On Generating Real-Time Traffic Statistical Profiles
Based on Helicopter Video Data for Traffic Monitoring,
Management and Emergency Response
Kimon Valavanis
Project team: Dr. P. S. Lin / PhD Students: A. Puri, M. Kontitsis, R. Garcia, D. Ernst
SWAN’06
Objectives
•
Collect real-time traffic video data over an intersection, road segment,
highway segment, specific traffic network using single / multiple unmanned
helicopters.
•
Store data on-board for evaluation, analysis, etc.
•
Transmit data to the traffic control centers (ground control stations) for onthe-spot / immediate decision making when necessary, as well as traffic
signal timing modifications, re-routing, emergency response, etc.
•
Convert real-time collected data to statistical profiles, to be used as inputs
to traffic simulation models, aiming at improving their accuracy,
predictability, parameter calibration, etc..
Goals
• Real-time, dynamic:
–
–
–
–
–
–
traffic monitoring
traffic network management
optimal traffic signal management
optimized traffic flow and rerouting
minimized emergency response time
improved resource/asset allocation in emergencies
• Improve:
–
–
–
–
traffic simulation models
model accuracy
calibration
predictability
The Problem
• Lack of accurate, detailed data;
• Historical or distribution-based data used to calibrate simulation
models;
• Inability to adjust model parameters in real-time;
• Calculating density, turning movement is extremely hard using
conventional methods.
The Proposed Solution Considers:
• Real-time “eye-in-the-sky” detailed video data;
• Every traffic network (segment of traffic network) has its unique
characteristics (for example downtown peak-hours differ from
campus peak-hours);
• Ability to update simulation model in real-time (especially important
in case of incidents or events);
• Performance measures can be easily observed;
• Ability to predict traffic patterns using real-time data.
Equipment
• Raptor 90 SE/Generation I
Controller Box
Needed:
– Safety Switch for Autonomous
Operation
– 5 Hz GPS
– IMU
– Stabilized Camera Platform
– Higher Performance Computer
System
– Better Vision Capabilities
– Cleaner, More Efficient
Operation
– Removable, Easy to
Reconfigure Boot Device
Equipment
• Raptor 90 SE/Generation I Controller Box (continued)
Equipment
• Emaxx/Generation I
Controller Box
Needed:
–
–
–
–
–
–
–
IMU
Faster Processing
Safety Switch
Better Ground Clearance
Better Vision
Pan/Tilt Unit
5 Hz GPS
Updated Equipment
• Generation II Controller Box
Includes:
– 2 Ghz Intel Pentium M
Processor
– 2 GB Memory
– 5 Hz Superstar II GPS
– Microstrain 3DM-GX1 IMU
– Microbotics Servo Controller
with Safety Switch
– Pico Power Supply Unit
– Four Port Video Capture Card
– USB Boot
Updated Equipment
• Maxi Joker 2/Generation II
Controller Box
Includes:
– Fully Electric Helicopter
• Quiet Operation
• No Mess
• Easy and Fast Set-up
–
–
–
–
Custom Shock Mount Skids
Shock Mounted Pan/Tilt
Double Shock Mounted IMU
Sony Block High Resolution
Camera with Zoom Capabilities
– Separate Power for Safety
Switch
– Full Autonomous Capabilities
– Wireless Video Transmission
Updated Equipment
• Maxi Joker 2/Generation II Controller Box (continued)
Updated Equipment
• Maxi Joker 2/Generation II Controller Box (continued)
Updated Equipment
• Maxi Joker 2/Generation II Controller Box (continued)
Emaxx UGVs Generation II Controller boxes
Includes:
–
–
–
–
–
–
–
–
Fully Electric Ground Vehicles
Special Oil Filled Shocks
Upgraded Springs
Brushless Motor
Two Sony Block High Resolution Cameras with
Zoom Capabilities
Custom Pan/Tilt Units
Full Autonomous Capabilities
Wireless Video Transmission
Equipment (more)
TREX Micro Electric
Framework for incorporating real-time data in simulation
models
Real-time traffic
planning and control
Model Simulation
Data Collection by
UAV Mounted Video
Cameras
Image Analysis
Data Collection by
Infra-red detectors,
other sources
Real-time update of
simulation parameters
Obtain Observed
Parameters (Vehicle type,
density, flow, etc)
Historical Data
Interface
Example of Traffic Monitoring
• Blue and Green boxes
denote counting zones
• Red rectangles “flash”
momentarily when the
program counts the car
• Video: part1a.m1v
Data Collection
•
•
•
•
•
•
•
Network geometry
Speed limits
Static Parameters
Traffic controllers
Field data using video cameras
OD matrix
Dynamic Parameters
Route choice
Dynamic signal controllers
Traffic Simulation Model - Flow chart for VISTA
Real-time data
from UAV
Define Network
OD-t Demand
Simulation
OD Demand
Adjustments
Path Generation
(Shortest Path)
Calibrate
Path Assignment
Convergence
Yes
Import Results
No
Compare traffic
composition
Statistics - Parameters
•
•
•
•
•
•
•
•
•
Speed
Flow
Occupancy
Density (Spatial-temporal)
Turning Movement
Queue Length
Delay
Origin-Destination
Efficiency Parameters (LOS,
VMT)
• Car-following behavior
• The following car maintains
acceptable gap from the
leading car:
hs  l1 g
h l  g
s
1
a
• For total length of link ‘d’, the
equation becomes:

n0
d  l   li  g
i 1
a

• Thus, approximate capacity of
link is: 1 n0
• Occupancy can be derived as:
Occupancy 
TotalVehicles
n

Capacity
1 n0
a
Speed
• Mean speed can be calculated by
observing the travel time of individual
vehicles through the link:
1 L n d
d L n 1
s  
 
n j 1 i 1 t i , j n j 1 i 1 t i , j
d  VD2 VD1
• Flow is given by number of vehicles
passing through a certain point in
network in a given time period:
f 
1
T
  n t   n t  1
T
L
t 1 j 1
j
j
(L is number of lanes.)
Density
• Spatial:
k
t
L
n
j 1
i 1
 t

T * x
L
n

k
t

i
j 1 i 1
x
s
L
n


i, j
T * x
j 1 i 1
1
s
i, j
T
• Temporal:
k
s

1
d
L
n
fr j 1
j
• (Pseudo) Spatial-Temporal:
T
T
 L

1  1  L
1



dt

k s ,t T   
n

j
  n j dt
T
*
t 1 d fr  j 1
d fr t 1 j 1 

Turning Movement/ O-D
•
Assign virtual detectors on start and end
of links.
•
Tag vehicle id with time of arrival and
position at each VD each passes.
•
Maintain a link list to record path of each
individual vehicle.
•
Vehicle Path = {VD1,VD2, …, VDn}
• Delay
Delay 
L
n
L
n

j 1 i 1
1
t

n * s f  d 
d
s

f
i, j
• VMT
VMT=Flow x Distance
d
VMT 
T
  n t   n t  1
T
L
t 1 j 1
j
j
n
j 1 i 1
L
n
s 
f
j 1 i 1
1
t
i, j
1
t
i, j
Efficiency Parameters
• LOS
Arterial
Class
Range of
free-flow
Speed
Typical freeflow Speed
Level of
Service
I
II
III
35 --> 45
30 --> 35
25 --> 35
40
33
27
Delay (sec)
Average Travel Speed (mph)
very short
delay
A
> 35
> 30
>25
< 10
B
> 28
> 24
>19
10 --> 20
short delays
C
> 22
> 18
>13
20 --> 35
significant
delay
D
> 17
> 14
>9
35 --> 55
congestion
influential
E
> 13
> 10
>7
55 --> 80
high delay
F
< 13
<10
<7
> 80
over-saturated
Synchro Model
•
Campus network simulated
in Synchro:
– accurate geometry
– speed limit
– storage lanes included.
VISTA - screenshot
Approach
• Create an interface for real-time
data.
• Tweak parameters, comparing
simulated results with real data.
• Incorporating specific vehicles.
• Identify underlying theoretical
aspects for the above two.
Leroy Collins Blvd
Alumni Dr.
Leroy Collins Blvd
Alumni Dr.
Southbound
Northbound
Westbound
Eastbound
Time
11:30
L
e
f
t
Th
ru
Rig
ht
App
Total
L
eft
Thr
u
Rig
ht
App
Total
Le
ft
Th
ru
Rig
ht
App
Total
Le
ft
Th
ru
Rig
ht
App
Total
1
15
2
18
6
11
12
29
5
10
3
18
6
5
1
12
11:32
2
16
8
26
7
12
14
33
8
3
2
13
9
9
5
23
11:34
3
14
3
20
5
7
2
14
8
5
1
14
7
2
6
15
11:36
3
7
9
19
6
9
12
27
11
6
1
18
8
8
4
20
11:38
1
14
1
16
7
10
12
29
8
10
0
18
8
7
8
23
11:40
3
9
14
26
6
2
11
19
6
7
3
16
8
8
10
26
11:42
0
9
4
13
3
7
2
12
9
10
2
21
5
10
5
20
11:44
1
7
8
16
7
9
11
27
8
9
1
18
7
13
3
23
Vehs Entered
689
Vehs Exited
692
Starting Vehs
91
Ending Vehs
90
Travel Distance (mi)
511
Travel Time (hr)
23.1
Total Delay (hr)
5.2
Total Stops
547
SimTraffic Report
Movement
EBL
EBT
EBR
WBL
WBT
WBR
NBL
NBT
NBR
SBL
SBT
All
Total Delay (hr)
0.6
0.5
0.1
0.6
0.4
0
0.4
0.4
0.3
0.1
0.9
4.4
Delay / Veh (s)
38
29.5
8.5
36.3
22.6
3.4
30.9
17.8
12.9
22.3
21.4
23
Total Stops
65
54
28
64
46
11
49
52
51
20
107
547
Travel Dist (mi)
35.9
39.3
24.4
13.8
14.1
3.4
14
23.3
23.8
8.5
59.7
260.1
Travel Time (hr)
1.8
1.9
1
1.1
0.9
0.1
0.9
1.2
1.2
0.4
2.9
13.5
Avg Speed (mph)
19
21
26
13
17
24
16
20
22
20
20
20
Vehicles Entered
58
64
39
62
62
15
50
83
85
22
151
691
Vehicles Exited
56
65
40
61
63
15
50
84
86
21
152
693
Hourly Exit Rate
224
260
160
244
252
60
200
336
344
84
608
2772
Observations - Conclusions
• Improve model for visual-based count of vehicles;
• Integrate system;
• Show real-time performance with ‘traffic statistical
profiles’ built / modified in real-time.