zone minimization
Signal optimization and adaptive priority control
…
18
Expected Capacity Gain
ℎ = (hr qr + haqa)/ (qr+qa)
Where,
ℎ is expected headway;
hr, qr and ha, qa are headway and volume of regular
and automated vehicles, respectively
Roadway Capacity
(veh/h)
3100
hcr1
hcr2
2800
hcr3
2500
2200
1900
50
200
350
500
Automated Vehicles
650
800
19
Application at Side Street Entrance
Stop sign or actuated signal (if warranted) is
commonly used at side street entrances.
20
Headway Distribution
For random arrivals, the expected probability Pcr for headways
greater than hcr can be determined as
Pcr = 2 ( 
hcr
q hcr
qe

1
2
q hcr
qe

1
2
q
) / hcr
2
Where, Pcr is the probability of gaps greater than selected hcr
q is the flow rate
21
Headways Distribution (Cont’d)
For headways in a range of interest, available gaps between an
automated vehicle and a regular vehicle (or between two
automated vehicles) can be estimated as

 qt
(1

e
)dt

N∆cr-AV = 3600
0

 qt
t
(1

e
)dt

Pcr2 – Pcr1
q
qr + qa a
0
where – qr and qa represent the volume of regular vehicles and
automated vehicles, respectively;
22
Usable Gap Creation by AVs
Gap to be created:
∆hcr
𝐿
𝐿
𝐿 1−𝛽
=
−
=
𝑉1
𝑉0
𝛽𝑉0
𝑉1 = 𝛽𝑉0
𝐿
𝛽=
𝐿 + t 𝑉0
Where,
L is the available roadway length for speed reduction
𝛽 is to be decided using the local site geometrics
23
24
AV Gaps vs. volume
250
AV Market Share
5%
200
AV Gaps
15%
35%
150
50%
100
50
0
500
550
600
650
700
Volume
750
800
850
25
AV Gaps vs. speed at 5% AV
25
Distance (ft)
20
700
1000
1200
AV Gaps
15
10
5
0
55
60
65
70
75
80
85
Vo (fps)
26
AV Gaps vs. speed at 40% AV
200
Distance (ft)
700
1000
1200
AV Gaps
150
100
50
0
55
60
65
70
75
80
85
Vo (fps)
27
Control Delay Change on Main vs. Side St.
60.00%
50.00%
Control Delay Reduction (%)
40.00%
30.00%
20.00%
10.00%
0.00%
450
500
550
600
650
700
750
800
850
Flow Rate (vph)
Control delay (sec/veh) Main st
Control delay (sec/veh) Side st
28
Capacity Increase on Main St.
(at 35% AVs)
30.0%
Capacity Increase (%)
25.0%
20.0%
15.0%
10.0%
5.0%
0.0%
450
500
550
600
650
700
750
800
850
Flow Rate (vph)
29
Dilemma Zone Reduction
at High-Speed Intersection
To go or stop if you see the yellow?
30
Dilemma Zone: Theoretical Boundary
v2
Cannot Pass zone  vt 
2 g ( f  G)
Cannot Stop zone  yv  (W  L)
31
Existing Practice
 Placing warning sign at the upstream of
the intersection
 Installation of red-light cameras to
discourage red-light running
 Extending the green time by using
actuated signal control
32
Actuated Control: Gap Out & Max Out
Gap-Out
Occurs when green time is terminated
when a passage time expires without
an additional activation
Max-Out
Occurs when the green is continuously
extended by arriving vehicles until the
max green duration is reached
33
Probability of Max out –
P(max out)  (1  e
MAH  T p  Tc
-qMAH
n
)
(Ld  Lv )

v
q – flow rate (veh/sec)
n – number of consecutive occurances
v – approaching speed (m/sec)
Tp – passage time (sec)
Tc – detector’s call-extension time (sec)
Ld, Lv – length of detector and vehicle, respectively (m)
MAH – maximum allowable gap (sec)
34
Probability of Max out (cont’d) –
nmax
Gmax

h
nmin
(Gmax  GQ  MAH)

h
1
1
1 qMAH 1
2
 qMAH
MAH  MAHe
 2e
 2
2
q
q
q
h
eqMAH 1
MAH 

q
q
Gmax – Maximum Green (sec)
GQ – Green time needed to clear the initial queue (sec)
h–
Average headway (sec)
35
Probability of Max out (cont’d) –
1.00
Probability of Max-Out
0.90
0.80
0.70
MAH = 2s,
Nmax
0.60
0.50
MAH = 2s,
Nmin
MAH = 4s,
Nmax
0.40
MAH = 4s,
Nmin
0.30
0.20
MAH = 6s,
Nmax
MAH = 6s,
Nmin
0.10
0.00
0
500
1000 1500 2000 2500 3000 3500 4000
Total Traffic Demand (vph)
36
Variations due to Traffic/Roadway Conditions
37
Impact of Speed Changes
38
Impact of Friction Changes
39
Three Scenarios of Analysis
1. Fixed detector location; fixed passage time –
Cannot handle C1 & C2 problems
2. Fixed detector location; allowing initial green & passage time
to change –
C1 problem remains although it improves C2, since
3. Automated Vehicles –
Both C1 & C2 are eliminated, and
40
Reliability Analysis (failure to protect DZ)–FOSM
For constant passage time –
 vo 2
2
2
 2  σ v To
β



2





 
 vo 2

2
 t o  vo 


σ

v
Cov(t,
v)

Cov(f,
v)


o
 v
2
 
f
g
2gf
o

o



2
 2 vo (W  Lo )  vo E(DS )
β


1
(W  Lo )2  2(W  Lo )E(DS )  E(DS )2  σ
2
β
2
l



 T
o



 Var(DS )  0
For variable passage time –
2
 2
vo
 σv  2

β


 To 2


2




 v o Cov(v,T)   t o  vo  σ v 2  vo Cov(t,v)  vo 2 Cov(f,v)

fo g 


2gf o


2
 To
v
E(D
)
S


 o2 (W  Lo )  Cov(v,T) 
vo
2


β
β


 2

vo 
2
2
2
Cov(v,T)
t

σ l  vo σ T  Var(DS )  2vo Cov(t,T)  2vo 
 o
fo g 




3
v
1
2

 o 2 Cov(f,T)  2 E(DS )  (W  Lo )  Cov(v,T)
β

gf o



 0



 41
Reliability Analysis (cont’d) –
1.0
Probability of Failure to Protect C1
0.9
0.8
0.7
Scenario 1, CV=0.1
0.6
Scenario 2, CV=0.1
0.5
Scenario 1, CV=0.3
0.4
Scenario 2, CV=0.3
0.3
Scenario 3
0.2
0.1
0.0
-0.1
0
10
20
30
40
50
60
70
80
Unprotected C1 Section of Dilemma Zone (m)
42
Reliability Analysis (cont’d) –
Probability of Failure to Protect C2
1.0
0.9
0.8
0.7
Scenario 1, CV=0.1
0.6
Scenario 2, CV=0.1
0.5
Scenario 1, CV=0.3
0.4
0.3
Scenario 2, CV=0.3
0.2
Scenario 3
0.1
0.0
-0.1
0
5
10
15
20
25
30
Unprotected C2 Section of Dilemma Zone (m)
35
40
45
43
Summary and Conclusions
• Capacity may be enhanced at side street entrance
via mainline gap management through AVs;
• Delay may be reduced at high speed intersection via
AVs to manage MAH for DZ minimization while
maintaining intersection safety;
• Benefits may be expanded through gradual
market share increase of AVs;
• Work has just started; more effort is needed to
look into the efficiency improvement due to AVs;
• Caution must be taken to minimize Impact on
regular vehicles; effects remain to be evaluated in
mixed traffic flow.
44
Questions ?
45