USING ARCHIVED DATA TO SYSTEMATICALLY IDENTIFY AND...
Transcript of USING ARCHIVED DATA TO SYSTEMATICALLY IDENTIFY AND...
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USING ARCHIVED DATA TO SYSTEMATICALLY IDENTIFY AND
PRIORITIZE FREEWAY BOTTLENECKS
Robert L. Bertini1, Huan Li
2, Jerzy Wieczorek
3 and Rafael J. Fernández-Moctezuma
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ABSTRACT. Bottlenecks are critical components of freeway systems and are of increasing
importance as congestion worsens in major urban areas. In the U.S., the Federal Highway
Administration has launched a new initiative aimed at identifying key bottlenecks in each
state. In Oregon, a freeway data archive has been established that stores vehicular count,
occupancy, and speed data from over 600 locations at 20-second intervals. Performance
measurement tools are also available and report on travel time, delay, vehicle miles traveled
and vehicle hours traveled. In addition, basic statistical tools are included that can report on
travel reliability across days, weeks, months and years. The Portland, Oregon metropolitan
region is known for its innovative transportation planning and multimodal transportation
system. This paper describes procedures used to develop an automated system to identify
freeway bottlenecks using historical data within an archived data user service environment.
Efforts are aimed at identification and display of active bottleneck features using graphical
tools. This research aims to detect bottleneck activation in real time and to expand the use of
reliability techniques. Efforts are aimed at improving the prioritization of improvements and
implementation of operational strategies. With the availability of archived data, new analysis
tools are possible at relatively low cost, with no new data collection.
INTRODUCTION
Understanding traffic behavior at freeway bottleneck provides a foundation for understanding
how the freeway system operates. A bottleneck is any point on the network upstream of which
one finds a queue and downstream of which one finds freely flowing traffic. Bottlenecks can
1 Associate Professor, Department of Civil and Environmental Engineering, Portland State University, Portland,
Oregon, USA, email: [email protected] 2 M.S. Student, Department of Civil and Environmental Engineering, Portland State University, Portland,
Oregon, USA, email: [email protected] 3 M.S. Student, Department of Mathematics and Statistics, Portland State University, Portland, Oregon, USA,
email: [email protected] 4 Ph.D. Student, Department of Computer Science, Portland State University, Portland, Oregon, USA, email:
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be static (e.g. a tunnel entrance) or dynamic (e.g., an incident or a slow moving vehicle). Also
sometimes a bottleneck can be active (meeting the conditions described above) and
sometimes it can be deactivated due to a decrease in demand or due to spillover from a
downstream bottleneck (Daganzo 1997).
Freeway congestion imposes significant costs on the movement of people and freight, and
new initiatives to measure freeway performance are being undertaken. In Oregon, toward
improving the management and operation of the transportation system as well as improving
transportation planning methods, a freeway data archive has been established that stores
vehicular count, occupancy, and speed data from over 600 locations at 20-second intervals.
Via a web interface, performance measurement tools are available and report on travel time,
delay, vehicle miles traveled and vehicle hours traveled. In addition, basic statistical tools are
included that can report on travel reliability across days, weeks, months and years. The
objective of this paper is to describe steps taken toward incorporating an automated system to
identify freeway bottlenecks using historical data within the Portland Oregon Regional
Transportation Archive Listing (PORTAL, see http://portal.its.pdx.edu) environment. Using
baseline knowledge of when and where bottlenecks occurred, a case study is described that
aims at identification and display of active bottleneck features using graphical tools. Previous
research conducted in California is validated for use in PORTAL, and suggestions for further
research are provided. With the availability of a rich source of archived data, new analytical
tools can be developed for use with historical data and in a real-time environment informed
by past performance.
BACKGROUND
Past research has sought a greater understanding of where freeway bottlenecks formed and
how and when they were activated, using oblique curves of cumulative vehicle arrival number
versus time and cumulative occupancy (or speed) versus time constructed from data measured
at neighboring freeway loop detectors. Once suitably transformed, these cumulative curves
provided the measurement resolution necessary to document and observe the transitions
between freely-flowing and queued conditions and to identify some important traffic features
(Cassidy and Windover 1995; Cassidy and Bertini 1999a, 1999b; Bertini and Myton, 2005;
Horowitz and Bertini, 2007). This same methodology has been applied here to systematically
define bottleneck activations and deactivations as a baseline for testing and implementing an
automated bottleneck detection algorithm.
Previous research has also developed techniques for automating and/or expanding the
identification of bottleneck activations and deactivations using comparisons of measured
traffic parameters and applying thresholds for those values. Most notably, Chen et al. (2004)
developed an algorithm that identified bottleneck locations and their activation and
deactivation times. That study used 5-minute inductive loop detector data from freeways in
San Diego, California, and focused on speed difference between adjacent detectors. Zhang
and Levinson (2004) also implemented a similar system to identify bottlenecks using
occupancy differentials. Banks (1991) used 30-second speed drop thresholds to identify
bottleneck activation in San Diego, and Hall and Agyemang-Duah (1991) developed a
threshold based on the ratio of 30-second occupancy divided by flow on a Canadian freeway.
Bertini (2003) tested several other signals including the variance of 30-second count, as
characterizing bottleneck activation at several sites. Building on this past research, the intent
in this paper is to test the Chen et al. method using a sensitivity analysis approach with data
from a freeway in Portland, Oregon.
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EXTENDING PORTAL’S FUNCTIONALITY
PORTAL has been in operation since July 2004. The regional archive has expanded its
functionality through time; In addition to providing loop detector data at different aggregation
levels, automatic data analysis tools have been developed and included into the archive
interface. The tools include the ability to produce oblique plots, travel time analysis,
congestion reports, and information layers in time-space surface plots with incident and
variable message sign information.
In response to the recent FHWA initiative of identifying bottleneck locations, the ITS Lab at
Portland State has identified and defined needs for a new PORTAL module that will provide
bottleneck identification and analysis capabilities. One of PORTAL’s main features is the use
of surface plots to represent the time-space evolution of a particular measurement. A design
goal for the bottleneck identification module is to visually represent the bottleneck activation
and deactivation times. A desired output is presented in Figure 1. Notice how the bottleneck
activation is marked as well as derived data, such as the average propagation speed of the
shockwave. The functionality of this tool will enable the study of previously observed
measurements with a single click, providing the most relevant information to the user:
activation, duration, deactivation, location, and shock propagation speed.
Figure 1. Desired output of the bottleneck analysis on historical data module for PORTAL
The next step is to incorporate acquired knowledge through historical analysis into live data
displays. Figure 2 illustrates an example of how previously known bottleneck locations can be
displayed on the time-space diagram in a real time environment. Bottlenecks A, B, and C
have been detected and mapped on the plot, and the boxes illustrate durations and locations
that occurred 90% of the time (for example) over a past period of time. These durations and
locations can be learned by analyzing data from previous archived days, for example,
analyzing the fifty previous non-holiday Wednesdays and characterize recurring features for a
B
A
Bottleneck
Activation
Deactivation
Estimated
Propagation Speed
A – 25 mph
B – 21 mph
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Wednesday display. The example in Figure 2 illustrates the time-space characteristics of two
known bottlenecks and provides a visual aid (bounding box). We acknowledge that detection
mechanism parameters may skip a detection, as with bottleneck A. In this case, displaying the
additional layer of ―learned‖ bottleneck locations provides the user with current analysis
output and historical knowledge, all in a single plot. Also notice how historical knowledge of
when and where to expect bottlenecks may be used to forecast propagation speeds as new
data arrives. As PORTAL continues to evolve, bottleneck information may also be displayed
in combination with incident information, providing a rich tool for users who may want to
distinguish recurrent congestion from incident related congestion.
Figure 2. Desired output of incoming data analysis in combination with historically learned
parameters, such as expected bottleneck locations and activations
Having defined design objectives for what the bottleneck module of PORTAL should
provide, one can identify as a key requirement the need for the identification mechanism to be
automatic and require no user intervention. This assumes the particular mechanism has been
calibrated a priori by the PORTAL staff and should be ready to use. In the following sections,
a discussion of existing techniques is presented, as well as an experimental analysis of
parameter fitting for a candidate method suitable to be implemented into PORTAL.
ANALYTICAL FRAMEWORK
The algorithm described in Chen et al. used the presence of a sustained speed gradient
between a pair of upstream-downstream detectors to identify bottlenecks. The most obvious
signals for identifying bottleneck in this method are the maximum speed measured by
upstream detector and speed differential between each pair of detectors. This algorithm,
which was simple and easy to be manipulated, was applied in San Diego using 40 mph
maximum upstream speed and 20 mph speed differential with 5 min speed data. However, the
values of the thresholds may be different in other cities like Portland because of different
A
B
C
Bottleneck
Estimated
Propagation Speed
A – 25 mph
B – 22 mph
C – 21 mph
Activation
Deactivation
90% percentile
of historicalbottlenecks
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situations in terms of the freeway network, geometry, weather, pavement, traffic conditions
and the nature of the available data. In this paper, the speed thresholds and data aggregation
levels comprising this algorithm are tested for bottleneck identification in Portland using
PORTAL data. In addition, real bottleneck locations and activation/deactivation times
identified using the method described in Cassidy and Bertini (1999) were used as baseline
reference points for comparing the outcome of the Chen et al. method using different data and
a range parameters. Specifically, we have tested the thresholds for maximum upstream speed
and speed differential; and evaluated different data aggregation intervals using PORTAL data.
The results are described in the next sections.
SITE DESCRIPTION AND DATA PREPARATION
The implementation of the Chen et al. method was tested with archived data from the
northbound Interstate 5 (I-5) corridor in the Portland, Oregon metropolitan region, shown in
Figure 3. There are 23 detector stations along this 24-mile corridor. The average detector
spacing is 1.09 miles and the standard deviation is 0.72 miles.
Figure 3. Interstate 5 corridor in the Portland region
MP 290.54 Lower Boones Ferry
MP 296.26 Spring Garden
MP 299.7 Terwilliger Blvd.
Portland
MP 310
MP 284
MP 307.9 Jantzen Beach
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Data from Wednesday October 3, 2007 was extracted from the PORTAL ADUS at the lowest
available resolution of 20 seconds, including count, occupancy and time mean speed in each
lane. For each station, a volume weighted mean speed for 1 minute aggregation was compared
to arithmetic averaging, and the results showed that the difference was not significant.
Therefore, mean speeds by station were calculated at further levels of aggregation (1 minute,
3 minutes, and 5 minutes) using arithmetic averages. So for each station, measurements from
multiple lanes were aggregated into a single quantity using simple arithmetic averaging for
simplified calculation. The obtained value is extrapolated across a region of influence of a
sensor station, i.e., the segment of the corridor starting with the sensor station location and
ending in the upstream sensor location.
Any data source will contain some errors. In this case, there is a large gap in the data at 3
consecutive detectors: milepost 292.18 has no data after 11:41 am, and the entire day’s data is
missing at mileposts 293.18 and 293.74. This constitutes 10,533 missing data points out of
our 96,876-point dataset. Since we impute the data for these mileposts by taking the average
of the data at the adjacent mileposts on either side, we can think of this as simply having one
less detector station prior to 11:41am and two fewer detectors afterwards. Thus, in practice we
are working with a 90, 555-point dataset. Besides this gap in the data, the rest of the data set is
missing only 4,277 points, or 4.72% of our data. Most of these remaining missing points are
isolated and randomly distributed across the day and corridor.
Some basic imputation was performed during the aggregations. When aggregating the 20-
second data into 1-minute data, all nonzero values for each minute (i.e., the nonzero values in
the set of three 20-second data points corresponding to each 1-minute period) were averaged.
Any further missing data elements were imputed by averaging the known values for the
detectors on either side of the data gap. After this step, there were no more missing values
when the 1-minute data was aggregated into the 3-, 5-, and 15-minute data sets. Incident
information provided by ODOT was also considered, and is reported in Table 1.
Table 1. Incidents for October 3, 2007. Data provided by ODOT.
Type Location Time
Stall I-5 Northbound Just North Of BURNSIDE 4:54:52
Stall I-5 Northbound Just South Of TAYLORS FERRY 8:29:45
Debris I-5 Northbound BOECKMAN 8:33:33
Debris I-5 Northbound Just South Of COLUMBIA BLVD 9:09:52
Stall I-5 Northbound Just South Of SANDY BLVD 10:20:13
Stall I-5 DELTA PARK 11:14:12
Other Closure I-5 Northbound At TRUCK LANE 13:12:47
Accident I-5 Northbound At BROADWAY 13:41:10
Construction I-5 Northbound At NYBERG RD - BONITA 19:53:11
Other Incident I-5 Northbound MARINE DR WEST 21:40:29
Suspicious about the perceived presence ofovernight bottleneck activation (midnight–4:00),
further analysis of speed and volume time series at Capital Hwy (milepost 295.18) was
conducted. Data from this location was clearly noisy overnight, in particular in the presence
of low volumes. Overnight reliability of measurements has proven challenging in the Portland
freeways, and ongoing work has exposed calibration issues with detectors (Cheevarunothai, et
al., 2006). Timeseries plots for speed and volume are shown in Figures 4 and 5.
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Figure 4. Speed time series for Capital Highway location. Notice the ―unnatural‖ variance in
the 00:00—04:00 period.
Figure 5. Volume time series for Capital Highway location. Low volumes overnight were
correlated with atypical speed measurements
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BASELINE BOTTLENECK ANALYSIS
In order to establish a baseline to act as ―ground truth‖ for this experiment, a detailed analysis
of bottleneck activations for October 3, 2007 was conducted. Figure 6 shows a timeseries
speed surface plot from PORTAL for this day. This location has two empirically observed
recurring bottlenecks. The first active bottleneck (A) occurs between the Macadam Ave. exit
and the Terwilliger Blvd. exit (Milepost 299.7) between 7:45 and 10:00 am. This location is
referred to as the ―Terwilliger Curves‖ often mentioned in radio traffic reports. The second
active bottleneck (C) is detected near Jantzen Beach (Milepost 307.9) between 2:30 and 7:30
pm. This afternoon bottleneck corresponds to commuters returning to Vancouver,
Washington. In addition, two other locations are also perceived to be bottlenecks when one
considers the speed map. One (D) is located at station 296.26 (Spring Garden) between
midnight and 4:00 am (due to data errors described above), and the other (B) is located at
station 290.54 (Lower Boones Ferry) between 7:15 and 9:15 am.
Figure 6. Baseline analysis of bottleneck locations and algorithm detection on Interstate 5
corridor on October 3, 2007
The activation and deactivation times of each bottleneck were analyzed using oblique curves
of cumulative vehicle arrival number versus time and cumulative occupancy (or speed) versus
time constructed from data measured at neighboring freeway loop detectors. Using procedures
described in Horowitz and Bertini (2007) and other sources (Cassidy and Windover, 1995;
Cassidy and Bertini, 1999a, 1999b; Bertini, 2003; Bertini and Myton, 2005), Figure 7
illustrates the partial diagnosis of bottleneck A for October 3, 2007.
In order to diagnose and verify the activation and deactivation times of bottleneck B, Figure 7
shows oblique curves of count and speed for the Terwilliger Blvd. station at milepost 299.7,
immediately upstream of bottleneck B. This was done for each station in the vicinity of
Terwilliger Blvd. Figure 5 shows the oblique count (left axis) and oblique speed (right axis)
curves for the Terwilliger Blvd. detector station. The cumulative plots are re-scaled in order to
A
B
C
Timeseries speed surface plot for I-5 NORTH on October 03, 2007 (Units in mph)
Bottleneck
Estimated
Propagation Speed
A – 20 mph
B – 17 mph
C – 15 mphD – 15 mph
Activation
Deactivation
Algorithm
detection
A
B
C
D
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magnify their features. The value of this visual tool is that the prevailing flows and speeds
measured at the particular station are seen as slopes of the oblique cumulative plot. This
method filters out the noise while not altering the times at which flow and speed changes
occurred. For the left axis of the graph representing cumulative count, a scaling factor q0 =
3,760 vph was used. For the right hand axis a scaling factor b0 = 52.2 mph was used. The two
vertical lines delineate the bottleneck’s active period, beginning at 7:45:20 and ending at
10:00:40. The straight lines superimposed on the curves designate piecewise linear
approximations to the average flow (across three lanes) and average speed. The times when
average flow and speed change slope are labeled, and average prevailing flows and speeds are
labeled in vph and mph respectively near the curve. It’s clear that the bottleneck’s activation
at 7:45:20 was marked by the passage of a queue past the Terwilliger Blvd. station. This is
seen via the drop in flow from 4,800 to 3,730 vph and a corresponding drop in speed. The
deactivation of the bottleneck later was marked by an increase in speed and a further drop in
flow after the morning peak period had ended and demand had dropped.
Figure 7. Terwilliger Blvd. oblique count and speed plot, October 3, 2007
The procedure described above and illustrated in Figure 7 was performed for all candidate
bottleneck activations, marked on Figure 6. Knowing the times at which a transition from
unqueued conditions to queued conditions occurred at a station also allowed for the
estimation of a shock speed between stations, and is also shown on Figure 6 for each
activation. The bottleneck activation and deactivation times are shown in Table 2, and can
now serve as the baseline ―ground truth‖ for experiments aimed at developing and testing an
automated bottleneck detection capability. Having this baseline should facilitate a sensitivity
analysis approach that will reveal the best combination of data aggregation level and speed
thresholds to be used within PORTAL’s bottleneck identification system.
Oblique Count and Speed for Terwilliger Blvd. MP 299.7 October 3, 2007
-500
-400
-300
-200
-100
0
100
200
300
400
6:0
0
6:1
5
6:3
0
6:4
5
7:0
0
7:1
5
7:3
0
7:4
5
8:0
0
8:1
5
8:3
0
8:4
5
9:0
0
9:1
5
9:3
0
9:4
5
10
:00
10
:15
10
:30
10
:45
11
:00
Time
Ob
liq
ue
Co
un
t (q
0 =
3,7
60
vp
h)
-100
0
100
200
300
400
500
600
700
Ob
liq
ue
Sp
ee
d (
b0 =
52
.2 m
ph
)
Count (vph)
Speed (mph)
6:4
2:2
0
7:2
8:2
0
7:4
5:2
0
8:2
3:4
0
10:0
0:4
0
10:3
6:4
0
6:2
3:0
0
10:2
7:4
0
9:2
2:0
0
9:0
3:4
0
8:5
5:0
0
8:2
6:2
0
7:0
5:0
0
6:4
2:0
0
52
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Table 2. Number of Speed Pairs Evaluated for Bottleneck Presence.
Bottleneck Milepost Activation Deactivation Sample Size
20sec 1 min 3 min 5 min 15 min
B 290.54 7:15 9:15 360 120 40 24 8
D 296.26 0:00 4:00 720 240 80 48 16
A 299.7 7:45 10:00 405 135 45 27 9
C 307.9 14:30 19:30 1,080 360 120 72 24
Total 2,565 855 285 171 57
EXPERIMENTAL SETUP
Now that bottlenecks have been analyzed as a baseline, in order to test the Chen et al.
algorithm on the Portland dataset from PORTAL, we implemented the core of the method
(identifying the locations of bottlenecks) using MATLAB. Chen’s method compares each pair
of longitudinally adjacent detectors at each 5-minute time point and declares that there is a
bottleneck between them when:
1. The speed at the upstream detector is below the maximum speed threshold , and
2. The difference in the speeds at the upstream and downstream detectors is above the speed
differential threshold .
Given that a bottleneck separates unqueued (downstream) from queued (upstream) traffic
states, Chen et al. used values of = 40 mph, = 20 mph, with data aggregated at 5 minute
intervals. That is to say, if the upstream speed is less than 40 mph and the downstream speed
is more than 20 mph greater than the upstream speed, the Chen et al. method identifies a
bottleneck between those detectors during that interval. The Chen et al. paper also specifies
that they used data aggregated over 5-minute intervals. However, Chen et al. recognized that
each of these three parameters (maximum speed, minimum differential, and aggregation level)
may need to be adjusted for the algorithm to work optimally in new settings. In this research,
we have tested different values of , , and the aggregation level as shown in Table 3.
Table 3. Input Variables Tested for Bottleneck Identification. Input Variables Values Tested
Max speed detected by upstream detector (mph) 30, 35, 40, 45, 50 mph
Speed differential (mph) 10, 15, 20, 25, 30 mph
Data aggregation level for measured speed data 20 sec, 1 min, 3 min, 5 min, 15 min
As shown in Table 2 above, the total number of speed-pairs where bottlenecks are activated
depends on the speed data aggregation level. Thus, the number of successful identifications
and false alarms is not comparable, so their proportions were used for evaluation instead. The
sensitivity analysis will include a comparison of success rate and false alarm rate, presented
as a ratio against the known bottleneck activation and deactivation times and locations.
The result of our tests is an assessment of how many bottleneck instances are captured by the
Chen et al. method with the different threshold combinations ( , ) and using different
aggregation levels for the measured speed data. As an example, Figure 8 shows the outcomes
for all 25 threshold combinations using 5 minute speed data. Each time-space plot shows a
combination of speed differential and maximum speed measured at the upstream detector,
labelled as ( , ).
The success rate and false alarm rate for the given thresholds are considered as a function of
the input values. The success rate is the proportion of speed-pairs which are real bottlenecks
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that become successfully captured by the Chen et al. method. These correctly-found
bottlenecks are labelled as magenta triangles in Figure 8. And the false alarm rate, that is, the
proportion of speed-pairs captured by the Chen et al. method even though they are not real
bottlenecks; these false alarms are labelled as dark triangles in Figure 8. From Figure 8, the
correct triangles and false ones are easy to recognize visually. The same procedure was
repeated for the five other aggregation levels shown in Table 3.
Figure 8. Outcome of bottleneck identification with different input value combinations ( , )
using 5 min speed
Figure 9 compares the percentage of successes (vertical axis) across different thresholds
(horizontal axes) and different speed data aggregation levels. The algorithm results are
compared with the benchmark ―known‖ activations described above. Typically, a speed
differential of 20 mph could be assumed to be a signal of bottleneck activation. However, on
the plot, it is clear that the success rate increases (regardless of data time aggregation level) as
the maximum upstream speed increases and the minimum speed differential decreases, i.e.,
when the standards for defining a bottleneck are loosened. The success rate is maximized
when the input value for maximum speed detected by the upstream detector is 50 mph, that of
the speed differential between a pair of upstream-downstream detectors is 10 mph, and 5 min
speed data is used. The Chen et al. method has the highest success rate with these input values
using 5 minute PORTAL speed data.
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Note that 5 minute data is not optimal across the board: at some thresholds it is surpassed by
15 minute data, while at others (especially when the minimum speed differential is high) the
finer aggregation levels such as 20-second and 1-minute data actually perform better.
Figure 9. The proportion of true bottleneck speed-pairs that are successfully captured by the
Chen et al. method
On the other hand, Figure 10 illustrates the false alarm rate (vertical axis) from the whole
picture. Again, regardless of data aggregation level, the percentage of all speed-pairs captured
by the Chen et al. method that turn out to be false alarms is also maximized at the 50 mph
maximum upstream speed and 10 mph minimum speed differential. Note that in this case, the
aggregation level has the same effect throughout: finer aggregation levels like the 20-second
data have the highest false alarm rates across the board, while the coarsest aggregations like
15-minute data have the lowest false alarm rates at each point.
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Figure 10. The proportion of speed-pairs captured by Chen’s method which are not real
bottlenecks
As illustrated in Figures 10 and 11, the 5 and 15 minute aggregation levels are able to produce
the highest success rates and lowest false alarm rates. Figure 12 provides a visual comparison
between the results with different speed data aggregation levels, shown on time-space
diagrams of the I-5 corridor. The selected ( , ) pairs are shown on the right side of the
figure. The 20 second aggregation level data shows an unacceptably-large number of false
alarm triangles, looking very messy. Meanwhile, the 15 min time interval data misses many
true bottlenecks at some threshold combinations.
Figure 12. Comparison of outcomes with different time interval speed data
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CONCLUSIONS
From Figure 11 we see that 5 and 15 minute aggregation levels produce the lowest false alarm
rates at each combination of thresholds. In other words, aggregation smoothed out some of the
noise in the data that would otherwise lead to false alarms. In Figure 10, we also note that
success rates over 80% are achievable only for minimum speed differentials of 15 mph or less
and maximum upstream speeds of 40 or more. The 5 and 15 minute aggregation levels have
the highest success rate throughout this region. Consequently, the 5 and 15 minute
aggregations appear to be ideal. Anything coarser than 15 minutes, however, would be
unlikely to reveal information that is useful for purposes of bottleneck identification, since it
is desired to be able to pinpoint the start and end of a bottleneck as precisely as possible. In
fact, when following the leading edge of a bottleneck to estimate the rate at which the
activation shock propagates upstream, finer data aggregation levels may be necessary in order
to estimate the propagation rate with enough precision. For this reason, the 5 minute
aggregation is a better choice than the 15 minute data.
It appears that the success rate does not increase much for differentials below 15 mph and
maximum speeds above 40 mph, so this combination of thresholds along with a 5 minute data
aggregation level appears to be an optimal choice of settings for this algorithm. Hence, the
Chen et al. original settings used for the San Diego data—20 mph differential, 40 mph
maximum upstream speed, and 5 minute aggregation—are indeed close to a reasonable
balance for Portland. Figure 12 illustrates this, showing that (20, 40) at 5 minutes finds most
of the real bottlenecks while having an acceptably low number of false alarms.
It is clear that each user’s optimal choice of thresholds and data aggregation level will depend
on how the user balances the need for a high success rate against the need for a low false
alarm rate. For commuters checking the traffic report before heading out to work, they may
prefer to have a high success rate (and thus avoid being stuck in traffic) even if it comes with
frequent false alarms. On the other hand, perhaps researchers or transportation operations staff
searching for the most important bottlenecks to repair may prefer to have a low false alarm
rate so that money is not wasted on fixing a non-existent problem.
FUTURE WORK
The results describe here are promising and are leading to additional research toward
automating the process of identifying bottlenecks on Portland freeways. One next step
consists of testing the use of other variables such as measured volume as a criterion for
sorting recurrent bottlenecks from false positives due to data errors. One further step includes
the desire to add the ability to track sustained bottlenecks and discard transient false positives
that are isolated (in the time dimension) from other bottlenecks. In this paper we have
presented measurements of false positive or false negative tracking, but have not concluded
anything regarding an optimal level of false positives or negatives. This may vary by user,
and could be variables chosen by a PORTAL user in an iterative way.
When implemented in PORTAL, historical data will be compiled to ―learn‖ where and when
recurrent bottlenecks occur. This will facilitate some simple forecasting procedures that can
be shown on the timeseries speed surface plots as the loop detector live feed proceeds. Finally,
travel reliability on a given corridor may be more important than travel time alone for
travelers, shippers, and transport managers. In addition to identifying recurrent bottlenecks
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using measures of delay, we plan to test several reliability measures including travel time,
95th percentile travel time, travel time index, buffer index planning time index, and
congestion frequency
ACKNOWLEDGEMENTS
The authors acknowledge the support of the Oregon Department of Transportation, the
Federal Highway Administration, TriMet and the City of Portland for their ongoing support
and guidance in the development of the Portland ADUS. This work is supported by the
National Science Foundation under Grant No. 0236567, and the Mexican Consejo Nacional
de Ciencia y Tecnología (CONACYT) under Scholarship 178258. The Portland metropolitan
area’s TransPort ITS committee has also encouraged development of this project.
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