ISSN 1055- 1425
August 2007
This work was performed as part of the California PATH Program of the
University of California, in cooperation with the State of California Business,
Transportation, and Housing Agency, Department of Transportation, and the
United States Department of Transportation, Federal Highway Administration.
The contents of this report reflect the views of the authors who are responsible
for the facts and the accuracy of the data presented herein. The contents do not
necessarily reflect the official views or policies of the State of California. This
report does not constitute a standard, specification, or regulation.
Final Report for Task Order 5321
CALIFORNIA PATH PROGRAM
INSTITUTE OF TRANSPORTATION STUDIES
UNIVERSITY OF CALIFORNIA, BERKELEY
Finding and Analyzing True Effect of
Non- recurrent Congestion on Mobility
and Safety
UCB- ITS- PRR- 2007- 10
California PATH Research Report
Pravin Varaiya
University of California, Berkeley
CALIFORNIA PARTNERS FOR ADVANCED TRANSIT AND HIGHWAYS
Finding and Analyzing True Effect of Non- recurrent Congestion on Mobility
and Safety
Final Report for PATH TO 5321
Pravin Varaiya
Department of Electrical Engineering and Computer Science
University of California, Berkeley CA 94720
Tel: ( 510) 642- 5270, Fax: ( 510) 642- 7815
varaiya@ eecs. berkeley. edu
April 29, 2007
Abstract
This report summarizes empirical research about the causes and impact of non- recurrent
congestion. A method is presented to divide the total congestion delay in a freeway section into
six components: the delay caused by incidents, special events, lane closures, and adverse
weather; the potential reduction in delay at bottlenecks that ideal ramp metering can achieve; and
the remaining delay, due mainly to excess demand. The method can be applied to any site with
minimum calibration, but it requires data about traffic volume and speed; the time and location
of incidents, special events and lane closures; and adverse weather. The method is illustrated by
applying it to a 45- mile section of I- 880 in the San Francisco Bay Area.
Data limitations preclude applying the method statewide. A simpler method, which depends
only on routine data collected in the PeMS system, has been implemented. A PeMS application
now provides a ‘ congestion pie’ for any district or freeway segment. The pie divides the total
congestion delay into three categories: potential reduction, excess demand, and accidents; and an
unexplained, ‘ miscellaneous’, category.
Keywords: freeway congestion; non- recurrent congestion; congestion pie; weather; ramp
metering; loop detectors
2
Executive Summary
Traditionally, congestion delay has been divided into recurrent and non- recurrent delay. The
availability of more detailed and massive amounts of data, and the need to design congestion
mitigation strategies whose benefits are more predictable, has spurred research into the causes
and impact of congestion. This report summarizes empirical research about the causes and
impact of non- recurrent congestion. The study has two major components.
The first component develops a method to divide the total congestion in a freeway section into
six components: the delay caused by incidents, special events, lane closures, and adverse
weather; the potential reduction in delay at bottlenecks that ideal ramp metering can achieve; and
the remaining delay, due mainly to excess demand. Applied to a 45- mile section of I- 880 in the
San Francisco Bay Area, the method reveals that incidents, special events, rain, potential
reduction by ideal ramp metering, and excess demand respectively account for 13.3%, 4.5%,
1.6%, 33.2% and 47.4% of the total daily delay. The delay distribution of the various
components is different between the AM and PM peak periods and between the two freeway
directions.
The method is fully automated and can be applied to any site with minimum calibration. It
requires data about traffic volume and speed; the time and location of incidents, special events
and lane closures; and adverse weather. Because such detailed data are not routinely available,
the method cannot be implemented today statewide.
The second component of the research develops a method that only uses data routinely available
in PeMS. That method is now implemented as part of the PeMS ‘ congestion pie’ application.
PeMS now provides a congestion pie for any district or freeway segment, dividing the total
congestion delay into three categories: potential reduction, excess demand, and accidents; and an
unexplained, ‘ miscellaneous’, category.
3
1. Introduction
Congestion1 is caused by incidents, special events, lane closures, weather, inefficient operations,
and excess demand. Their impact can be summarized in the division of the FHWA congestion
‘ pie’ into six components as in Figure 1. Knowledge of the congestion pie is essential to the
selection of effective congestion mitigation strategies ( 1).
The research summarized here proposes and tests a method to divide the total congestion Dtotal
into six components: ( 1) Dcol, the congestion caused by incidents, which could be reduced by
quicker response; ( 2) Devent, the congestion caused by special events, which could be reduced by
public information and coordination with transit; ( 3) Dlane, the congestion caused by lane
closures, which could be reduced by better scheduling of lane closures; ( 4) Dweather, the
congestion caused by adverse weather, which could be reduced by demand management and a
better weather response system; ( 5) Dpot, the congestion that can be eliminated by ideal ramp
metering; and ( 6) the residual delay, Dexcess, largely caused by demand that exceeds the
maximum sustainable flow. The method is applied to a 45- mile section of I- 880 in the San
Francisco Bay Area, using data for January- June, 2004.
The six components in our method correspond closely with the six slices of the FHWA pie of
Figure 1, with three differences. First, the cause ‘ poor signal timing’ is replaced by ‘ poor ramp
metering’ or, in our terminology, ‘ potential reduction in delay’. Also, what is regarded in the
FHWA pie as the delay due to ‘ bottlenecks’ is in our terminology ‘ excess demand’. Lastly, the
‘ work zone’ slice in the FHWA pie is called ‘ lane closures’ in our method.
Our method refines previous studies ( 2,3,4) that group Dpot and Dexcess together as ‘ recurrent’
congestion. It also refines our recent work ( 15), which considers only three components ( Dcol,
Dpot and Dexcess). Transportation agencies measure recurrent congestion in various ways and find
it accounts for 40%- 70% of total congestion ( 5). The availability of more comprehensive data
has prompted attempts to separately estimate the contribution of different causes of congestion.
There are studies that divide total congestion into ‘ recurrent’ and ‘ non- recurrent’ congestion; and
studies that divide the non- recurrent congestion into accident- induced congestion and other
incident- induced congestion. There also are estimates of the congestion caused by adverse
weather. These studies are reviewed in the next section.
These studies leave a large fraction ( between 40 and 70 percent) of the total congestion
unexplained. This unexplained residual is often called ‘ recurrent’ congestion. As Hallenbeck et
al. observe, “ Many large delays still occur for which incidents are not responsible, and for which
no ‘ cause’ is present in the [ data].” They suggest that one cause of these delays may be “ unusual
volume surges at ramps ... that are not being effectively handled by the ramp metering program”
( 2, p. 11). The proposed method estimates this potential reduction in delay, Dpot.
The report is organized as follows. Previous studies are reviewed in Section 2. The proposed
method is described in Section 3. The congestion components of I- 880 are determined in Section
4. Section 5 summarizes a simpler method that relies on routine data collected by PeMS. Section
6 concludes the paper. Some remarks about data collection are provided in the Appendix.
1 Congestion ( delay) is measured as the extra vehicle- hours spent driving below 60 mph on a freeway.
4
2. Previous Studies
Transportation agencies until recently only reported recurrent congestion. ( For an example see
( 7) 2; for an extensive survey of the practice see ( 5).) The availability of more comprehensive data
has inspired studies to quantify the relative impact of different causes of congestion.
Several studies estimate the impact of incidents. The earliest studies relied on correlating
specially- collected incident data using ‘ floating cars’ with loop- detector data ( 8). These data
provide a great deal of information about the nature of incidents, but the data collection efforts
are too expensive to replicate on a large scale or on a continuing basis. 3
Data from California Highway Patrol computer aided dispatch ( CAD) and Freeway Service
Patrol ( FSP) logs were used to evaluate FSP effectiveness in Los Angeles freeways ( 9) and in
Oregon ( 10). These studies need much human effort, data analysis skill, and subjective judgment
in determining the spatial and temporal region of the congestion impact of an incident. Our
previous work ( 15) developed an automated method to delineate an incident’s impact region. But
that approach requires accurate time and location of incidents, which may not be routinely
available.
Determining every individual incident’s impact region can be avoided if one is willing to average
out the impact of individual incidents as in ( 2, 3). 4 Both studies separate ‘ non- recurrent’ and
‘ recurrent’ congestion but differ in definition and method. We pause to briefly discuss how this
‘ separation’ poses conceptual and measurement questions.
From knowledge of the location and time of an incident one can estimate the total delay in a
space- time region that includes the impact of the incident ( 15). However, one needs to separate
this total delay into recurrent delay— that is the delay that would have occurred in the absence of
the incident and the additional delay that was caused by the incident. The conceptual challenge
is how to estimate the recurrent delay component, since it cannot be directly measured. The
measurement question is how to estimate the space- time region of impact of an incident when
the exact time and location of the incident are not known with the required accuracy.
We also note more subtle questions of causation that our study does not address. For example,
how should one divide the total congestion caused by two nearby incidents? How should one
account for the fact that ( recurrent) congestion may itself increase the likelihood of accidents?
How should one estimate the impact of an accident that causes congestion to spread into a
different freeway ( via a freeway- freeway connector) or into arterials as drivers respond to traffic
information by diverting?
While we have discussed ‘ separation’ in terms of estimating the impact of incidents, the same
questions arise in estimating the impact of other causes: special events, adverse weather, lane
2 The HICOMP report publishes recurrent congestion on urban area freeways for typical weekday commute periods.
The report defines recurrent congestion as a “ condition lasting for 15 minutes or longer where travel demand
exceeds freeway design capacity and vehicular speeds are 35 miles per hour ( mph) or less during peak commute
periods on a typical incident- free weekday.”
3 The rich information revealed in ( 8) inspired the PeMS project, which automates the collection and processing of
freeway loop detector data.
4 This limits the use of the results predicted by our model. The prediction is reasonable in terms of the average
contribution of incidents to delay, but not in terms of contribution of particular incidents.
5
closures, etc. For each cause the total delay is ( relatively) easy to measure; the difficulty is to
separate this delay into a recurrent component and a component that should be attributed to the
cause being analyzed. Naturally, the difficulty is compounded when there are multiple causes.
For example, adverse weather increases incidents as well as recurrent delay, and it is not clear
how to separate the different impacts.
We return to review how previous studies have addressed separation. Skabardonis et al. ( 3)
consider a freeway section during a peak period. The total congestion on each of several days is
calculated as the additional vehicle- hours spent driving below 60 mph ( see equation ( 1) below).
Each day is classified as ‘ incident- free’ or ‘ incident- present’. The average congestion in
‘ incident- free’ days is defined to be the recurrent delay. Total congestion in incident- present days
is considered to be the sum of recurrent and incident- induced congestion. Subtracting average
recurrent congestion from this gives an estimate of the average non- recurrent or incident- induced
congestion. On the other hand, Hallenbeck et al. ( 2) take the median traffic conditions on days
when a freeway section does not experience lane- blocking incidents as the “ expected, recurring
condition.”
A less data- intensive approach is taken by Bremmer et al. ( 4). In the absence of incident data,
they simply assume that an incident has occurred if a trip “ takes twice as long as a free- flow trip
for that route.” The aim of this study is to forecast travel times, measure travel time reliability,
and conduct cost- benefit analysis of operational improvements, rather than to measure the
congestion contribution of different causes.
Lastly, the impact of inclement weather on freeway congestion is studied in ( 11, Chapter 22) and
( 12), which find that light rain or snow, heavy rain, and heavy snow reduces traffic speed by 10,
16, and 40 percent, respectively.
3. Proposed Method
The method applies to a contiguous section of freeway with n detectors indexed i = 1,…, n,
whose flow ( volume) and speed measurements are averaged over 5- minute intervals indexed t =
1,…, T. 5 Days in the study period are denoted by d = 1,2,…, N. Detector i is located at postmile
xi; vi( d, t) = v( xi, d, t) is its speed ( miles per hour, mph) and qi( d, t) = q( xi, d, t) is its flow ( vehicles
per hour, vph) at time t of day d.
The n detectors divide the freeway into n segments. Each segment’s ( congestion) delay is
defined as the additional vehicle- hours traveled driving below free flow speed vref, taken to be 60
mph. So the delay in segment i in time t is
Di( d, t) = li × qi( d, t) × max{ 1/ vi( d, t) − 1/ vref, 0} vehicle- hours, ( 1)
in which li is the segment length in miles. The total delay in the freeway section on day d is the
delay over all segments and times,
Dtotal( d)= ΣΣ
= =
n
i
T
t
Di d t
1 1
( , ). ( 2)
5 The reader may recognize that this formulation of the available data conforms to the data available in PeMS.
6
The average daily total delay is simply
Dtotal = Σ=
N
d
Dtotal d
N 1
1 ( ) . ( 3)
In the application below we separately consider the daily delay over two peak periods: 5- 10 AM
for the morning peak and 3- 8 PM for the afternoon peak.
Incidents are indexed a = 1, 2, … . The time τ a
when incident a occurs and its location σ a
are
approximately known. The incident clearance time and the spatial and temporal region of the
incident’s impact are not known.
Decomposition of Delay
The method divides the average daily total delay ( 3) into six components,
. total col event lane weather pot excess D = D + D + D + D + D + D ( 4)
It will be useful to define
, non rec col event lane weather D = D + D + D + D − ( 5)
. rec tot non rec pot excess D = D − D = D + D − ( 6)
Above,
Dcol is the daily delay caused by incidents,
Devent is the daily delay caused by special events,
Dlane is the daily delay caused by lane closure,
Dweather is the daily delay caused by adverse weather condition,
Dpot is the potential reduction of Drec by ramp metering,
Dexcess is the residual delay, attributed mostly to excess demand,
Drec is the daily ‘ recurrent’ delay, and
Dnon- rec is the daily ‘ recurrent’ delay.
Dtotal, calculated from flow and speed data, is the average daily total delay. Dcol, Devent, Dlane and
Dweather are components of so- called ‘ non- recurrent’ congestion. The difference between their
sum and Dtotal is the ‘ recurrent’ congestion ( 2, 3). A portion of recurrent congestion due to
frequently occurring bottlenecks could, in principle, be reduced by ramp metering. That potential
reduction is estimated as Dpot. The remaining delay, Dexcess, is due to all other causes, most of
which is likely due to demand in excess of the maximum sustainable flow. The delay due to
excess demand can only be reduced by changing trip patterns. We now describe how each
component of ( 4) is estimated.
Non- Recurrent Delays
The components of non- recurrent delay are identified using the following model,
7
Dtotal( d) = β0 + βcol Xcol( d) + βevent Xevent( d) + βlane Xlane( d) + βweather Xweather( d) + ε( d), ( 7)
in which
ε( d) is the error term with mean zero,
Xcol( d) is the number of incidents on day d,
Xevent( d) is the number of congestion- inducing special events such as sport games on day
d,
Xlane( d) is the number of lane- closures on day d, and
Xweather( d) is the 0- 1 indicator of adverse weather condition on day d.
The explanatory variables listed above are used in our application, but the list could be
augmented if additional data are available. For example, Xevent( d) could be the attendance at
special events instead of the number of special events; Xlane( d) could be the duration instead of
the number of lane closures; and Xweather( d) could be the precipitation ( as in our application).
The model assumes that each incident, special event, lane- closure, and adverse weather condition
contributes linearly or additively to the delay. Figure 2 illustrates that such model is reasonable
for our study site.
More complicated causality relations between explanatory variables, such as between the bad
weather and the number of accidents, are not considered to keep the number of parameters in the
model small. But if one has enough data and the interaction is strong enough, such interaction
terms could be included. For example, interaction between adverse weather and collisions could
be modeled by adding a multiplicative term such as β Xcol( d) Xweather( d). For the San Francisco
Bay Area data considered below, however, the correlation coefficient between precipitation and
number of accidents is only 0.032 so such a multiplicative term would contribute nothing to
explaining the total delay.
Fitting the model to the data via linear least squares gives the parameter estimates, again denoted
β0, βcol, βevent , βlane and βweather. The components of the total delay then are
Dcol = βcol × avg{ Xcol( d)}, ( 8)
Devent = βevent × avg{ Xevent( d)}, ( 9)
Dlane = βlane × avg{ Xlane( d)}, and ( 10)
Dweather = βweather × avg{ Xweather( d)}, ( 11)
in which the average is taken over days, d = 1,…, N.
The intercept β0 in ( 7) is the delay when there are no incidents, special events, lane- closures, or
adverse weather. Thus, consistent with convention, it may be identified with recurrent
congestion, since it equals total delay minus the non- recurrent delay Dnon- rec defined above,
β0 = Drec = Dtotal – Dnon- rec. ( 12)
8
Recurrent Delay Algorithm: Separating Recurrent and Non- recurrent Congestion
The next step is to divide the recurrent delay into the delay that can be eliminated by ramp
metering and the delay due to excess demand. For this, the method identifies recurrent
bottlenecks on the freeway section using the automatic bottleneck identification algorithm
proposed in ( 13). 6 Then the ideal ramp metering ( IRM) is run on those recurrent bottlenecks that
are activated on more than 20% of the weekdays considered ( 14, 15).
Here is a brief description of the IRM algorithm. For a specific recurrent bottleneck, let segment
i and j be the upstream and downstream boundaries of the bottleneck, respectively. For the
upstream boundary j, we use the median queue length of the bottleneck. Then we compute the
total peak period volume at the two locations. The difference between the two would be the
difference between the total number of cars incoming or exiting the freeway between the two
segments. We assume that all those cars contributing to the difference are arriving ( or leaving) at
a virtual on- ramp ( off- ramp) at the upstream segment i. Also, the time- series profile of that extra
traffic is assumed identical to the average of those at segment i and j. That enables us to compute
the modified total input volume profile at the segment i. The capacity of the whole section is the
maximum sustainable ( over 15- minute) throughput at location j and we compute this from the
empirical data. We meter the virtual input volume at segment i at 90% of Cj to prevent the
breakdown of the system, assuming:
( 1) The metered traffic will be in free flow ( 60 mph) throughout the freeway section, and
( 2) The upstream metered ramp has infinite capacity.
Thus, under IRM, the delay occurs only at the metered ramps. The potential savings from IRM at
these bottlenecks for each day d is then computed as,
Dpot( d) = DBN, before IRM( d) - DBN, after IRM( d). ( 13)
Here DBN, before IRM( d) and DBN, after IRM( d) is the delay at the bottlenecks before and after IRM is
run. The average daily potential saving is
Dpot = min { median( Dpot( d), d = 1, …), Drec}. ( 14)
In ( 14) the median instead of the mean is used to ensure that the influence of incidents and
special events etc. is minimized in the computation. Also, the potential saving can’t be larger
than the total recurrent delay Drec, hence the ‘ min’ in formula ( 14).
Due to the ‘ ideal’ nature of IRM, Dpot need to be interpreted with caution. Especially, the
assumption of a very large, though not infinite, capacity at the meter is not realistic for many
urban freeways and metering at certain locations can lead to breakdown of arterial traffics nearby.
Thus, it is recommended that Dpot be viewed as the maximum possible saving in the recurrent
delay by metering. 7
6 The algorithm in ( 13) has been implemented as part of PeMS’ bottleneck application, which provides a list of
bottlenecks, the delay and extent ( or queue length) of the congestion each bottleneck causes, and its frequency of
occurrence.
7 The emphasis in this research is on developing a method to estimate the congestion pie statewide, using readily
available data. For this reason, we resort to a rough, but easily automated, method. A more refined estimate would
9
Congestion Pie
The method described above divides the average daily total delay Dtotal into six components,
summarized in easily understood pie charts like those in Figure 4, which may be compared with
the FHWA pie of Figure 1.
4. Case Study
The method is applied to a 45.33 mile ( postmile .39 to 45.72) section of southbound ( SB) and
northbound ( NB) I- 880 in the San Francisco Bay Area. Two time periods are considered: AM
peak, 5- 10 AM; and PM peak, 3- 8 PM. Data cover 110 weekdays during January 5– June 30,
2004. There are four scenarios, distinguished by peak period and freeway direction: SB AM, SB
PM, NB AM and NB PM.
Data Sources
Traffic Speed and Volume Data
The 90 ( NB) and 94 ( SB) loop detector stations in the section provide 5- minute lane- aggregated
volume and speed data, available at the PeMS website ( 16).
Freeway Service Patrol ( FSP) Incidents
Incident data are for Freeway Service Patrol ( FSP) assisted incidents. On an average non- holiday
weekday the FSP assists upwards of 80 motorists on I- 880 during 6: 00- 10: 00 AM and 3: 00- 7: 00
PM. FSP peak hours are an hour shorter than peak hours used for computing total delay ( 5- 10
AM and 3- 8 PM) but we don’t expect the effect to be substantial. On weekends and holidays,
FSP assistance is not provided. FSP drivers record the date and time, duration, freeway name
and direction, incident description ( e. g. traffic accident, flat tire, out- of- gas), and location ( e. g.
on- or off- ramp, left shoulder, right shoulder, in- lane). We only consider in- lane incidents ( as
opposed to those on the left or right shoulder or on a ramp) during peak hours. There were 829
such incidents during the study period.
Special Events
On 45 out of 110 weekdays, there were special events in the Oakland Coliseum, near postmile 36
of I- 880, including baseball ( the Oakland A’s) and basketball ( the Golden State Warriors) games
and show performances, mostly starting at 7 PM. Data were provided by Networks Associates
Coliseum & The Arena in Oakland.
Weather
Weather data were collected from California Department of Water Resource ( DWR) for
“ Oakland north” ( station ID “ ONO”) station ( 17). The station reports daily precipitation,
temperature, wind speed and direction, etc; only precipitation was considered in the analysis.
require a simulation of how much delay reduction is possible using realistic ramp metering strategies. Such an
approach is being pursued under California PATH Task Order 6611, Tools for Operations Planning ( TOPL).
10
Lane closure
Lane closure data were obtained from the Lane Closure System ( LCS) managed by California
Department of Transportation ( 18). LCS records include, for each lane closure:
Location: freeway, direction, county, and postmile,
Begin/ End date and time,
Facility/ Lanes: on/ off- ramp, # lanes, which lanes, and
Type of work: sweeping, construction, etc.
For the first half of 2004, for NB I- 880, there were 224 lane closures, 126 of them in the traffic
lanes. It turns out that all day time closures were ‘ sweeping’ or ‘ call box remove/ repair’, which
involve a moving closure of at most one lane and have negligible impact on congestion. All
congestion- inducing lane closures ( repair, striping, and paving) occurred at night ( after 10 PM
and before 5 AM) or on weekends outside the AM and PM peaks. This was also the case for SB
880. Thus we assign Dlane = 0 for all scenarios.
Results
Table 1 summarizes the regression results for non- recurrent congestion. The last column shows
the multiple R- squared values for each scenario, which is the ratio of the sum of squares of the
delay explained by the regression model and the total sum of squares around the mean. The F-statistic
for testing whether the fit of the model is valid is significant with practically zero P-value
for all four scenarios, suggesting the linear regression model successfully explains the
delay variation. We also observe:
1. βevent is statistically significant ( P- value < .10) only for PM shifts. This is to be expected
since most special events occur in the afternoon or evening. Each special event on
average contributes a delay of 1,084 and 705.5 veh- hrs for NB and SB respectively.
2. βcol is statistically significant ( P- value < .001) only for PM shifts. This suggests that a
larger portion of the congestion in the morning peak hours is recurrent in nature than in
the afternoon/ evening. In PM shifts, each incident contributes a delay of 486.13 ( NB) and
383.75 ( SB) veh- hrs on the average.
3. βweather is statistically significant ( P- value < .001) only during AM shifts. On average one
inch of rain adds 1305.7 ( NB) and 2125.6 ( SB) veh- hrs of delay. Note that it rained on
29 out of 110 weekdays; the median precipitation was .13 inches, and the maximum was
2.44 inches.
Figure 2 shows the relationship between Dtotal and some of the explanatory variables illustrating
the correlation between the total delay and those variables.
Next, formulas ( 8)-( 11) are used to compute the delay components shown in Table 2. Before
applying the formula, we set to zero those regression coefficients that are not statistically
significant at significance level 0.1.
The automatic bottleneck detection algorithm is applied to speed data of the kind whose contour
plot is shown in Figure 3. Clearly visible in the figure are an AM bottleneck near postmile 10
11
and a larger PM bottleneck near postmile 27. Dpot and Dexcess are computed from the IRM
algorithm and shown in the right columns of Table 2. About 44% of recurrent delay can
potentially be eliminated by ideal ramp metering. Note that Dpot and Dexcess were extrapolated
from district wide quantities; freeway- specific computation is now available in PeMS.
From the charts in Figure 1 one can conclude:
1. One- third of the congestion delay occurs at recurrent bottlenecks and can be potentially
eliminated by ideal ramp metering.
2. One- half of the delay is due to excess demand in both directions, and can be reduced only
by changing trip patterns.
3. Incidents and special events contribute 18% of the delay. The former can be reduced by
more rapid detection and response; impact of special events may be reduced by
information on changeable message signs.
The 486.13 ( NB) and 383.75 ( SB) vehicle- hours of delay per incident for the PM shift is in
rough agreement with other estimates. A regression of total daily delay vs. number of accidents
for all of Los Angeles yields a slope of 560 vehicle- hours per accident ( 6, p. 20). For southbound
I- 5 in Seattle, Hallenbeck et al. find that a lane- blocking incident causes between 318
( conservative estimate) and 591 ( liberal estimate) vehicle- hours of delay ( 2, p. 15).
The average daily delay caused by incidents, Dcol, is 986 and 837 vehicle- hours, which is 20.3%
and 18.8% of total PM delay for NB and SB, respectively. By way of comparison, Hallenbeck et
al. find that “ for the urban freeways examined [ in the Central Puget Sound region of Washington
State] lane- blocking incidents are responsible for between 2 and 20 percent of total daily delay”
( 2, p. 8). These average numbers must be used with caution because the delay impact of incidents
varies considerably from freeway to freeway and over different times of day. For example, in our
study, during the AM peak ( 5- 10 AM), the average incident- induced delay is 0 ( because βcol is
not significantly different from 0) for NB and 9.9% of the total peak hour delay for SB.
Aggregating over both peaks and both directions, the delay components are 13.3%, 4.5%, 1.6%,
33.2%, and 47.4% for incidents, special events, rain, potential reduction and excess demand.
5. Statewide implementation
A modified ( simpler) version of the method described above has been implemented in PeMS.
The differences stem from data sources and method.
PeMS uses 1) the Caltrans TASAS incident database, 2) its bottleneck identification algorithm,
and 3) the Ideal Ramp Metering algorithm described above. PeMS divides
Dtotal, the total daily delay on the freeway, into
Dcol, the delay that is assigned to collisions;
Dpot, the potential delay that can be saved by running an ideal ramp metering algorithm at
the major bottlenecks on the freeway;
Dexcess, the delay caused by excess demand that no ramp metering algorithm could reduce;
and
Dmisc, the delay that cannot be assigned to collisions or bottlenecks.
12
There is a slight difference in method having to do with the division of the recurrent delay Drec
into Dpot and Dexcess. In the method described here, by definition, Dexcess = Drec - Dpot comprises
all of the recurrent delay that cannot be reduced by the Ideal Ramp Metering. By contrast, in
PeMS, Dexcess ( PeMS) = Dbn- Dpot , in which Dbn is the total delay that occurs only at bottlenecks;
whereas Dmisc = Dtotal - Dcol - Dbn. Thus Dexcess ( PeMS) is smaller than the estimate of Dexcess
provided here.
Figure 5 displays the congestion pie from PeMS for 2004 Q1, which roughly coincides with the
study period.
6. Conclusion
Between 1980 and 1999, highway route- miles increased 1.5 percent while vehicle miles of travel
increased 76 percent ( 1). In 2000, the 75 largest metropolitan areas experienced 3.6 billion hours
of delay, resulting in $ 67.5 billion in lost productivity, according to the Texas Transportation
Institute. Mitigating congestion through more efficient operations is a priority of transportation
agencies. The first step in designing an effective mitigation strategy is to know how much each
cause contributes to congestion. One can then design a set of action plans, each aimed at
reducing the contribution of a particular cause. The more detailed the set of causes that are
considered, the more effective the strategy that can be devised.
The research summarized here proposes a fully automated method that calculates six components
of congestion: delay attributed to incidents, special events, lane closures, and weather; delay that
can be eliminated by ramp metering; and the remaining delay, mostly due to excess demand.
The method is applied to a 45- mile section of I- 880 in the San Francisco Bay Area for AM and
PM peaks and for both directions. Incidents and special events together account for 17.8% of
total delay. Lane closures caused no delay because delay- causing closures were not scheduled
during peak hours. Rain caused 1.6% of total delay. A surprisingly large 33% of all delay could
be eliminated by ideal ramp metering. Lastly, 47% of the delay is due to excess demand.
Certainly, as discussed in the text, the 33% potential reduction due to metering needs to be
interpreted with caution, as the maximum possible reduction. Even with such precaution, if these
estimates are supported in more detailed studies, it is likely that most congestion mitigation
strategies would harvest large potential gains from ramp metering.
A slight modification of the method proposed here has already been implemented in PeMS’
‘ congestion pie’ application and can be applied to any freeway or district.
Acknowledgement
This report summarizes the joint work under TO 5321 of Jaimyoung Kwon, Michael Mauch and
Pravin Varaiya. Lisa Davis and Jose Perez of Caltrans and Alex Skabardonis of U. C. Berkeley
helped us with their comments and guidance during the course of this project; Jacqueline Ghezzi
provided lane closure data; and Networks Associates Coliseum & The Arena in Oakland
provided data on special events. We are grateful to them all. The contents of this paper reflect the
views of the authors who are responsible for the facts and the accuracy of the data presented
herein. The contents do not necessarily reflect the official views of or policy of the California
13
Department of Transportation. This report does not constitute a standard, specification or
regulation.
References
[ 1] FHWA. FHWA Congestion Mitigation website. http:// www. fhwa. dot. gov/
congestion/ congest2. htm, Last Accessed July 1, 2005.
[ 2] Hallenbeck, M. E., J. M. Ishimaru, and J. Nee. Measurement of recurring versus non-recurring
congestion. Washington State Transportation Center ( TRAC), October 2003.
[ 3] Skabardonis, A., K. Petty, and P. Varaiya. Measuring recurrent and non- recurrent traffic
congestion. In Proceedings of 82nd Transportation Research Board Annual Meeting,
Washington, D. C., January 2003.
[ 4] Bremmer, D., K. C. Cotton, D. Cotey, C. E. Prestrud, and G. Westby. Measuring congestion:
Learning from operational data. In Proceedings of 83rd Transportation Research Board
Annual Meeting, Washington, D. C., January 2004.
[ 5] Dowling Associates, Berkeley Transportation Systems and System Metrics Group.
Measuring Non- Recurrent Traffic Congestion: Final Report. Prepared for California
Department of Transportation, June 2002.
[ 6] System Metrics Group. Freeway performance report. Prepared for California Department of
Transportation, 2003.
[ 7] California Department of Transportation. 2002 HICOMP Report. State Highway
Congestion Monitoring Program, November 2003.
[ 8] Petty, K., H. Noeimi, K. Sanwal, D. Rydzewski, A. Skabardonis, P. Varaiya, and H. Al-
Deek. The freeway service patrol evaluation project: Database support programs, and
accessibility. Transportation Research, Part C, 4 ( 2): 71– 85, April 1996.
[ 9] Skabardonis, A., K. Petty, P. Varaiya, and R. Bertini. Evaluation of the Freeway Service
Patrol ( FSP) in Los Angeles. Research Report UCB- ITS- PRR- 98- 31, California PATH,
University of California, Berkeley, CA 94720, 1998.
[ 10] Bertini, R., S. Tantiyanugulchai, E. Anderson, R. Lindgren, and M. Leal. Evaluation of
Region 2 Incident Response Program using archived data. Transportation Research Group,
Portland State University, July 2001.
[ 11] Transportation Research Board. Highway Capacity Manual 2000, December 2000.
[ 12] Chin, S. M., O. Franzese, D. L Greene, H. L. Hwang, and R. C. Gibson. Temporary losses of
highway capacity and impacts on performance. Technical Report ORNL/ TM- 2002/ 3, Oak
Ridge National Laboratory, National Transportation Research Center, Knowville, TN
37932, May 2002.
[ 13] Chen, C., A. Skabardonis, and P. Varaiya. Systematic identification of freeway bottlenecks.
In Proceedings of 83rd Transportation Research Board Annual Meeting, Washington, D. C.,
January 2004.
[ 14] Jia, Z., P. Varaiya, C. Chen, K. Petty, and A. Skabardonis. Congestion, excess demand and
effective capacity in California freeways. Online at pems. eecs. berkeley. edu, December
2000.
[ 15] Kwon, J. and P. Varaiya. The congestion pie: delay from collisions; potential ramp
metering gain, and excess demand. Proceedings of 84th Transportation Research Board
Annual Meeting, Washington, D. C., January 2005.
[ 16] PeMS. PeMS website. http:// pems. eecs. berkeley. edu.
[ 17] California Department of Water Resource Website, http:// cdec. water. ca. gov/ intro. html, Last
Accessed November 11, 2004.
14
[ 18] California Department of Transportation, Lane Closure System ( LCS) Website
http:// www. lcs. dot. ca. gov/, Last Accessed November 3, 2004.
[ 19] FHWA. Trends and Advanced Strategies for Congestion Mitigation, 2005.
http:// www. ops. fhwa. dot. gov/ congestion_ report/, Last Accessed May 3, 2007.
15
Table 1 Regression Result for Non- Recurrent Delay
Scenario Factor Estimate Std. Error t value
Multiple R-squared
NB AM ( Intercept) 3,301.1 191.1 17.28 0.000 *** 0.12
Event - 221.5 216.2 - 1.03 0.308
Incident 115.8 74.2 1.56 0.122
Weather 1,305.7 384.4 3.40 0.001 ***
NB PM ( Intercept) 3,419.7 408.1 8.38 0.000 *** 0.14
Event 1,084.6 416.0 2.61 0.010 *
Incident 486.1 133.9 3.63 0.000 ***
Weather 75.4 732.7 0.10 0.918
SB AM ( Intercept) 3,402.6 339.6 10.02 0.000 *** 0.17
Event - 482.0 342.2 - 1.41 0.162
Incident 221.1 127.6 1.73 0.086 .
Weather 2,125.6 598.5 3.55 0.001 ***
SB PM ( Intercept) 3,311.1 374.8 8.83 0.000 *** 0.12
Event 705.5 419.9 1.68 0.096 .
Incident 383.8 116.9 3.28 0.001 **
Weather 28.7 751.3 0.04 0.970
Pr(>| t|) 1
1. Significance codes “***”, “**”, “*” and “.” mean the P- value is between 0 and .001, between
.001 and .01, between .01 and .05, and between .05 and .1, respectively.
16
Table 2 Delay Contributions from Each Cause and Congestion Pie1
Scenario Factor β
Mean
Weakday
Occurrences
Delay
Contributions
( veh- hrs)
Factor,
after
Bottleneck
Analysis
Delay
Contributions
( veh- hrs)
Percent
of
Total
Delay
NB AM Recurrent 3,301 NA 3,301 Pot 1,307 38.4%
NA NA NA Excess 1,994 58.6%
Event 0 0.42 0 Event 0 0.0%
Incident 0 1.55 0 Incident 0 0.0%
Weather 1,306 0.08 102 Weather 102 3.0%
NB PM Recurrent 3,420 NA 3,420 Pot 1,336 27.5%
NA NA NA Excess 2,084 42.9%
Event 1,085 0.42 454 Event 454 9.3%
Incident 486 2.03 986 Incident 986 20.3%
Weather 0 0.08 0 Weather 0 0.0%
SB AM Recurrent 3,403 NA 3,403 Pot 1,327 33.5%
NA NA NA Excess 2,076 52.4%
Event 0 0.42 0 Event 0 0.0%
Incident 221 1.78 394 Incident 394 9.9%
Weather 2,126 0.08 166 Weather 166 4.2%
SB PM Recurrent 3,311 NA 3,311 Pot 1,565 35.2%
NA NA NA Excess 1,746 39.3%
Event 705 0.42 295 Event 295 6.6%
Incident 384 2.18 837 Incident 837 18.8%
Weather 0 0.08 0 Weather 0 0.0%
1. NA means the number is not needed.
17
Figure 1 The FHWA congestion pie. Source ( 19).
18
0 1 2 3 4 5 6 7
2000 6000 10000 14000
NB PM
FSP Incidents
Delay ( veh- hrs)
FALSE TRUE
2000 6000 10000 14000
NB PM
PM Events
Delay ( veh- hrs)
FALSE TRUE
2000 4000 6000 8000
SB AM
Precipitation > 0.13 in.
Delay ( veh- hrs)
FALSE TRUE
2000 4000 6000
NB AM
Precipitation > 0.13 in.
Delay ( veh- hrs)
Figure 2 Relationship between delay and selected factors. The distribution of the average
daily total delay Dtotal( d), summarized as the box- and- whisker plot, is shown for each level
of the number of incidents ( upper left), special event occurrence ( upper right), or adverse
weather condition ( bottom plots).
19
Figure 3 Lane- aggregated speed by postmile and time of day for I- 880 S on April 2, 2004.
20
Precip.
Potential
Reduction
Excess
Demand
NB AM
Events
Incidents
Potential
Reduction
Excess
Demand
NB PM
Incidents
Precip.
Potential
Reduction
Excess
Demand
SB AM
Events
Incidents
Potential
Reduction
Excess
Demand
SB PM
Figure 4 Congestion pie chart for four scenarios on I- 880.
21
Figure 5 Congestion breakdown for I- 880 for 2004Q1. NB: AM ( top left), PM ( top right);
SB: AM ( bottom left), PM ( bottom right). Source: PeMS ( 16).
22
Appendix: Data Issues
This appendix discusses issues relating to data.
Routine data
Routine data for volume ( flow), speed, freeway configuration ( length) etc. are obtained from
PeMS, which provides 5- min aggregated values and freeway configuration.
Incidents
There are many choices, each of which has problems. Ideally, one would like to know the
location of an incident ( post mile, lane), its nature ( collision, breakdown), and the response
( clearance time). Unfortunately, these are not available.
PeMS uses TASAS data, which do provide location and nature of incidents, but no clearance
time. TASAS data do not contain all incidents, e. g. breakdowns are not recorded. The major
difficult is that there is a significant time lag ( up to six months) in the availability of TASAS
data.
FSP ( Freeway Service Patrol) data provide a rough location and nature of the incidents.
However, FSP data are only available for the time periods when FSP is in operation.
CHP ( California Highway Patrol) data records are available in PeMS, which gets them from the
CHP website. PeMS parses the CHP logs and for some of the incidents it is able to determine
the nature of the incident.
BAIRS ( Bay Area Incident Response System) identifies and directs local personnel and.
equipment to traffic obstructions. It has proved successful in reducing the response time by
quickly locating and dispatching crews and equipment.
Lane closures
This study relied on the Lane Closure System database. The major difficulty in using the
database is that it records lane closure requests but not whether the lane closure was actually
implemented. This makes the database not useful.
Weather
The Department of Water Resources ( DWR) ( http:// cdec. water. ca. gov/ intro. html) provides
historical and hourly ( in some cases) measurements of ( 1) precipitation, ( 2) temperature, and ( 3)
wind. Unfortunately, there are no measurements of fog, which is sometimes significant in the
Bay Area.
There is one station near North Oakland, measurements from which are used in this study.
However, the micro- climate variations in the Bay Area means that we use county- wide
measurements for I- 880 conditions, which is not accurate.