PeMS 6 - Institute of Transportation Studies - University of California ...

CALIFORNIA CENTER FOR INNOVATIVE TRANSPORTATION
INSTITUTE OF TRANSPORTATION STUDIES
UNIVERSITY OF CALIFORNIA, BERKELEY
Freeway Performance Measurement System
(PeMS), PeMS 6
Pravin Varaiya, PhD
Department of Electrical Engineering and Computer Science
CCIT Research Report
UCB-ITS-CWP-2007-7
ISSN 1557-2269
The California Center for Innovative Transportation works with
researchers, practitioners, and industry to implement transportation
research and innovation, including products and services that improve
the efficiency, safety, and security of the transportation system.

Freeway Performance Measurement System (PeMS), PeMS 6:
Final Report for CCIT TO 15
Pravin Varaiya
University of California, Berkeley CA 94720
Department of Electrical Engineering and Computer Science
Tel: (510) 642-5270, Fax: (510) 643-2356
varaiya@eecs.berkeley.edu
February 18, 2006
Executive summary
Under development and operation since 1999, PeMS now is the de facto repository for all
Caltrans loop detector data. The system is used to investigate everything from the
characteristics of individual loop detectors to highly aggregated performance trends
across Caltrans districts. PeMS supports 4000-5000 web hits per day. It distributes data
to 55 registered Value Added Resellers, 20 of which actively retrieve real-time data.
PeMS is located on the U.C Berkeley campus. In summer 2006, a copy of PeMS will
start operating within Caltrans. Caltrans PeMS will be a 24×7 production facility.
Campus PeMS will then become a platform for developing new algorithms and features.
Once or twice a year, a version of the Campus PeMS software will be uploaded to
Caltrans PeMS.
The PeMS 6 effort completed six tasks.
1. Components of total congestion
The ‘congestion pie’ application divides total congestion delay into four components: the
portion of recurrent congestion that can be eliminated by ramp metering; the portion due
to excess demand; collision-induced congestion; and the remainder. The algorithm can
be applied at the level of a freeway, district or the entire state.
For the first time, Caltrans management can obtain quantitative estimates of the
contribution to congestion by different causes; set congestion reduction targets; and
monitor the effectiveness of congestion relief strategies.
2. Bottleneck analysis
This application locates the bottlenecks for any freeway and determines how frequently
each bottleneck is activated, the delay it causes, its duration in time, and the extent of the
congestion queue.
The application was used to locate the most important bottlenecks in the state. The
application provides a scientific basis for directing resources to combat congestion.
3. Productivity
Congestion causes a drop in flow (volume). The resulting productivity drop can be
estimated in terms of lane-mile-hours of capacity lost. In money terms, this loss is the
capital cost of constructing a certain number of lane-miles of freeway.

4. Truck traffic
Single-loop data are processed to provide real-time estimates of truck traffic. For the first
time, Caltrans has some understanding of truck traffic statewide.
5. Travel time
This application permits specification of a route; calculates travel time statistics (average,
variability) for the route; and gives a real-time prediction of the travel time for any
departure time. It opens the way for posting travel times on changeable message signs
throughout the state.
6. Maintain and support Campus PeMS
This continuing task is undertaken by Berkeley Transportation Systems (BTS).
Introduction
PeMS 6 is the latest of six task orders devoted to research, development, and maintenance
of the PeMS system. PeMS collects, processes, stores, and makes available online data
from eight Caltrans districts (D3, 4, 5, 6, 7, 8, 11, 12). The data are obtained from 22,067
loops 1 , grouped into 8,649 vehicle detector stations (VDS). These loops cover 3,154 out
of 30,572 directional-miles of interstate and state highways in California.
PeMS began as a research project. As the research system evolved, Caltrans determined
that the information it provided was very valuable, and additional resources were then
directed towards the development of PeMS. Faculty, post-doctoral fellows, and graduate
student researchers at U.C. Berkeley (UCB) conduct the research element of the project.
Berkeley Transportation Systems (BTS) is responsible for PeMS software development
and system maintenance. The UCB and BTS groups meet weekly. There are periodic
conferences with members of Caltrans Division of Traffic Operations. The project is
now administered through CCIT and co-managed by the PeMS PI and the Division of
Traffic Operations.
The product of the research activity is reported in professional meetings and journals and
in algorithms. The algorithms are incorporated into PeMS software as part of BTS’
development activity. (Generally speaking, there is a one year lag between research
results and their implementation.) We now describe the accomplishments under the six
tasks that constitute the PeMS 6 project.
Task 1 Components of total congestion
A report by Dowling Associates [2] proposed a methodology for dividing the total delay
on a freeway segment into recurrent and non-recurrent delay. The total delay is obtained
from PeMS [1]. The recurrent delay is taken to be the average delay on the segment on
incident-free delays. The non-recurrent delay on a particular day (which may have
incidents) is taken to be the delay on that day minus the recurrent delay.
The approach in [2] was refined by dividing the delay on a particular delay into (1)
collision-induced delay; (2) the delay that could potentially be eliminated by ramp
1 Some of these ‘loops’ are microwave radar detectors that act like loops.
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metering; (3) delay due to excess demand (which can only be reduced by demand
management); and (4) the residual delay. The result is a congestion pie divided into four
component slices [3]. When additional data are available, the division can be refined, as shown
in [4].
Figure 1 Congestion pie for California. Source [1].
Figure 1 shows the result of selecting the ‘congestion pie’ application for California.
Figure 2 Details of congestion pie of figure 1. Source [1].
The congestion pie of Figure 1 is calculated by aggregating the congestion across all
freeways. Figure 2 gives the details for some freeways. By following the hyperlinks, one
can ‘drill’ down to find out how ‘total delay’, ‘potential reduction’, ‘excess demand’, and
‘accident’ are calculated. The accident-caused delay calculation is based on TASAS
data, which are only available through the end of 2004.
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Task 2 Bottleneck analysis
Congestion occurs at a bottleneck—a location at which traffic moves freely downstream
but slowly upstream. A computer program processes speed data to identify all
bottlenecks by searching for freeway locations with such speed differentials [5].
Bottleneck locations and their durations can be visually recognized from speed contour
plots like those shown in Figure 3 (dark areas correspond to lower speed). The severity of
a bottleneck is measured by the associated congestion delay.
Figure 3 Speed contour plots of I-15S, 5:00 -10:00 AM, between postmiles 10
and 30, for 20 weekdays in May, 2003, show recurring bottlenecks at postmiles
26 and 16. Traffic flows from top to bottom. Source [5].
Figure 4 illustrates PeMS’ ‘bottleneck analysis’ application for I-5N in D11. During the
30-day period, January 19-February 18, 2006, the bottleneck near Sea World Dr. was
activated on two days, causing on average a delay of 517.8 vehicle-hours, lasting 50
minutes, and creating a 2.7 mile-long queue. By following the hyperlinks in Figure 4, the
bottlenecks are located on a map (Figure 5). The size and colors of the circles that
indicate the bottleneck are keyed to the average delay and frequency of activation.
An early version of the bottleneck application was used to locate the most severe
bottlenecks statewide.
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Figure 4 Some bottlenecks on I-5N in D11. Source [1].
Figure 5 Map of bottlenecks. Source [1].
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Task 3 Productivity
The TMS Master Plan [6, Exhibit II-2] proposed a measure of productivity loss due to
congestion. This is the number of lane-mile-hours that are lost due to the freeway
operating under congested conditions. The PeMS ‘productivity’ application automates
the procedure. It calculates drop in flow due to the fact that the freeway is operating in
congested conditions instead of in free flow, multiplies the drop by the extent of freeway
(lane-miles) in congestion and the duration of congestion (hours) to obtain the lane-milehours
of lost capacity.
Figure 6 Lane-hour-miles of capacity loss on I-5N during 2/11-2/18/2006. Source [1].
Figure 6 shows the capacity loss on I-5N during the week of February 11-18, 2006.
Task 4 Truck traffic
Algorithms are proposed in [7, 8] to estimate truck traffic (VHT, VMT) using single-loop
detector data. These algorithms are now implemented in PeMS. Figure 7 shows the
hourly truck traffic in VMT on I-5N during 2/11-2/18/2006. Interestingly, the traffic is
heaviest around noon. PeMS also estimates truck VHT.
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Figure 7 Hourly truck traffic (VMT) on I-5N, 2/11-2/18, 2006. Souce [1].
Task 5 Travel time
Total congestion delay is the single best measure of freeway performance measure from
the viewpoint of Caltrans. Travel time is the most relevant measure from the public’s
viewpoint. The two are related by the accounting identity:
CongestionDelay
= Total travel time − Free flowtravel time
PeMS’ travel time application has two parts: route selection and travel time calculation.
A qualified user can create and name any route comprising contiguous freeway segments.
Figure 8 shows the route ‘Circuitous Route 1’ created by a user. It consists of 9 segments
on different freeways. Once the route is created, it can be retrieved by name to view
travel time estimates for that route.
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Figure 8 A complex route comprising 9 segments. Source: [1].
Figure 9 plots actual travel times along a route comprising a single segment on SR-55N,
for different departure times during February 7-9, 2005. The figure shows travel times
along the HOV lane, and lanes 1 and 4. It is interesting that during both morning and
afternoon peak hours, the travel time along the HOV lane is only slightly shorter than
along lane 1; outside the peak hours, lane 1 traffic is faster than HOV traffic.
The tabs at the top of Figure 9 name the available travel time calculations:
• Time series—travel time vs. departure time (for particular lanes)
• Comparison—plot of one day vs. average over several days
• Time of day—variability in travel time vs. time of day
• Departure time—travel time for a given departure time
• Prediction—predicts travel time using real-time data
Also available are aggregate measures relating to travel time.
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Figure 9 Travel time on different lanes of a one-segment route on SR-55N. Source: [1]
Task 6 Maintain and support Campus PeMS
Campus PeMS consists of four machines; 135 disks holding over 4TB of data; two
(backup) tape drives; and seven different databases. BTS provides Oracle database
support, operating system support, and support to Unix and Oracle users. BTS is
responsible for continued system monitoring and operations. BTS is also assisting the
installation of Caltrans PeMS.
Conclusion
A recent FHWA report [9] describes six Archived Data Management Systems: PeMS,
Washington State TRAC, Detroit MITS, Minnesota TMC, Maricopa County RADS, and
King County Metro. All these systems, except for King County Metro, use loop
detectors as their primary source of data. (The King County system, concerned with bus
transit, uses AVL data.) Only PeMS makes the data available in real time to anyone with
an Internet connection. The other systems distribute the raw data after a delay, ranging
from one day to one month. None, except for PeMS, imputes missing or incorrect data
values in real time. None comes remotely close to providing the range of applications that
PeMS offers. These applications convert raw data into information useful to its wide
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audience: decision makers, Caltrans engineers and planners, VARs, consultants,
academics, and the interested public and media. In this respect, PeMS is years ahead of
other systems. The Division of Traffic Operations and the research team that jointly
designed and developed PeMS showed remarkable foresight (or they have been lucky) in
the design choices they made along the way.
Acknowledgements
The research summarized here is the joint effort of the PeMS Development Group,
especially Alexander Skabardonis and Pravin Varaiya of U.C. Berkeley; Jaimyoung
Kwon of CSU East Bay; and Bill Morris and Karl Petty of BTS. We have benefited
greatly from the advice, interest and guidance of Fred Dial and John Wolf of Caltrans,
Division of Traffic Operations, and Tarek Hatata of System Metrics Group.
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 of or policy of the California Department of Transportation.
This report does not constitute a standard, specification or regulation.
References
[1] http://pems.eecs.berkeley.edu/
[2] Dowling Associates, Berkeley Transportation Systems and System Metrics Group.
Measuring Non-Recurrent Traffic Congestion: Final Report. Prepared for California
Department of Transportation, June 2002.
[3] J. Kwon 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.
[4] J. Kwon, M. Mauch and P. Varaiya, “The components of congestion: delay from
incidents, special events, lane closures, weather, ramp metering gain, and excess
demand,” Proceedings of 84th Transportation Research Board Annual Meeting,
Washington, D.C., January 2006.
[5] C. Chen, A. Skabardonis and P. Varaiya, “Systematic Identification of Freeway
Bottlenecks,” 83 rd Annual Meeting Transportation Research Board January 2004,
Washington, D.C. Also, Transportation Research Record, No. 1867, 46-52, 2004.
[6] System Metrics Group, Inc. Transportation Management System (TMS) Master Plan,
February 2004.
[7] J. Kwon, A. Skabardonis and P. Varaiya, “Estimation of Truck Traffic Volume from
Single Loop Detector Outputs Using Lane-to-lane Speed Correlation,” 82nd Annual
Meeting Transportation Research Board January 2003, Also, Transportation Research
Record, No. 1856, 106-117, 2003.
[8] J. Kwon, A. Skabardonis and P. Varaiya, “Acquisition and Utilization of Real-Time
District-Wide Truck Traffic Volume Data from Single Loop Detectors.” 83 rd Annual
Meeting Transportation Research Board January 2004, Washington, D.C.
[9] FHWA. Archived Data Management Systems: A cross-cutting study, Report No.
FHWA-JPO-05-044, EDL # 14128, December 2005.
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