Bluetooth BlueTOAD
Technology Evaluation
in Sarasota County,
Florida USA
Andy J. Lucyshyn, PE, PTOE
Atkins North America
A presentation on the Atkins study which
tested the effectiveness of BlueTOAD, a
travel-time device that utilizes Bluetooth
technology.
2
Agenda
• Methods of travel-time data collection
• Probe vs. point-based
• What is BlueTOAD?
• Evaluating the product:
• Distribution, match rates, speeds
3
Methods of Travel-Time Data
Collection
4
Methods of Travel-Time Data Collection
●
Point-based
– Roadside device measures spot speed of passing cars
– Spot speeds are averaged to determine travel time between points
●
Probe-based
– Roadside device reads unique identifier and adds time stamp
– Time stamp differential of those unique identifiers provide travel
times
Benefit
Point-Based
Probe Based
Aging of Data
N
Y
Issue of Statistical outliers
N
Y*
More likely to accurately
reflect travel conditions
N*
Y
5
What is BlueTOAD?
6
BlueTOAD Devices
tooth
ravel-time
rigin
nd
estination
7
Bluetooth Travel-Time Technology
BlueTOAD device
detects MAC
address and adds
time stamp
Raw MAC address
data is discarded by
TCI to preserve
anonymity
Cellular Modem /
Ethernet inside
BlueTOAD devices
send raw data to TCI
servers
Data is calculated
(Travel times and
origin-destination)
Raw data is processed
by TCI (MAC
addresses are
matched)
Data is filtered by TCI
(statistical outliers
removed)
In-device (on-site)
functions
Off-site functions
Data is sent to TCI
web server (for client
use) and to client
TCI = Traffic Cast
International
Client-access
8
Evaluating the Product
9
Sarasota County Test Location
Test Location
10
Study Purpose and Methodology
●
Test Product
– Two solar/cellular BlueTOAD devices
●
Control
– Hi-Star NC-97 Nu-Metric devices
● Spot speeds
Fruitville Road at:
Honore Ave
Beneva Rd
~2 miles
● Volumes
●
Data collected in June 2010
●
Test components
– Data Distribution & Match Rate
– Flow Speeds & Travel Times
11
Data Distribution
AM Peak
Mid-Day
Peak
PM Peak
12
Sample Size for Accurate Readings
σ = 6.1
Statistical Patterns of
Traffic Data and
Sample Size
Estimation
Stanley Young, Ph.D.,
PE Center for
Advanced
Transportation
Technology (CATT)
University of Maryland
13
Sample Size for Accurate Readings
●
Sample size (N) = (z2)(σ2)/(H2)
– Z is a coefficient of confidence
● Z = 1.96 at a 95% confidence level
● Z = 2.475 at a 99% confidence level
– σ = 6.1 (as shown previously)
– H = acceptable error range
● 3 mph for traffic operations
● 2 mph for before and after studies
14
Match Rates: Match Rate Criteria
Fruitville Road AADT between Honore Ave and Beneva Rd:
~19,000 vehicles
Time Increment
Before and After
Studies
Traffic Operations
15 Minutes
9
6
1 Hour
36
25
1 Day
864
600
Well placed sensors:
3%-4% detection rate
15
Match Rates
16
Match Rates
Total daily average = 3.45%
17
Flow Speeds
AM Peak
Mid-Day
Peak
PM Peak
18
Flow Speeds
19
Study Conclusions
Segment: 1.9 mi
Avg: 42 mph
Avg: 2.7 min
●
Distribution
– Matches evenly distributed across time
– Data trend was consistent with Nu-Metric devices
– Any inconsistencies are in off-peak intervals
●
Match Rates
– Achieved between 3-4% (25-36 matches per hour)
●
Flow Speeds
– Nu-Metric vs. BlueTOAD speeds within 2 mph on average
– Nu-Metric vs. BlueTOAD travel-times within 13.8 seconds on
average
20
Contact us
●
Andy J. Lucyshyn, PE, PTOE
– Atkins North America
– Andy.Lucyshyn@atkinsglobal.com
●
Efrain Duque, PE, PTOE
– Sarasota County
– eduque@scgove.net
●
Whitney D’Annunzio, EI
– Atkins North America
– Whitney.DAnnunzio@atkinsglobal.com
21