-
Mat/d. Comput. Modelling Vol. 27, No. 9-11, pp. 257-291, 1998 @
1998 Elsevier Science Ltd. All rights reserved
Printed in Great Britain
PII: SO895-7177(98)00064-S 08957177/98 $19.99 + 0.09
Traffic Incident Detection: Sensors and Algorithms
R. WEIL, J. WOOTTON AND A. GARCIA-ORTIZ Advanced Development
Center, Systems & Electronics Inc.
201 Evans Lane, Saint Louis, MO 63121-1126, U.S.A.
Abstract-Incident detection involves both the collection and
analysis of trailic data. In this paper, we take a look at the
various traffic flow sensing technologies, and discuss the effects
that the environment has on each. We provide recommendations on the
selection of sensors, and propose a mbc of wide-area and
single-lane sensors to ensure reliable performance. We touch upon
the issue of sensor accuracy and identify the increased use of
neural networks and fuzzy logic for incident detection.
Specifically, this paper addresses a novel approach to use
measurements from a single station to detect anomalies in tragic
flow. Anomalies are ascertained from deviations from the expected
norms of tragic patterns calibrated at each individual station.
We use an extension to the McMaster incident detection algorithm
as a baseline to detect trsffic anomalies. The extensions allow the
automatic field calibration of the sensor.
The paper discusses the development of a new novel time indexed
anomaly detection algorithm. We establish norms as a time dependent
function for each station by integrating past normal tragic
patterns for a given time period. Time indexing will include time
of day, day of week, and season. Initial calibration will take
place over the prior few weeks. Online background calibration
continues after initial calibration to continually tune and build
the global seasonal time index. We end with a discussion of
fuzzy-neural implementations. @ 1998 Elsevier Science Ltd. All
rights reserved.
Keywords-Algorithm, Incident, Performance, Sensor, Trafllc.
1. SENSOR APPLICATIONS AND TECHNOLOGIES
Drew [l] defines traffic engineering as the science of measuring
traffic characteristics and applying thii information to the design
and operation of traffic systems. Early trafhc flow measurements
were rather subjective and involved visual assessments by police
patrols and helicopter pilots. The realization occurred from this
early experience that efficient system operation demanded objective
assessments based on quantitative metrics.
Surveillance of roads via CCTV (Closed Circuit Television) was
first implemented in the U.S. in Detroit, Michigan, in 1961. Four
years later, that system included trafhc detection and mea-
surement, variable speed signs, and lane and ramp control. Other
projects of the time included the Chicago Area Expressway
Surveillance Project (1961), the Port of New York Authoritys
Holland Tunnel (1963), and the Gulf Freeway Surveillance Project
(1963). One of the traffic control schemes featured on the Gulf
Freeway was a traffic merge control system that used a sonic sensor
mounted above the road, in a side-fired configuration, and
inductive loops embedded in the road. The sonic sensor measured
vehicle speed and gap upstream of the merge area, the inductive
loops detected vehicle presence and measured queue length on the
frontage road and upstream of the merge area. Today there is a
myriad of traffic control and management systems operating in the
U.S. and around the world, with many more in the works. One thing
remains constant in all of them, the need for sensors that provide
quantitative measurements of traffic flow.
257
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258 R. WEIL et al.
There are two major areas where sensors are used in traffic
engineering, (1) highways, and (2) road intersections. The
objective in both csses is to maximize vehicle flow; however, the
operational requirements for each are dr~atic~ly different. On a
highway application the traffic parameters of interest are: volume,
speed, density, lane occupancy, travel time, vehicle class, and
vehicle headway. Typical traffic volumes on a five-lane highway in
North America are 100,000 vehicle per day. Traf?lc speeds range
from 30 to 85 miles per hour, though posted limits are 40 to 65 in
most of the U.S. On an intersection application the traffic
parameters of interest are: vehicle count (volume), speed, queue
length, delay, gap, headway, and turning movements counts (U, left,
through, and right). Traffic volumes in this case are nowhere near
those of highways, and speeds are typically 25 to 45 miles per
hour. The two parameters that play the most important role are
queue length and turning movements, ss they dictate the cycling of
a traffic light.
We can further categorize vehicle sensing in each of these areas
by the permanency of the installation as temporary or permanent.
Temporary sensing applies primarily to equipment that is deployed
to perform detailed traffic studies in order to ascertain the need
for expanding the road infrastructure or install a new traffic
signal. The equipment is always located above ground, and the
primary technology involved is pneumatic. A rubber hose is secured
across the lane of interest and a counter placed on the side of the
road; the counter responds to pressure variations when a tire comes
in contact with the hose, thus it counts vehicle axles. In recent
years a self-contained, el~troma~etic sensor roughly the size of a
pocket calculator has become quite popular because of its simple
installation. In addition to counting vehicles, this sensor also
measures the vehicle length which can be used to classify the
vehicle.
In the case of intersections, permanent sensing is used
exclusively to establish vehicle presence and cycle the traffic
light. On highways, this type of sensing is used to determine peaks
and valleys in traffic volume for the purpose of setting road
maintenance schedules, and to keep an eye on the traffic volume
demand. For this reason, the sensors are sparsely located; Figure 1
shows the approximate location of permanent sensors on the
interstate highway system in St. Louis County, Missouri. The
traditional sensor technology used in permanent installations is
the inductive loop. The sensor is embedded into the road, and the
sensor electronics sit in a large enclosure on the side of the
road. Data is collected for an extended period of time, e.g., two
weeks, and averaged over predefined time intervals, e.g., 60
minutes. Weekly, monthly, and annual reports are generated from the
collected data. A typical diurnal traffic volume profile is shown
in Figure 2. While this sensing modality is prevalent in all major
metropolitan areas, it is hardly used in real-time except for major
cities with extremely congested roads like Chicago, Los Angeles,
and Toronto. In such cases, the data collection interval is
typically 30 seconds, and the sensors are placed at l/3 to l/2 mile
intervals because the primary objective is prompt incident
detection. Detection is achieved primarily through the use of the
CALTRANS or McMaster algorithms, which will be discussed later in
this paper. Panda has reported the use of the AIDA (Autoscope
Incident Detection Algorithm) for incident detection on I-35W in
Minneapolis, Minnesota, and in the Wattwil tunnel in Switzerland
[2].
In incident detection applications, the inductive loop is
complements with strategically placed surveillance cameras that
relay video to the Traffic Management Center. They are used to
confirm the existence of an incident, and assess its severity. This
strategy is actively being pursued by a variety of cities and
metropolitan areas [3-51. Table 1 summarizes the use of sensors in
traffic engineering according to the deployment setting and type of
installation. The remainder of this paper focuses on the
application of traffic sensors to incident detection, i.e.,
permanent installations on a highway setting.
We have already mentioned some types of traffic sensors. Table 2
summarizes the breadth of sensing technologies applicable to
incident detection. The table describes the mode of operation of
the sensor: active or passive. An active sensor is one that senses
its environment by processing the echo of a known signal
transmitted by it. A passive sensor is one that processes the
natural radiation present in the environment. The inductive loop
has tradition~ly provided vehicle count
-
Ikaflk Incident Detection
Figure 1. Permanent Automatic TkaEc hording (ATR) stations are
sparsely dis- tributed across a metropolitan area.
1oRM
9000 I 6000.
7000.
?
6coO-
!I
SOOO-
a 4000.
........ . . . . . , . .., , * ,
012345670 9 10 11 12 13 14 15 16 17 19 19 20 21 22 23
TlmrP&d
Figure 2. A distinct diurnal traffic volume with a distinct
traffic signature can be associated with every ATR station.
Table 1. Application of sensors to traf%c engineering.
Type of Installation I
and lane occupancy data, and when two loops are used in tandem,
speed data can be generated. These three parameters have influenced
the development of incident detection algorithms to date. As the
new generations of sensors appear on the market, more parameters
are being measured
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260 R. Wmt et al.
or derived. Table 3 details the trafkc parameters available from
commercially available sensor technologies. With the exception of
the inductive loop and perhaps the magnetometer+ all are installed
off-road and above the tr&c flow, and are thus affected by the
weather.
Table 2. Detector technologies used in trafhc sensors.
Technology
Inductive loop
Magnetometer
Infrared
Infrared
Acoustic
Ultrasonic
CCD camera
Radar-Doppler
Radar-FMCW
Laser-Pulsed
Mode
Active
Passive
Passive
Active
Passive
Active
Psssive
Active
Active
Active
Reeponds To
Ferrous mass
Ferrous mass
Contrast in thermal radiation
Reflected signal
Sound
Reflected sound
Contrast in visible light
Fkequency shift of refkcted
signal, i.e., motion sensor
Frequency of reflected signal
Reflected signal
wavelength: 6-14 mm
wavelength: O.&l.1 mm
frequency: 10.24 GHz
InGaAs diode laser
Table 3. lkaflic flow parameters measured or derived by the
various sensor technolo- gies.
Technology
Inductive loop
Magnetometer
Infrered----Passive
Infrared-Active
Acoustic-Passive
Ultrasonic
CCD camera
Redar-Doppler
Radar-FMCW
Laser-Pulsed
Volume
J
J
J
J
J
J
J
J
J
J
2 sensors
J
J
J
J
J
J
J
J
Class occupancy Density
J
J
J
Headwey
J
J
The graphs in Figure 3 show the effect of particular weather
conditions on a signal as a function of its frequency; the data is
adapted from The infrared & Electrv-Optical Systems Handbook
[S]. Notice that rainfall tends to affect all wavelengths equally
except milliieter waves where the attenuation varies somewhat
linearly with frequency. Ground fog a&&s the infrared
spectrum less than the visible light spectrum, but more than the
millimeter wave spectrum. The impact on millimeter waves is once
again almost linear with frequency. Signal attenuation in infrared
sensors is due primarily to the water vapor content in the air, and
by rain, snow, dust, fog, ocean spray, and smoke [7]. Because they
operate over a large bandwidth, molecular absorption is not a
concern. Lasers, on the other hand, are impacted by molecular
absorption and small particles present in the atmosphere, e.g.,
snow, dust, and smoke [S]. When comparing sensor technologies based
on the effects of atmospheric attenuation, one must keep in mind
that with the exception of road surveillance using CCTV, which
involves distances of around 1.6km, most sensor applications
comprise a one-way distance of 10-50 meters so that signal
attenuation is small.
Whereas sensors that operate in the electromagnetic spectrum are
affected by particle scatter- ing and molecular absorption,
acoustic and ultrasound sensors are affected by temperature and
wind. Sound (acoustic) waves are compressional disturbances that
propagate through a solid or fluid medium. In the case of air at
ambient pressure, the speed of sound depends primarily on
-
IMFic Incident Detection 261
HEAVY RAIN (25 mmlhr)
DRIZZLE(025mmlbr)
WAVELENGTH [mm]
Figure 3. The impact of weather on a sensor depends on the kind
of weather end the portion of the electromagnetic spectrum
involved.
temperature [9,10]. At 0 C the speed is about 332 m/s, and at 20
C it is about 344 m/s. These variations impact the performance of
the sensor by introducing a temperature dependent time delay in the
vehicle detection process. For an active sensor mounted 6 meters
above the road, a 20 C change (summer to winter) translates into a
one second change in the two-way signal travel time. A vehicle on
the road below moving at 100 kph would travel approximately 28
meters in that amount of time. Wind, however, is perhaps a more
obvious offender than temperature because, as a compression
disturbance created by differences in atmospheric pressure, it
tends to direct the sought after sound waves away from the
sensor.
Traffic sensors can also be grouped according to the road
surface coverage they provide: single lane and wide srea. Single
lane sensors as the name implies have a field-of-view that covers
only one lane of trafllc. They are typified by the likes of the AGD
300 Doppler radar sensor (AGD Systems Ltd, UK), the Autosense II
laser sensor (Schwartz Electra-Optics Inc., U.S.), and the Model
IR-222 infrared sensor (ASIM Engineering Ltd, Switzerland). Wide
area sensors, on the other hand, have a field-of-view that covers
several lanes, and they include the RTMS radar sensor in a
side-fire configuration (EIS Electronic Integrated Systems Inc.,
Canada), the BEATRICS radar sensor (Thompson-CSF, France), and the
INSIGHTTM CCD sensor (Systems & Electronics Inc., U.S.).
A variety of informative articles on trafllc sensors appear
regularly in Z?x@c Technology In- ternational magazine. The
interested reader is referred to the 1996 annual issue which
contains several articles dealing with various radar, infrared, and
video sensors [ll-181. Given all of the alternatives shown earlier,
the obvious question to ask is, which one is the best?
Several studies have been funded by the U.S. Department of
Transportation, Federal Highway Administration, to answer this
question. Three such studies are the one by Hughes Aircraft Co.
[19], the one by Bolt, Beranek and Newman (BBN) [20], and the one
by the Minnesota Department of Transportation (MnDOT) [21,22]. The
Hughes Aircraft Co. study evaluated
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262 H. WEIL et al.
various commercial, overhead, traffic sensors in diverse
geographic locations, and it addressed both highway and road
intersection applications. Table 4 lists the test locations, and
describes their geographic characteristics. Their goal was to see
where each traillc sensor technology would best serve the US. ITS
needs. The BBN study had more of an applied research orientation.
Its objective was the evaluation of several overhead, vehicle
sensing technologies from the point of view of measurement
accuracy, sensor cost, and communication bandwidth and
computational throughput requirements. All the sensors and
algorithms used were designed and implemented by BBN. The MnDOT
study, like the Hughes study, deals with commercial, overhead,
traffic sensors. It compares their performance to that of the
traditional inductive loop sensor. The study, however, goes beyond
just traffic parameter measurement and addresses sensor cost,
installation, calibration, and maintenance.
Table 4. Characteristics of the operational environments
addressed by the Hughes Aircraft sensor study [23].
Location
Phoenix, Arizona
Tucson, Arizona
Orlando, Florida
Minneapolis,
Minnesota
Mean
Elevation
1,117ft
2,584 ft
108 ft
834 ft
Climate
Arid
Arid
Subtropical
Humid Continental
Relative
Humidity
18-49
16-40
47-61
54-72
I Average I Average Temperature Annual January July Snowfall
35-64 75-105 0
37-63 74-98 I -l--t 50-71 73-93 0 2-22 61-84 45 Average
Annual
Rainfall
7
11
51
24
Technology
Inductive
/ loop
Magneto-
meter
Infrared
(passive)
Infrared
(active)
Acoustic
( passive)
Ultrasonic
CCD camera
Radar-
Doppler
Hadar- FMCW
Leser-
Pulsed
Table 5. Environmental niches for the various sensor
technologies.
Xear
Day
J
Clear
Uight
J
>old
Day
J
- Hot
W - J
J
J
J
Environmental Condition
Light High ,ight
Nind Nind Eain tain
J J
J
,ight lard
inow
J
-
?og - J
J
J
J
4
J
4
-
Smoke Neather
vlonitor
J
-
Traffic Incident Detection 263
Figure 4. View of uncongested traffic flow provided by a CCD
sensor.
Figure 5. View of rush-hour congested traffic flow provided by a
CCD sensor.
All of these studies certainly help to increase the awareness
and understanding of sensor technol-
ogy by the traffic engineering community. However, we believe
that their somewhat uncommital
and inconclusive nature may lead the casual reader astray, and
perhaps even discredit valuable
sensor technologies. They all implicitly search for the one
technology that will solve all needs
and replace the inductive loop.
In selecting a traffic sensor, the engineer must have a clear
understanding of the particulars
of the application for which a sensor is sought, and second,
he/she must also understand the
environment in which the sensor will operate. Earlier in the
paper, we addressed some of the
key environmental issues that affect sensor performance. Based
on these, some guidelines can be
established for the selection of sensors for use in incident
detection. Table 5 shows a variety of
environmental situations and the sensor technologies that would
serve them best.
Where obtaining a visual reading of the road is of interest, the
two technologies of choice
are passive infrared and CCD sensors. The added value of this
capability is demonstrated in
Figures 4 and 5. The first one shows a view of uncongested
traffic flow; the latter shows a view of
congested, rush-hour traffic flow. Areas frequently subjected to
fog, such as coastal cities, would
do best with an infrared sensor. Localities that experience
large changes in temperature, high
-
264 R. WEIL et al.
winds, or wind gusts should refrain from using acoustic (or
ultrasonic) sensors. Radar sensors
do well in most localities; the only drawback to their use is
perhaps the cumulative effects of low
level radiation on the driving population.
Places where snowfall is frequent should favor the use of
wide-area sensors. In these areas the
snowfall temporarily wipes out the road lane markings [22]. This
causes drivers to carve out
lanes which tend to straddle the real lanes. Single lane sensors
like the inductive loop and some
radar will fail to provide an accurate reading of the traffic
flow under such conditions. Wide
area sensors, on the other hand, can be equipped to sense where
the traffic flow is occurring, and
automatically adjust their traffic detection zones [24].
Where a visual confirmation of an incident is desired, the use
of a CCD sensor is recommended
over an infrared (IR) sensor. The CCD sensor provides a visible
light image that is easily
understood by the viewer. The IR sensor, on the other hand,
provides an image that is based
on temperature variations across the scene. This is much harder
to analyze visually because
it is not the way humans see their surroundings. The view from a
CCD sensor mounted on
I-70, in St. Louis, is shown in Figure 6. Even the most casual
viewer can see that an accident
has occurred involving the two leftmost lanes. The CCD sensor
also lends itself very well for
making assessments of the impact of weather on the road
infrastructure. This particular use was
brought to our attention by the Ministry of Transportation of
Ontario, Canada. In its ultimate
configuration, a CCD sensor could report not only the occurrence
of an incident, but also the
condition of the road.
Figure 6. View of incident related traffic flow provided by a
CCD sensor.
Our experience with sensors in military applications has been
that in general no one sensor
ever meets all the operational requirements of a given
application. Instead, a suite of sensors is
usually required. Every indication we have seen so far points to
traffic management as being no
different. To ensure reliable operation, a system-level concept
of how to mix and match sensors
needs to be developed, and some degree of sensor redundancy is
required. Currently, for incident
detection, sensors are spaced l/3 to l/2 mile apart. This allows
prompt detection of the shock
wave that arises when an incident takes place. We envision the
best traffic sensor configuration as one where the l/2 mile spacing
is maintained, wide-area sensors are used at every station, and
every other sensor station is equipped with single-lane sensors.
Such a configuration retains quick
detection of incidents, allows automatic reconfiguration of the
detection zones, and provides for
graceful degradation of the system in case of sensor failures or
inclement weather.
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Trafiic Incident Detection 265
Besides the operational environment, there are also
considerations of public safety and envi- ronmental impact that
need to be addressed when choosing a sensor. One that has already
been alluded to is low level radiation. Thii issue has plagued
electric utilities for decades, especially where high voltage,
electric power transmission lines exist. More recently, it has
appeared in cellular telephone operations where brain tumors have
been blamed on the close proximity of the transmitter to the brain.
In the case of trafhc management, radar sensors radiate everything
on the road. So far, their deployment has been limited, so
cumulative emission levels have been low. But, as the deployment
density increases, to perhaps every l/2 mile, so will the
background ra- diation level. The impact that thii will have on
people and animals, and on the communications infrastructure,
whether real or implied is yet to be established.
1.1. Sensor Performance
A topic to which the trafhc engineering community has devoted
considerable attention is that of sensor performance. Although a
thorough system performance analysis would normally address the
reliability, availabiiity, and maintainability of the system, in
the case of traffic sensors the focus has been primarily on
measurement accuracy.
But measurement accuracy should not be the only metric used for
selecting a sensor. The experiences related by those involved in
sensor performance studies gives us considerable insight into some
of the other issues which need to be examined. Consider the study
by MnDOT. The report of this rather comprehensive study helps us
identify at least two critical sensor operation issues:
(1) proper sensor setup and calibration, and (2) driver
behavior.
It does not matter if a sensor is capable of generating 100%
accurate measurements, if it is not properly installed and
calibrated, it will never achieve its full potential. And even if
it is properly configured, since it has no understanding of the
process being monitored, it can be misled by unusual driver
behavior. One should choose a sensor that is sufficiently accurate
for the application, easy to install and configure, and adaptable
to changes in the environment.
Attention should also be paid to how sensor performance is
evaluated. Three observations can be made about the MnDOT
study:
(1) it used actual traflic conditions, (2) it used inductive
loops es the performance baseline, and (3) it collocated highway
and road intersection sensors.
Let us look briefly at each one of these issues. It would seem
that the obvious thing to do when assessing the performance of a
sensor is to
subject it to real traffic conditions. The problem with this
approach is that it lacks control over the traffic situations
presented to the sensor. Not a good experimental approach. The
correct way is to present to the sensor stimuli that has been
ground-truthed in terms of the number and type of vehicles. The
stimuli should also be catalogued by weather or trafhc conditions,
e.g., clear day, rainy night, or rush-hour traffic. The output of
the sensor can then be meaningfully compared to the events which
took place on the road.
This brings up the issue of recording the stimuli. The people
involved in the BBN study used Digital Audio Tape (DAT) and video
tape to record the raw output of the nonimaging sensors and
maintain a visual record of events. Their objective was not to
generate stimuli for the sensors, but rather to record data for
post processing in the lab. The approach nevertheless sets a good
example. The recorded signals can be played back to the sensor
using a suitable transducer, much like music emanating from a
speaker. Any temperature and humidity effects present at the time
the recording was made would be intrinsic to the stimuli. The
physical effects of the environment on the sensor hardware should
be evaluated using an environmental chamber.
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266 R. WEIL et al.
Ground-truthing data is a labor intensive exercise. As part of
our own sensor evaluation efforts, we collected some 50 hours of
traffic video during 1995-1996. Our experience ground-truthing this
data has been that its takes about 1 hour of labor for every three
to five minutes of video. Long term sensor evaluation in this
manner is clearly infeasible. That is perhaps the reason why the
inductive loop has emerged as a calibration standard. The problem
with baselining performance against an inductive loop is that it
tacitly fails to recognize that loops are themselves inaccurate,
and very much subject to the environment. When a measurement
disagreement arises in the data collected, it is impossible to tell
whether it is the loop or the sensor-under-test that is incorrect.
For this reason, we feel strongly that long-term field tests are
suitable only to ascertain the resiliency of a particular piece of
hardware to the environment, and is not the way to evaluate
accuracy.
The performance of a traffic sensor depends much more on the raw
data processing than on the capabilities of the detector itself.
This is evidenced in the work by BBN where they adjusted the sensor
algorithms until meaningful performance was obtained. Presenting a
battery of test cases, i.e., recorded stimuli, to the sensor is a
much better way of establishing performance because the ground
truth is known. It also allows direct comparison of two sensors
since they would both be subjected to exactly the same events. In
our work, we use catalogued video sequences of some 5 minutes in
duration. The output of the sensor is then processed by a data
association program that quantifies the sensor performance against
the ground truth.
The final point we want to make about interpreting sensor
evaluation reports pertains to the sensor design itself. As
mentioned at the beginning of this paper, the conditions that
prevail in a highway are not the same as those on a road
intersection. The traffic parameters of interest are generally
different too. When a sensor designed for one type of traffic
application is subjected to the rigors of another, except for a
degree of luck, it will show substandard performance. A sensor
designed for collector road speeds of 5 to 50 mph will not measure
highway speeds of 35 to 90 mph very accurately. One meant to sense
approaching or departing traffic will not necessarily work well in
a transverse configuration. A sensor should only be tested against
the operational conditions for which it was designed. Extreme care
should be exercised when extrapolating test results.
The unfortunate consequence of improper sensor evaluation, or
test result analysis, is that the potential end-user can be
unwittingly biased toward one particular sensor technology, forever
discrediting other viable and potentially better technologies. But
let us assume that the correct approach is followed to establish
the measurement accuracy of traffic sensors. The next question to
ask is, how much accuracy is really needed?
In addressing this issue for traffic management and incident
detection applications, we have found that for many traffic
engineers the subjective metric of Level Of Service is deemed
suffi- cient [25]. This metric is rather loose and does not require
high sensor accuracy. It classifies congestion into six classes, A
through F, based on a somewhat subjective visual evaluation on the
traffic on the road. To understand where these engineers are coming
from, consider the overlay of traffic volume data sets shown in
Figure 7. The data is for the same day of the week, Tuesday, so it
represents the typical work day. And, it was collected by a
permanent recording station on I-70 in St. Louis, Missouri, that is
equipped with inductive loops. Note that a consistent trafhc
pattern evidences itself at this road location. The mean and the
standarddeviation for this data are shown in Figure 8. Small
deviations from the mean are likely due to trip route choices made
by some drivers. Thii accounts for the slight differences in the
daily volumes measured. Large and prolonged deviations, on the
other hand, indicate an incident, be it planned like a lane
closure, or unplanned like a stalled car. The data shown in Figure
9 serves as an example.
What seems to be of interest to these engineers is not so much
the measured value of traffic flow but rather the trend in traffic
flow. This is something that lends itself well to the application
of neural networks and fuzzy control [26]. Lee and Krammes have
reported the use of fuzzy logic for incident detection in diamond
interchanges (27). Hill has also reported on the application
-
Traflic Incident Detection 267
9ooo
6ooo
7om
6000
P
cm0
I
3 4ow
3ooo
-. . 0 12 3 4 ; s ; a s lb 1; 1; 1; 1; 1; ii 1; Ii lb 2i 2.1 2i
2i
Tlmo Pwlod
Figure 7. The traffic volume pattern at a given highway location
is fairly consistent from week to week.
, , , , , , , , . ~, , *, , ., , ,
0 1 2 3 4 6 6 7 6 0 10 11 12 13 14 16 16 11 (6 19 20 21 22
23
TIma Pulod
Figure 8. The average or median tr&ic volume is a good
indicator of the normalcy of traffic flow at 8 particular highway
location.
of neural networks to incident detection on the Long Island
Expressway [24]. We ourselves are collaborating with the Center for
Optimization and Semantic Control, at Washington University in St.
Louis, to develop system-wide, neural network and fuzzy approaches
to highway traffic
-
R. WEIL et al.
3000-
MOO-
- - - - - -Average -07~Mar
0
0 12 3 4 5 6 7 6 9 10 11 12 13 14 15 16 17 16 19 20 21 22 23
Tim* PerlocI
Figure 9. A traffic incident manifests itself ss a significant
deviation from the traffic volume pattern.
parameter estimation [28-311. Later in this paper, we discuss
some of the approaches we are following to estimate traffic volume
and speed at a road site, and from these do incident detection.
The point to be made about sensor accuracy is that if a sensor
can provide us with a consistent measure of the traffic flow, not
necessarily an exact one, then it is a good sensor for traffic
management applications. Of course, a consistent and accurate
sensor is the ultimate bonanza. But the data so far reported by
MnDOT shows that most sensors lack consistency, and it always seems
related to weather conditions.
2. INCIDENTS
The single most important problem in urban freeway traffic
operations is the timely detection of unscheduled incidents. Humans
most readily observe incidents and this is, perhaps, the most
compelling reason for the recent proliferation of surveillance
cameras in urban areas. However, the work force necessary to
completely survey urban traffic rapidly becomes cost prohibitive.
Thii has led the industry to seek automatic incident detection
mechanisms from data derived from measurements made at stations
along the freeway. In place of extensive coverage requiring a large
dedicated staff continuously watching traffic monitors, many
departments of transportation rely on a relatively small number of
motorist-aid vehicles. These vehicles circle critical highways
around the urban area. As in most cases, this approach has
tradeoffs. The obvious advantage is that the motorist-aid vehicle
is there on the spot, and for simple incidents (such ss out-of-fuel
cases) can render immediate assistance and clear the incident. In
other cases, the motorist-aid can call for appropriate assistance.
The disadvantage of such a system relates to the coverage required.
The probability of the motorist-aid vehicle being in the right
place at the right time to render the assistance is a function of
the number of vehicles and the road miles required to be
covered.
The most important incidents are those that result in stopped
vehicles (either by breakdown, out of fuel, or by accident). The
rapid detection of these situations and the early removal
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lk&?c Incident Detection 269
of the offending vehicles is most critical [32]. A highway
automatic incident detection system is imperative. Coupling this
system to a means to automatically relay imagery of a detected
incident to a traffic control center significantly reduces the
staff necessary to monitor urban traffic.
The key to the acceptance of such a system is its ability to
detect incidents reliably, rapidly, and with a low false alarm
rate. The authors have been working on such an automatic incident
detection system as an adjunct to our INSIGHT TM traffic management
sensor. This system uses the basic derived highway attributes per
lane of speed, flow, and occupancy to ascertain the presence of
traffic pattern anomalies, recognizing that traffic patterns are
not stationary. The drive to perform automatic incident detection
from derived highway data is not new, and it is worthy to reflect
on the work of past researchers [33-411. We note their successes
and rationalize when and why their algorithms work and
breakdown.
Cook and Cleveland [33] record that traffic incidents occur
predictably in frequency once every 20,000 to 30,000 vehicle-miles
(30,00&50,000 vehicle km) on heavily traveled urban freeways,
but unpredictably by time, location, and impact. The number seems
inordinately high, because during rush hour in urban America, it is
not uncommon for a five-lane highway to peak daily at 10,000 cars
per hour. This would suggest a daily traffic incident every two or
three miles of urban highway. There may be overstatement of
incidence frequency, but clearly, unscheduled incidences on urban
highways dominate the course of traffic delays. These delays
dominate over the more predictable delays due to traffic
congestion, or scheduled incidences due to road repair, etc. The
predictable occurrences of these latter traffic delays make them
easier to manage. Therefore, the focus is on the automatic
detection of unscheduled incidences.
Freeway incident management foremostly concerns the detection of
traffic flow anomalies and the identification of specific traffic
flow patterns that belong to the class of incidents. Upon incident
recognition, a full incident management system has a set of
strategies and tactics. Im- plementation may be through some form
of communication to the highway users, enabling timely alternative
route planning, and early removal of the congestion forming
incident by such mecha nisms as a motorist-aid subsystem.
Some clarification of terms is necessary. A traffic flow anomaly
is an unexpected flow pattern for a given location on a given
highway (modified by the weather condition) for a given day and
time of day. An incident, on the other hand, is any nonrecurrent
event which causes reduction of roadway capacity or abnormal
increase in demand [42, p. l]. Incidents may be predictable or
unpredictable as shown in Table 6 [42, p. 31. In Figure 10, the pie
chart gives the distribution of incident types [42, p. 31. The
chart illustrates that the majority of incidents are minor, such as
flat tires, overheating, and out of gas. Minor incidents will, in
general, only result in a vehicle on the hard shoulder. However, of
the total delay caused by incidents, 65% are attributable to this
minor category (see Table 7 and [42, p. 31). Characteristically,
minor incidents last less than half an hour and reduce the capacity
of a three-lane highway by typically 25%-26%. Accidents (which
constitute only 15% of all incidents) fall into the major category.
Major incidents contribute 35% of the overall incident caused
delays, constituting severe capacity reduction according to how
many lanes are blocked or whether there are accompanying injuries
associated with the accident. Table 8 [42, p. l] provides an
example of the capacity reduction associated with different types
of incidences on the three-lane Gulf Freeway in Houston.
Table 6. Incidents may be predictable or unpredictable as
incident types.
Predictable Unpredictable
l Mfdntenance activities
0 Construction
l Accident
l Special events (ball games, fairs,
parades, Olympics, concerts)
l Stalled vehicle
l Weather (rain, ice, snow, fog) . Bridge or roadway
collapse
0 Spilled load
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270 R. WEIL et al.
MECHANICAL
FLAT TIRE 34%
OVERHEAT 8%
ACC.,,.. . !InFNT wh
OUT OF GAS
ABANDON
4%
r OTHER
Figure 10. Incident type distribution (percent).
Table 7. Incident magnitudes.
Characteristic I Minor I Major
Duration
Blockage
< l/2 hour > l/2 hour
Shoulder area only One or more traveled lanes
Contribution to Overall
Incident-Caused Delay 65% 35%
Table 8. Typical capacity reduction.
Incident Type Capacity Reduction (percent)
Normal flow (three lanes) _
Stall (one lane blocked) 48
Noninjury accident (one lane blocked) 50
Accident (two lanes blocked) 79
Accident on shoulder 26
The distributions of the times of occurrence vary on a daily
cycle (as shown in Figure 11, see [42, p. 41, which is an example
from a Toronto study). The pattern relates, not surprisingly, to
peak usage, and seems to reflect the impact of driver fatigue. Note
the morning between 7:00 and 9:00 A.M., when the volume is high,
but presumably people are going to work and they are fresh after a
nights rest. Compare this with the evening rush hour, where
apparently they are less alert. That tiredness factor coupled to
high volume creates a higher proportion of incidences.
One study of incidents on a four- or five-mile section of
Highway 401 in Toronto between June 8 and September 4 in 1987 (as
given in Table 9) [42, p. 21 h s ows about 130 incidences a week.
The majority of these are of the shoulder blocking variety and less
than 2% are severe enough to block two lanes or more. Even though
this is a small percentage, the fact is that these will occur on
average twice a week, will be difficult and time consuming to
clear, and will represent a large traffic delay.
Not only is the severity of the incident and the time of an
incident important, but the time to recognize that an incident has
occurred is also critical. Luckily, nature works for us in this
instance. A direct relation exists between the severity of a
situation and the severity of the traffic flow anomaly. For
example, an accident resulting in a two-lane highway closure has a
correspondingly dramatic change in capacity. This infers that it is
easier, and therefore one could surmise that it would be quicker,
to detect severe anomalies and incidents. Speed of recognition is
of paramount importance. Consider a three-lane highway during rush
hour with an approximate flow of 6,000 vehicles per hour. Cars
arrive at a station on the road at an average of 100 cars per
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Daff~c Incident Detection 271
11
10
9
%8
7
8
5
HOUR ENDING
Figure 11. Times of incident occurrences. As might be expected,
incidents exhibit similar peaking characteristics to traihc
volumes, and the Toronto study reported an hourly breakdown of
incidents.
Table 9. Incidents on fivemile section of Highway 401, Toronto,
June &September 4, 1987.
Incident Severity
Shoulder blocking
1 lane blocking
2 lane blocking
> 2 lane blocking
Total
Incident Type
Reportable
Accidents
Nonreportable
Accidents
Noncolliion
Incidents
~ Number 1 % 1 Number 1 % 1 Number I %
Total No. Per
Week
Number %
minute. A reduction in throughput capacity of 80% caused by a
two-lane closure incident will allow servicing only 20 cars per
minute. This results in a queue being built at around 80 cars per
minute. A 30 second delay in detecting such a problem will impact
40 vehicles. The time to clear or reroute traffic and to clear the
incident determines the secondary impact of the incident.
A hard shoulder problem does not severely impact the capacity,
and it is a more difficult task to determine the anomaly in flow.
It could take on the order of four times longer to detect and four
times longer to clear this incident before impacting traffic at the
same level as a severe incident. The time to clear an incident is
critical. Delays increase geometrically with the time it takes to
clear an incident.
Automatic Detection Critical and Measures
process determining presence an is The step a termination
congestion. an of congestion if cause an
All the are and rely the variables, rate occupancy One presume
this a preference that rate occu-
are most measurements single sensors. preference these variables
probably in future todays trafhc provide addition
reliable of speed other such homogeneity traffic
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272 I%. WEIL et al.
Eight key factors [35,36,40,41] as summarized in Table 10 are
responsible to a first order for the performance of all incident
algorithms. Table 10 needs a little further explanation. A key
factor is the operating state of the highway in relationship to its
total capacity. As discussed earlier, it is more difficult to
determine that an anomaly has occurred directly from sensor
measurements on a highway that is operating well below capacity.
Fortunately, it is considerably less critical to make this
determination under low capacity conditions than at the other
extreme. Variation in operating conditions is a daily event with
peaks nominally related to rush hour. For example, Figure 12
represents a monthly flow pattern for westbound traffic on Highway
70 into St. Louis. Observe that there is a marked similarity (and
even predictability) on a work day basis. There is an even higher
consistency Monday to Monday, Tuesday to Tuesday, etc., with the
weekend traffic showing greater variability, however, with the
absence of a peak associated with rush hour. This daily and hourly
variation in traffic precludes any simple single thresholding
algorithm that does not take cognizance of the time varying nature
of the underlying traffic flow pattern. In an almost parallel
manner, the deviation of the incident as well ss its location
relative to the measuring station is significant. Any incident that
is short (say, less than one minute) in nature will be difficult to
detect (in all but the densest of traffic) especially if it is some
distance from the measuring station. Furthermore, by the time of
detection, the incident will have cured itself, and requires no
action. This is obviously the extreme case. At the other end of the
spectrum, an incident that exists for upwards of an hour will have
an accumulation effect on trafhc that will be evident in all but
the lightest of traffic conditions.
Table 10. Factors affecting all algorithms.
I. Operating conditions
A. Heavy B. Medium C. Light D. At capacity
E. Well below capacity
II. Duration of the incident
III. Geometric factors A. Grade
B. Lane drops
c. Ramps
IV. Environmental
A. Dry
B. Wet C. Snow D. Ice
E. Fog
V. Severity of the incident
VI. Detector spacing
VII. Location of the incident relative to detector station
VIII. Heterogeneity of the vehicle fleet
Geometric factors will influence incident detection algorithms.
The grade of the road, lane drops, and ramps will all have a
tendency to alter the traffic patterns such that uncongested
trafIlc might mimic the pattern of an incident occurrence in the
traffic flow. Correspondingly, the environment, particularly the
road surface condition and weather (in association with or
independent of the road surface), will impact traffic flow patterns
independent of the presence of an incident [39,43]. Heavy snowfalls
will reduce average vehicle speed, as will any icy road
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Traffic Incident Detection 273
Figure 12. I-70 west of Lucas and Hunt Station 605,
eastbound.
surface. Rain on a road surface after a long dry spell (where
the oil residue has not been washed
from the road surface for some time) also reduces average
vehicle speeds. These reductions are
independent of any incident.
As discussed earlier, the severity of the incident will impact
the capability of the algorithm.
An incident detection algorithm optimized to detect incidents
causing two-lane highway closures
will have few false alarms, but will have a tendency to miss
incidents that result from a vehicle
stopped on the shoulder. Correspondingly, an algorithm optimized
to detect even minor incidents
on the hard shoulder will have a tendency to yield a higher
false alarm rate.
For algorithms that rely on comparison of reports from
measurements made at two or more
spaced detectors, the spacing impacts the incident algorithm
performance. The wider the spacing,
the greater the opportunity for the underlying assumption that
the patterns came from essentially
the same flow conditions is less likely to hold true.
First-order factors affecting the performance of
these incident algorithms include the presence of intermediate
ramps, merged highways, and lane
drops. Finally, the heterogeneity of the vehicle fleet will
impact the performance of the incident
detection algorithms. Most incident algorithms assume a high
proportion of cars that dictate the
traffic flow pattern. A disproportionate percentage of large
trucks will have a tendency to slow
up traffic, reduce arrival rate, and increase headway. This can
alter the flow pattern sufficiently
to mimic an incident flow pattern.
The above discussion provides an illustration of the
dimensionality of the problem faced when
trying to ascertain from traffic patterns the existence of an
incident. It is not sufficient to know
only the presence of an incident, but also to be able to locate
to some degree of accuracy the
location of an incident relative to a given sensor. Summarizing,
there are basically two types of
detection algorithms, viz.,
(1) those that rely only on the measurement from one station,
and
(2) those that use a comparison method of the readings from two
stations spatially separated
along the highway.
The latter are known as comparative algorithms and generally
expect
(1) an increase in occupancy upstream of an incident (and where
speed is used, a drop in
vehicle speed), and
(2) a drop in downstream occupancy (together with an increase in
vehicle speed).
Clearly, of the factors given in Table 10, operating conditions,
duration of the incident, detector
spacing, and location of the incident are critical for
comparative algorithms (single station or
comparative). Within the two types of algorithms, there are
generally two classes of algorithms,
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274 I%. WEIL ei! d.
those that use instantaneous measurements (albeit integrated
over a short period of 30 seconds to minutes), or those that use a
filter methodology, such as a recursive linear filter (e.g., CY~
type or even a low order Kalman), to in some way balance the
measurement uncertainty with the fundamental noise (e.g.,
uncertainty of arrival rate) in the underlying generalized pattern
flow assumptions.
It is difficult to generalize as to which approach (single
detector or comparison of two detectors) is better. Clearly, the
comparison method relies upon the ability of the two detectors to
commu- nicate, which intrinsically increases cost of the system and
reduces reliability. In spite of these shortcomings, the comparison
methods, as implemented in the California algorithms (developed by
TSC), are perhaps the most widely accepted set of automatic
incident detection algorithms. The prevailing wisdom is that the
comparison method is a better device over a single station
algorithm which has a tendency to generate excessive false
alarms.
The two main problems in developing a single station algorithm
are:
(1) the complexity of distinguishing incident from nonincident
(or recurrent) congestion, and (2) the difficulty of adjusting for
incident related changes in trafhc operation because of factors
such as weather.
Progress has been made, however, in single station detectors
because of the recognition that a single station detector is an
economically better proposition, because it does not require con-
tinuous communication between two adjacent sensors. An example is
the McMaster algorithm which uses flow and occupancy to determine
the presence of an incident. This algorithm has been continuously
improved and includes speed when available from the detector. We
will introduce later in this text a novel single station detector
that is self-calibrating and seeks anomalies in trafhc flow
patterns. When such anomaly occurs, then and only then does the
system elevate itself to look at the adjacent sensors to determine
if the anomaly is an incident and, thereafter, seeks to locate the
incident.
Evaluating incident detector performance is not totally
subjective. Before proceeding further, we briefly describe the
appropriate measures to determine whether one set of algorithms
performs a better job of incident detection.
The most commonly used performance measures are the ability of
an algorithm to detect an incident (detection rate) as opposed to
its false alarm rate, see Table 11. Detection rate is the number of
incidents detected as a percentage of the number of incidents
occurred. The false alarm rate is the number of false alarm signals
as a percentage of tests performed by the algorithm. The evaluation
of algorithms is still somewhat subjective as evaluations must
allow for spatial and temporal deviations allowable between the
incident occurring and its detection. Usually the temporal leeway
is rather generous in most evaluations. Other measures include the
mean time to detect an incident and the accuracy of location of an
incident. The above performance measures are not one-dimensional. A
fundamental algorithm will perform differently according to the
thresholds chosen. Evaluation is often a function of thresholds and
severity of the incident. Detection ability and false alarm are
generally functions of incident severity.
Table 11.
Condition Indicated Actual Condition by Algorithm
Incident-k 1 Incident
Incident-F&e Incident
Missed detection
FUse alarm Detection I
2.2. Adjacent Sensor Algorithms
The adjacent sensor algorithms exploit the fact that an incident
reduces the capacity of the freeway at the site of the incident. If
the resulting capacity is less than the volume of the traffic
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lIaffic Incident Detection 275
upstream, there is a build-up of congestion. The boundary of
thii congested region propagates in an upstream direction dependent
upon the values of capacity and volume. Note that the severity of
the incident will affect this boundary. How far the boundary
propagates depends both on how long the incident is resident, as
well as the capacity and upstream volumes.
0 l INCIDENT FREE CONCITION 1 n lNClDENTCONDillON
TYPfCAL FIGURES Tl - 6 T2 = 0.6 13 = 0.15
Figure 13. Basic California algorithm.
Table 12. California algorithm.
Station indices (i) increase in the direction of travel.
Definition:
OCC(i, t) Occupancy at station i,
for time interval t (percent)
DOCC(i, t) Downstream occupancy OCC(i + 1, t)
OCCDF(i, t) Spatial differences in OCC(i, t) - OCC(i + 1, t)
OCCRDF(i, t) Relative spatial differences OCCDF(i, t)/OCC(i,
t)
in occupancy
DOCCTD(i, t) Relative temporal differences (OCC(i + 1) - OCC(i +
1, t)] in downstream occupancy -OCC(i + 1, t - 2)
Note: Occupancy ie measured ae an average over all instrumented
lanes at a single location on the freeway over a one-minute
interval.
The most well-known of adjacent sensor algorithms are the
California Algorithms developed by TSC. These algorithms are
occupancy-based algorithms. These were found to be superior over
volume-based algorithms. Figure 13 shows the most basic form with
the parameters described in Table 12 [35]. The traffic condition
passes three sequential thresholds viz. the spatial difference in
the twostation occupancy measures. The presence of an incident is
declared by the relative difference and the relative temporal
difference in downstream occupancy all exceeding respective
thresholds. The advantage of the California algorithm in its most
basic form is its simplicity and its intuitiveness. The algorithm
has been subjected to modifications and extensions since its first
introduction. These modifications are as simple as shown in Figure
14 and as complex as shown in Figure 15 [35]. The rationale for the
later augmentations is not only to understand when an
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276 Ft.. WEIL et al.
Figure 14. Simple modified California algorithm.
incident has occurred, but also to know when that incident has
terminated. Other extensions
relate to smoothing the surveillance data and providing a
forecast of the error in the measure of
spatial differences in the occupancy of adjacent sensors.
The basic California algorithms work well when conditions before
the upstream station are very
similar to the conditions before the downstream station. The
fact that it uses differential readings
over absolute measurements makes it less susceptible to such
issues as weather induced trafllc flow
patterns. It performs less reliably when there are intermediate
ramps, or intermediate freeway
to freeway mergers. The overpowering rationale to seek a single
station algorithm to detect
incidents is cost. The cost of requiring two stations to perform
this task immediately doubles
the capital outlay. The cost pales in comparison with the need
for continuous communication
between stations to implement the algorithm. For these reasons,
work on single station algorithms
continues to grow.
2.3. Single Station Algorithms
The prevailing wisdom is that single station algorithms tend to
generate excessive false alarms.
Clearly, this wisdom contains an element of truth. However,
single station algorithms have come
a long way since their first introduction. The most famous of
these algorithms is the McMaster
Algorithm [37]. Since its first introduction, it has grown and
been modified almost on a yearly
basis [43-481. The basic algorithm uses flow, occupancy, and,
where available, speed. It uses 30- second flow and occupancy data
to ascertain whether trafhc congestion is present. The algorithm
uses a graph of flow versus occupancy (Figure 16) to determine
whether the cause of the transition
into congested flow is an incident or recurrent congestion. One
weakness of the algorithm stems
from its use only of temporal data, and not spatial differential
data. This causes it to have
difficulty in differentiating between changes in trafllc
operation due to such factors as weather,
as distinct from incident related changes. Nevertheless, the
original algorithm developed by Hall et al. has been tested against
real tra6ic data and found to have good false alarm
characteristics.
Recently, Hall and others have modified their approach by
applying catastrophe theory to the
underlying traffic patterns [44,46,47].
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Traffic Incident Detection
..m..-..-..-..a..,
: I : I : I
i B : % !*
i : *I I
i
-
R. WEiL et al.
ABOUT 26%
278
C AITI 4 F
..-...
80 80
_I
At t fi UI . .
OCCUPANCY 46
Figure 16.
2.4. Incident Patterns and Causes of False AIarms
There are five basic incident patterns. The characterization of
the first (and there is no sig- nificance to the number~g) is
capacity at the site of the incident being less than the volume of
oncoming traffic. As a result, a queue develops upstream and
simultaneously a region of light traffic develops downstream. This
is the easiest of incident trafIic patterns to detect.
A second pattern occurs when the prevailing condition is freely
flowing but the impact of the incident is less severe. Thii
situation occurs when the capacity of highway after the incident is
greater than the volume of oncoming traffic (e.g., one blocked lane
in a four-lane hiihway, with the capacity of the remaining three
lanes still exceeding the demand).
A third pattern is a more extreme case of the second type. Here
the traffic is freely flowing with impact of the incident not
noticeable in the traffic data. This situation might occur in
moderate traffic with a disabled vehicle on the side of the road.
This situation is extremely difficult for any automatic incident
detection system to detect.
The fourth and fifth types relate to incidents occurring in
heavy trafhc. In the fourth case, the capacity at the incident site
is less than the volume (and the capacity) of the traffic
downstream. This difference will lead to a clearance of the
downstream problem. This traffic pattern evolves slowly unless the
incident is severe.
The fifth type happens when the capacity at the site of the
incident is greater than the down- stream volume. The effect of
this situation is that the pattern is localized and not discernible
in traffic data. Similar to the third type of incident pattern,
this is difficult for any automatic algorithm to have any degree of
success.
There are four main causes of false alarms in automatic incident
detection systems. The first, rather obvious, is a malfunction in
the basic detector. Detectors are not infallible, and obviously
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TkafFic Incident Detection 279
false significant to false alarms, occurs in heavy traflic in
which vehicles experience speed variations such as stop-go pockets
of traffic. This shows up in traffic data as waves that propagate
in a direction counter to the flow of the traffic. The third cause
of false alarms is abnormal
such as intermediate bottlenecks usually in locations that have
volume of
on ramp trafbc. This will be most when the total demand exceeds
the capacity of the freeway.
2.5. Summary of the Art Today
The use of occupancy dominates todays Few are speed based. In
some respects, the authors feel that thii reflects upon the basic
sensor used (i.e., loops provides a more reliable figure for
occupancy and basically needs two sensors to provide speed). Our
belief is that we are going to see a greater proliferation of video
sensors that provide occupancy, volume, and speed, and provide
measures of the of the traflic with greater reliability.
comparative algorithms appear to work better for incident
detection. However, the cost of the continuous communications
confirmation of an incident. This allays the continuous cost of
the communication between multiple sensor system, and
simultaneously experienced with single sensor detector
3. INCIDENT DETECTION APPROACH
Our approach tries to capture the best features of both the
single station (e.g., McMaster) and comparative (e.g., California)
architectures. We capture the cost and communication bandwidth
benefits of the single station configuration. At the same time, we
retain the lower false alarm rates of comparative configurations.
The key is coupling single station autonomous anomaly detection
with remote fusion. Each sensor autonomously detects the presence
of local traffic anomalies. Multiplc+sensor anomaly reports are
transmitted to and fused at a remote base-station for incident
detection.
Figure 17 illustrates our approach, consisting of a network of
autonomous trallic sensors dis- tributed over a highway system.
Each sensor works independently of all other sensors. The only
communication is between the base station and an individual sensor.
The individual sensors au- tonomously monitor traffic for anomalous
situations. At the sensor level, an anomalous situation may or may
not be an incident. The only requirement is that trafIic patterns
differ significantly from the norm. Once a sensor detects an
anomaly, it contacts the base station and transmits appropriate
tra.& status information. Contact can take place over a variety
of communication mediums.
Current sensor prototypes use telephone; future options will
allow the use of ISDN, or networks protocol such as SONET. The main
tradeoff is the ability to send back live imagery, the number of
sensors that can be communicating with the base station
simultaneously, and the initial delay for a connection between a
sensor and the base station. POTS delays can be in the order of l/2
minute. ISDN and SONET connections are almost instantaneous, but
there is a cost complexity penalty. Telephone is also limited to
still or slow update rate imagery, while ISDN and SONET can obtain
different levels of real time imagery.
The base station receives all anomaly reports and must perform a
fusion to determine if an in- cident actually occurred. Other
information extracted by the base station includes an estimate
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R. WEIL et al.
Incident Alert, Type,
Location Autonomous Sensor Anomaly Reports
ST. LOUIS COU
TRAFFIC SENSORS 4
Figure 17. Coupling single station autonomous anomaly detection
with a remote fusion of multiple-sensor anomaly reports at a
base-station for incident detection.
of the type and location of the declared incident. Information
pertinent to the incident decla ration include the traffic data
returned by the individual sensors, as well as the time of report,
and the location of the reporting sensors. The base station also
has the ability to query sensors that have not yet reported a
traffic anomaly. Notification of the traffic manager occurs once
the base station determines and declares an incident. The traffic
manager can then query the base station for traffic flow pattern
statistics. He may also view still images taken both at the time
the sensors detected traffic anomalies as well as current still
images. Figure 18 illustrates the base station user interface.
Communication takes place only between the base station and the
sensors. There is no sensor to sensor communication. Typically,
communication only occurs when a sensor has determined a traffic
pattern anomaly.2 This alleviates the continuous communication
problem of the adjacent sensor approach.
Once the base station has received a first report of a traffic
flow anomaly from a sensor, shortly thereafter3 other clustered
sensors will also start reporting. The base station may also poll
any sensor in the network for traffic status information. The base
station then uses data fusion techniques to determine if the
anomalies from the sensors actually indicate the presence of a
traffic incident. Other information extracted may include the
location and type of incident. The base station may also recommend
a course of action. It is the fusion of these multiple anomaly
reports that alleviates the high false alarm rate inherent in
single sensor approaches.
If image based sensors are used. *Occasionally the base station
may poll a sensor to gather trafiic state information. 3The time
delay would of course depend on the sensor spacing, traffic speed,
volume, etc.
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TrafEc Incident Detection 281
Figure lg. Base station interface.
Currently the INSIGHTTM sensor and accompanying base station
infrastructure is under phased development. Initial work developed
the sensor measurement hardware and software. The INSIGHTTM is an
image based sensor; currently it has the ability to measure volume,
speed, headway, occupancy, link travel time, and determine vehicle
class mix. Software to the units is remotely upgradable, and new
measurements can be added as deemed appropriate.
Phase II entails the development of the anomaly detection
algorithms. The first step of Phase II was the development of a
self-calibrating McMaster Incident Detection algorithm. In this
case, the algorithm is used only to declare anomalies, not
incidents. The self-c~ibrating ability of the algorithm is
currently beginning testing; results will be published in future
publications. The results will be used to develop new and enhanced
anomaly detection techniques. The approach for these algorithms
will be discussed in later sections.
The last stage will entail the development of sensor fusion
methods for the detection, classi- fication, location, and
recommended response to freeway incidents. The base station will
have the ability to fuse data from remote sensors reporting
anomalies and poll nonreporting sensors adjacent to reporting
sensors. Sensor reports, along with sensor locations, and the
temporal arrival of the alerts will be used in generating the
incident alarms and recommendations.
The rest of this paper will outline the status and details in
the current stage of this systems implementation.
3.1. Anomaly Detection
For the purpose of this paper, traffic anomaly detection is the
determination of a traffic situation differing significantly from
normal. An anomaly includes but is not limited to the class of
traffic incidents. A nonincident anomaly might be the effect of a
rain or snow storm on traffic, or the closure of a lane for
scheduled maintenance. The distinction is important because at the
sensor level, the requirements for screening for anomalies is less
stringent then screening for incidents. The problem of false alarms
is alleviated.
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282 R. WEIL et al.
This section describes the development of methodologies for
detecting anomalies. The require- ments for anomaly detection are
first spelled out, next a modification to the McMaster algorithm is
described, finally future directions for new detection algorithms
are explored.
Anomaly detection requirements
Since a traffic anomaly is a situation differing significantly
from normal, it is important to determine what normal is, and what
effects normal. Clearly the geometry of the location being
monitored by the sensor has a unique normality. This means that for
each site the sensor must be uniquely calibrated. Since a fielded
system would have hundreds if not thousands of sensors, it would
not be feasible to manually calibrate each sensor. The first
requirement is that any algorithm be self-calibrating.
Self-calibration is not a trivial task. Though Figure 12 shows
that there is clearly a pattern to describing normal traffic, there
are two main problems. The first is the ability to screen the data,
separating it into normal and anomalous conditions. The raw data
being collected will contain both types of observations with a
random mix. The problem of prescreening the data was solved for the
McMsster algorithm by having human operators log all events for the
collection and calibration period 24 hours a day. Clearly this is
untenable for a fielded system. An algorithm must be able to
self-calibrate without supervision or a training set of exemplars
of any kind.
Figure 12 showed a temporal pattern to trafllc flow patterns.
Casual observation shows there is a weekly and daily cycle of flow
with minor variations. These variations can be natural variations
or caused by anomalies. Seasonal changes, holidays, special events
(ball games), even weather can have an effect on normal flow, and
should be accounted for in any algorithm. Thus, any algorithm
should be temporally dependent with additional ability to account
for special events.
Speed of detection is the next important requirement. Since a
queue can build at up to 40 cars per minute per lane, a sensor
should respond as timely as possible to an anomaly. Since we are
only screening for anomalies, this should help alleviate the high
false alarm rates normally associated with short detection
periods.
Last, the algorithms must function autonomously. The large
quantity of sensors in a fielded system imply each sensor must act
autonomously from any central control. The algorithms must be such
that no coor~mation between sensors is needed, no regular updates
or control can be given by any outside means. It must be
self-bootable, and able to determine both the start and stop of an
anomaly independently.
3.2. McMaster Algorithm
The McM~ter algorithm is based on a catastrophe theory model
developed by Persaud and Hall [47]. They use catastrophe theory to
model the relation between speed, occupancy, and flow. Figure 19
illustrates the basis of the approach. The figure consists of data
collected before, during, and after several incidents. Area 1
consists of uncongested normal occupancy-flow data. Operation moves
into Area 2 or Area 3 during the presence of congestion (an
incident). Not illustrated but equally important is a sudden drop
in speed. The basis of the McMaster algorithm is the calibration of
the curve separating Area 1 and Area 2. C~bration can be automatic
or manual, but depends on the calibration data being recorded as an
incident, or an incident-free observation. Typically, a function is
fit to the lower bound of the incident-free observations. One form
used is
Flow = c + dl(occupancy) + da(occupancy)2, (3.1)
where c, Qi, and d2 are the calibration c~~cien~. Additions
calibration includes a lower limit of uncongested speed (SPL), and
a constant difference coefficient (CDL). Calibration of the example
shown in Figure 19 results in (see [37, p. 171))
Flow = 0.7 -t 1.29(occupancy) - 0.00i(occupancy)2,
SPL = 91 (km,hr), CDt = 2.9.
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Trsffic Incident Detection
AREA3
283
0 20 40 60 80
~~~~CY,~
Fi8ure 19. Example of a 30-second flow-occupancy relationship
from Station NB-7 of the Burlington Skyway, Ontario Canada
[37].
Figure 20 illustrates the logic of the MeMaster algorithm.
Basically there are two states, congested (incident has occurred)
and uncongested (incident-~~). To go from the uncongested to
congested state, the congested test based on equation (3.1) must
hold persistently for a count of Persistent_Limit. The opposite is
true in going from the congested to uncongested state.
Traffic-Status = Uncongested; PersistenceXounter = 0;
~ile(Obse~~ Traffic) (
switch (TrafficStatus){ case: Uncongeeted
if ((Speed < SPL) or (c$d~(Occupancy)+d~(Occupancy)a -I-CDL
ZFlow)), Persistence_Counter++;
else Persistence-Counter = 0;
if (PersiatenceXounter > Persiatencelimit) { TrafficStatus -
Congested; Persistence-Counter = 0;
1 break;
case: Congested if ((Speed> SPL) and
(c~d~(Occup~cy)~d~(Occup~cy)* +-CD& ZFlow)),
Persistence_Counter++; else
Persistence-Counter = 0; if (Persisteace_Counter >
Persistence4imit) {
TrafficStatus = Uncongested; PersistenceXounter = 0;
1 break;
1 1
Figure 20. McMaster incident detection logic.
One of the main weaknesses of the ~plementation of the algorithm
is the need for prescreening the calibration data, The screening
removes any incident contaminated measurements from the calibration
data. Screening require a human observer sitting at the highway and
logging incidents during the entire data collection process. Thii
was possible for the initial development of the algorithm because.
of the manning of the test and data collection site (the Burliin
Skyway), 24 hours a day, seven days a week [37, p. 1721. The
operator logs all events, i.e., stalled vehicles
-
WEIL et at.
on the shoulder, accidents, etc. The manning, logging, and
screening of calibration data is clearly not practicable for a
fielded system that requires hundreds of sensors uniquely
calibrated for each individual sensor location.
A second drawback of the calibration methodology is the
constancy of the process. Calibration of each sensor occurs once at
installation. Furthermore, either the sensor itself must be capa-
ble of collecting the calibration data, or some external
calibration data collection device must be used. Constancy of the
process implies the sensor cannot respond to changes in season or
highway geometry without the user intervening and recalibrating the
sensor. The addition of an exit or entry ramp upstream or
downstream of a sensor may certainly change the slope of the
uncongested flow-occupancy relationship. Ibrahim and Hall [39] have
shown the effect on the speed-flow-occupancy relationship by
weather. The question still remains on the seasonal effect on the
relation. One could reasonably conjecture that in Minneapolis,
Minnesota, the relation- ship in December (with two feet of snow
plowed onto the shoulder) must differ from the August
relatio~hip.
Our self-calibrating and self-tuning technique for the McMaster
algorithm solves the constancy problem. The motivation to calibrate
the line separating Area 1 and Area 2 in Figure 19 remains central
to our technique. Additional requirements are that the sensor
collect its own calibration data and that the collected data not be
screened to remove incident related data. Furthermore, calibration
should continue inde~nitely in the b~~round to tune the sensor to
changes in the environment. Figure 21 illustrates the online
calibration technique.
3460 3360 3240 3120 3m 2666 2760
;E 24aJ 2260 2160 2040 1926
:tz 1660
1% 1200 1060
E 720 600 460
:: 27 la? 1129 0 3363
0
Weekly Acc~ulatio~ And Calculation Of Volume Cuttoff Per 1 :
10 3
Lane
Calculate 3 6 for 3 16263s 66
ff4 m 143 to Occupancy 54669276 9 2 1
19 677 446 18 2362714 32 2 11 1
134 976 43 1 1 1032 49 4 1 : 2 66 2 12 1
1 2 3 4 5 6 7 8 9 10 11 12 13 I4 15 16 17
Figure 21. Self-calibrating McMester algorithm.
Flow and occupancy levels are split into an la x m matrix of
bins. These bins are then used to build a ~~~~ of the number of
observations of each. flu-~up~cy level during an accu- mulation
interval. For example, during the weak long accumulation interval
of Figure 21, there were 319 observations of a flow rate of 1320
vehicles per hour, and occupancy level of 8%. At the end of the
accumulation period, calculation determines a noncongested cutoff
flow level for each bin. The first step in determining the cutoff
flow levels require calculating the mean (pO) and standard
deviation (co) of the flow observations for each occupancy bin (of.
The cutoff level (X,)
-
TkaEic Incident Detection
for the occupancy bin o is then
&=/-Jo--oao,
where cy is a sensitivity factor (typically 3).
Again, by example from Figure 21, we have for the 8% occupancy
bin,
285
(3.2)
43 - 1440 + 319 - 1320 + 300 - 1200 + 50 + 1080 + 7.960 cla = =
43 + 319 + 300 + 50 + 7
1257 7
43. 14402 + 319 - 13202 + 300. 12002 + 50. 10802 + 7.9602 0s =
-
43 + 319 + 300 + 50 + 7 12572 = 89,
and&= 1257 - 3 + 89 = 990.
The end result, a vector of flow cutoffs, levels as a function
of occupancy bins4 If no previous
data has been accumulated for calibration, this vector is used
directly. Otherwise, low pass
filtering of the vector occurs on a bin by bin basis. Using
linear interpolation, a cutoff flow
level can be determined from this vector for any occupancy
level. Denote this function by
F& (occupancy).
Computation of a cutoff speed occurs similarly. The mean speed
(psp&) and speed stan-
dard deviation (.Q,&) are determined during the accumulation
period. Again a cutoff speed is
calculated as
x speed = kpeed - @-speed, (3.3)
where p is again a sensitivity factor. Xspd is again low pass
filtered to determine a cutoff speed
A speed * The logic to the McMaster algorithm stays basically
the same. The only change is to replace
SPL by Asp&, and c+di(Occupancy)+dz(0ccupancy)2+CDr, by
Fl,,t(occupancy) in Figure 20.
Evaluation of the algorithm is ongoing.
The self-calibrating McMaster algorithm is to go under field
trials shortly. Two INSIGHTTM
sensors are currently being installed in the northbound lanes of
I-270 at Olive Road and Page
Blvd. in St. Louis. These units will be used to field test the
self-calibrating McMaster Algorithm.
The results will be presented in a future publication, and will
serve as a baseline for comparison
for future algorithm development.
3.3. Future Directions
A clear shortcoming of the McMaster algorithm is the lack of
temporal dependence. The
conditions that describe an anomaly are the same at the height
of evening rush hour as at mid-
afternoon and 3 A.M. The boundary condition separating congested
and uncongested trafiic
flow is essentially an artificial mechanism with no intuitive
context. Our belief is that a fuzzy
logic approach is one promising approach to overcoming inherent
limitations of the McMaster
algorithm.
Zadeh [49] introduced fuzzy sets to overcome the criticism of
classical set theory for being an
extreme simplification of reality. The black and white nature of
classical set theory prevents the
handling of real world uncertainty. Uncertainty implies that it
may not be known with true or
false certainty whether an object belongs to a set.
Fuzzy set theory extends regular set theory by allowing each
element to have a degree of
membership in a fuzzy set. Let X be a universe of discourse and
let S be a crisp (traditional)
subset of X. The characteristic function associated with S is a
mapping
PS : x --) (0, 11, % is possible to directly accumulate the flow
cutoffs without first building the histogram. We felt the histogram
approach to be more intuitive for explanation.
-
286 R. WEIL et al.
PS
T
rl- 20 X
Figure 22. Characteristic function of a crisp set.
such that Vx 5 X, ps(x) = 1 if x E S and ps(x) = 0 if x $ S. For
example, let X = R be the real numbers and S = (x 1 0 5 x 5 20, x
< a}, then the characteristic function is shown in Figure
22.
The fuzzy set extends the notion of a crisp subset by extending
the range of the characteristic function from the pair (0, 1) to
the unit interval I = [0, 11. A fuzzy subset A c X is associated
with a characteristic function
/.&X-[O,l].
This characteristic function is called the membership jkrction
of A. The membership function indicates for each z E X, the degree
of membership in A. If PA(z) = 1, z belongs completely to A. If
PA(z) = 0, x does not belong to A. Inbetween 0 and 1 is a continuum
of degrees of membership. Figure 23 illustrates some typical fuzzy
sets on the real line 3.
PB T
llzl- X-
Figure 23. Typical fuzzy sets on the real line.
One of the primary applications of the fuzzy subset is its use
in conveying a concept. One could define the phrases early morning
rush hour and mid-morning rush hour as subsets of driving time of
day. Abnormal Flow and Normal Flow could be defined as subsets of
Flow. Figure 24 illustrates potential fuzzy subsets of driving time
of day. This representational ability of fuzzy subsets helps define
linguistic values. Let T stand for the variable driving time of
day, then a statement can be made such as
T is mid-morning rush hour.
-
Tra& Incident Detection 287
The value of T, mid-morning rush hour, is an example of a
linguistic value. Linguistic values are in essence the association
of a fuzzy subset with the value of the variable.
FUW Subeete Of Driving Time Of DaY I -+early morning msh&
-mid morning rurh-hour
0:oo 2:oo 400 6:OO 8:OO 1o:oo 12:oo woo l&O0 l&O0 20:oo
22:oo
Driving Time Of Day
+late morning rush-hour
*late morning +lunch-time
drnki afternoon
+early evening rush-houf
&late evening rush-hour -early evening
+-late evening
Figure 24. Fuzzy subsets of driving time of day.
The point or power of assigning a fuzzy subset as the value of a
variable is to incorporate the uncertainty in our knowledge of the
actual value of the variable, If the value of T is mid-morning
nicsh hour, we are certain that T is not 2:00 P.M., but 7:00 A.M.
to 8:30 A.M. are possible values for the variable T.
Zadeh [50] introduced the theory of Approximate Reasoning (AR)
based on fuzzy sets to provide a framework for reasoning in the
face of uncertainty. This theory represents propositions as
statements assigning fuzzy sets as values to variables. Figure 25
illustrates the form of an AR mechanism applied to incident
detection.
I FUZZIFICATION
1 ANOMALLYOUTPUT
Figure 25. Anomaly approximate ressoning mechanism.
The fuzzification function translates inputs such as time in
seconds, flow rate in vehicles per hour, into a vector indicating
the degree of membership in each of the fuzzy sets. For example, if
the time is 9:00 A.M., then from Figure 24, the vector indicating
the travel time of day is {0,.5, .5,0,0,0,0,0,0,0). The
defuzzification function transforms the fuzzified internal member-
ships back into a crisp form for external output. Thii may simply
be a threshold test to declare the presence of an anomaly.
The heart of the mechanism the Fuzzy Rule Base System. The rule
base consists of a set of fuzzy rules of the form:
IF A is Vj AND B is uk THEN C is rjv,,
and a fuzzy inference mechanism for chaining between the rules.
The chaining mechanism is based on Zadehs AR theory. A typical
anomaly rule could include:
IF travel time of dap is mid-morning rush hour AND flow is
Low
THEN Anomaly is sigh.
-
288 R. WEIL et d.
The determination of the fuzzy sets and rule base, though
certainly not a trivial task, is not beyond the efforts of
reasonable traiIic engineering expertise. The real problem is the
determi- nation of the membership functions. It might be possible
to determine one set of membership functions for the tived time of
dag identical for every sensor location. The problems is the de-
termination of membership functions for parameters such as ftovr
Me, occupancy, speed, etc. As noted earlier, these values are
dependent on each sensors location, A mechanism is needed to
autonomously calibrate these membership functions for each
sensor.
Artificial Neural Networks (ANN) are an appropriate way of
solving the calibration problem of the rn~be~~p unction. One of the
key capabiities of ANN is their ability to train and learn. A
judicial application of ANN techniques could be to learn the
membership functions. The training would of course need to be
unsupervised.
Simpson [51, p. 31 gives the following simple definition of an
Artificial Neural Network, . . . an ANN is a nonlinear directed
graph with edges that is able to store patterns by changing the
edge weights and is able to recall patterns from incomplete and
unknown inputs.
A biological neuron is the basic building block of the nervous
system. Figure 26 shows a sim- plified view of a biological neuron.
The body cell of the neuron is termed the soma. Connected to the
soma are multiple dendrites and an axon. These serve as the
mechanism of commu- nication to other neurons. The dendrites are
spine-like connections that receive stimulus from other neurons.
Each neuron has a single axon that serves to transmit the same
stirn~~ to all the other connected neurons. The connection between
a neurons axon and another neurons dendrite is termed a synapse.
Each synapse has a level of transmission of the stimulus on the
axon to the connected dendrite. Only when a neuron receives
sufficient stimulus from other neurons connected at the dendrites
does the neuron become active and send a stimulus out on its own
axon. Since each neuron accepts a different level of stimulus from
a connected neuron based on the tr~m~ion level at the synapse, the
network knowledge is equivalent to these transmission levels.
Biological neural networks work by a biochemical process that is
beyond this surveys scope of interest.
Figure 26. Biological neuron.
The basic model for an artificial neuron is shown in Figure 27.
Lines (or wires) replace the functions of biological axon and
dendrites. Synapses are replaced by weights (or resistors). The
body of the neuron or soma is split into two components. The first
component is an adder that sums up the weighted outputs of other
neurons. The second component is the activation function. This
function determines the artificial neurons activation level based
on weighted net input. Typically, this function is an s-shaped
function known as a squashing function (e.g., f(z) = (ez - l)/(e +
1)).
The method of connote and addressing the network neurons and
weights deiines the net- wo: k topology. Two general categories of
networks exist: recurrent and nonrecurrent. Recurrent networks have
cycles in the network connections. These cycles make recurrent
networks dy- namical systems. Nonrecurrent networks have no cycles
and can be viewed as a transformation from n-dimensional Euclidean
space to m-dimensional Euclidean space. Figure 28 illustrates a
feedforward nonrecurrent network and a fully recurrent network.
-
Traffic Incident Detection 289
Figure 27. Artificial neuron model.
FEEDFORWARD Note:wai@knotsham FULLY NON-RECURRENT RECURRENT
NETWORK NEIWORK
Figure 28. Network topologies.
Backpropagation [52] is the most popular training algorithm in
the training of multilayer artificial neural networks. The learning
procedure, called the generalized delta rule, involves the
presentation of a set of pairs of input patterns and target output
patterns. The system of multilayered networks with randomly (or
otherwise) initialized interconnection weights uses the given input
vector to produce its own output vector and compares this with the
target output pattern. When there is a difference, the rule for
changing weights is given by
where tkj is the target output for the jth COXIIpOnent of the
output pattern for pattern k, okj is the jth element of the actual
output pattern produced by the presentation of the input patte
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