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Generating Performance Measures

From Portland’s Archived Advanced Traffic

Management System Data

Robert L. Bertini Department of Civil and Environmental Engineering

Portland State University P.O. Box 751

Portland, OR 97207-0751 Phone: 503-725-4249 Fax: 503-725-5950

Email: [email protected]

Monica Leal Department of Civil and Environmental Engineering

Portland State University P.O. Box 751

Portland, OR 97207-0751 Phone: 503-725-4297 Fax: 503-725-5950


David J. Lovell Department of Civil and Environmental Engineering

University of Maryland 1179 Glenn Martin Hall College Park, MD 20742

Phone: 301-405-7995 Fax: 301-405-2585


March 2002

Bertini, Leal and Lovell 2

Generating Performance Measures From Portland’s Archived Advanced Traffic Management System Data Robert L. Bertini, Monica Leal and David J. Lovell ABSTRACT Certain performance measures were generated for a freeway corridor in Portland, Oregon

(eastbound US 26) using archived loop detector data. The US 26 Sunset Highway is a major

east-west corridor connecting downtown Portland to the burgeoning west side, including major

residential communities as well as Silicon Forest, containing the region’s high tech industry.

The study shows that with the use of real data, it is possible to determine the functionality of the

facility with respect to measures such as mobility, economic development, quality of life, the

environment, resource conservation and safety. Because surveillance systems are often already

in place for traffic management purposes in urban areas, archived data can easily be used to

develop performance measures in real time and to track them over time. This concept can be

applied to specific corridors of interest or to an entire metropolitan area. This kind of

information can help agencies have a better vision of the current performance of the

transportation network, its evolution over time, as well as aiding in setting clear goals and

objectives to improve the performance of the facility. These data may also be used to calibrate

and/or validate travel demand forecasting models.


INTRODUCTION

With the implementation of numerous Advanced Traffic Management Systems (ATMS) as part

of our nation’s Intelligent Transportation Systems (ITS), many jurisdictions are realizing that the

data collection and surveillance systems used to operate transportation systems on a day-to-day

basis can be used as rich sources of data that can be useful for many purposes. Once this vast

quantity of data is archived, processed and converted to useful information, it is possible to

generate performance measures to aid in the planning, design and operation of transportation

systems.

There is a nationwide movement toward development of performance measures for operations,

as evidenced by the National Dialogue on Operations led by the U.S. Department of

Transportation. The vision adopted by the National Dialogue is “managing and operating the

existing transportation system so that its performance meets or exceeds customer expectations.”

Another indicator of the movement toward the greater use of performance measures is the recent

publication of the National Cooperative Highway Research Program (NCHRP) Project 8-32(2),

Multimodal Transportation: Development of a Performance-Based Planning Process. (NCHRP

1999) Also, the newest generation of travel demand forecasting, such as Metroscope and

TRANSIMS (currently being developed in Portland), require vast amounts of performance data

for validation and calibration. Performance measures generated from archived ITS data can be

valuable inputs to these models.

As one example of the ongoing development of performance measures on a statewide basis,

Table 1 shows a menu of preliminary recommended performance measures (safety and mobility

categories) being considered for adoption by the Oregon Department of Transportation (ODOT).

By definition, performance measures generated on a statewide basis must be simple enough to


satisfy the “least common denominator” in terms of data availability and level of detail. Often

statewide measures can seem somewhat watered-down, since rural areas are usually not rich in

surveillance capabilities. Clearly, in urban areas with highly developed ATMS it is possible to

provide more detailed measurements on a corridor and/or regional level. With this recognition,

the objectives of this paper are to:

Review current literature documenting performance measures of interest

Experiment with real archived ATMS data to expand and/or focus statewide level

measures to a corridor level

Demonstrate what may be possible to implement on a system-wide basis for tracking

freeway performance.

Consider possible corridor level performance measures.

Next, we will describe the test bed chosen for this experiment, as well as the sensor data that

were used. In the following section, the NCHRP Project 8-32(2) guidelines are used to generate

various sample performance measures in categories of mobility, economic development, quality

of life, the environment, and resource conservation and safety. In addition, some data fusion

combining incident data, automatic vehicle location (AVL) data from incident response vehicles,

and loop detector data are presented. Finally, the manuscript ends with some observations and

conclusions.

DATA

ODOT, the City of Portland, Tri-Met (Portland’s transit agency), Metro (regional government),

and other regional jurisdictions have developed the TransPort (Transportation Portland)

program. This organization brings together the various agencies to develop, operate and evaluate


traffic management, incident response, and traveler information systems as part of the region’s

ATMS. The program’s goals are to reduce congestion, reduce travel times, and minimize and

prevent accidents on the transportation network in the Portland region (including Vancouver,

Washington).

Portland’s ATMS includes freeway ramp meters at nearly every on-ramp. For this experiment an

11-mile corridor along eastbound US 26 between Helvetia Road and Skyline Road was chosen as

shown in Figure 1. The eastbound freeway has two lanes between stations 1 and 9 and three

lanes at station 10. This freeway is the major east-west corridor connecting downtown Portland

to the burgeoning west side, including major residential communities as well as Silicon Forest,

containing the region’s high tech industry. The traffic surveillance system in this corridor,

installed as part of the implementation of the ramp metering system, consists of 10 mainline

inductive loop detector stations (with pairs of detectors located in each lane) and associated on-

ramp detectors at 10 eastbound on-ramps. The detector stations are labeled 1 through 10 (ramp

volume data at station 8 includes volumes from OR 217 plus Parkway Road, which merge

upstream of the ramp meter). The data recorded by these sensors include vehicle count,

occupancy (percent time that the detectors are occupied by a vehicle) and average speed as

measured by the detectors in each lane and on each on-ramp and are aggregated locally every 20

seconds. These 20-second data are transmitted to the traffic operations center (TOC) via the

regional fiber optics network. Typically ODOT archives their data at a 15-minute level; but for

this research, data were archived in the most raw form available (20 seconds). The time period

chosen for this experiment was the week of Monday, October 30 to Friday, November 3, 2000.

The presence of congested conditions in this corridor heightens the need for estimating the

quality of its performance. Using archived data from loop detectors, performance values can be


calculated not only to assess safety and mobility characteristics now, but also to have the ability

to track these indicators over time.

DATA ANALYSIS

The NCHRP 8-32(2) guidelines include a library of performance measures proposed for adoption

by transportation operating agencies, so that decisions can be based on actual performance

measurement. The measures considered are divided into categories of: mobility, economic

development, quality of life, environmental/resource conservation and safety. Calculations of

sample measures using Portland’s archived ATMS data are included in the following

subsections. It is clear that there are numerous ways to visually display performance data, but the

NCHRP guidelines do not focus on this important area of research. While some efforts are made

here to display performance data in unique ways, this paper is by no means comprehensive. For

further explorations of helpful means of displaying data, see several papers produced by the

Washington State Transportation Center (Nee et al. 2000, Ishimaru et al. 2001, Ishimaru and

Hallenbeck 1999).

Mobility

Mobility measures provide indications of how easy or difficult travel can be along a corridor or

in a region. Performance measures related to the mobility of passengers or freight are defined in

this section. Suggested mobility measures according to NCHRP 8-32(2) include congestion

measures, such as delay, volume-to-capacity (V/C) ratio, level of service (LOS), trip time,

amount of travel such as vehicle miles traveled (VMT) and vehicle hours traveled (VHT), mode


share, transfer time, and transit performance. The following performance measures were

developed as indicators of the quality of mobility of the corridor:

Average Daily Traffic (ADT)

Figure 2 shows the ADT measured during the weekdays of October 30 to November 3. The ADT

values were calculated by obtaining the average weekday 24-hour volume at each detector.

Also shown in the figure are the ADT data from the ODOT permanent count recorders (PCRs)

and tabulated statewide by the ODOT Transportation Systems Monitoring Unit (ODOT 1999).

The corridor’s bi-directional ADT reported by the PCRs is also shown in Figure 2. In order to

compare the ADTs, a directional distribution of 50/50 is shown (westbound ATMS data were not

provided). As shown in the figure, the ADT generated from the archived loop detector data is

very close to the PCR measures when a directional distribution of 50/50 is assumed, until station

8. At station 9 the directional distribution is reduced for the eastbound direction and at station 10

the directional distributional is close to 50/50 again. This is important because ODOT could

replace their PCR data with the ATMS data and save data collection costs.

Average Daily Traffic Per Freeway Lane

ADT per freeway lane was also calculated for weekdays during the study period (not shown).

This analysis confirmed that the left lane (lane 1) had higher flows than the other lanes except at

stations 1 and 7, where shoulder lane (lane 2) flows were higher. However, the difference

between volumes in the individual lanes at these stations was very small. At station 1 the

difference was nearly 1000 vehicles, and at station 7 the difference was about 350 vehicles.


Also, at station 8 the ramp volume was higher than the volume in lane 2, where there is a heavy

movement from OR 217.

Average Speed

Figure 3 displays the average speed for the corridor on one day (November 1), where the raw 20-

sec speed data are plotted using the right-hand axis. The speed reductions can be observed

during the a.m. and p.m. peak periods. Figure 3 also shows the speed plotted cumulatively (left-

hand axis), where the slope of this curve represents the speed (normalized by the 20-sec time

intervals). To magnify the curve’s features, it has been skewed (consider that the difference

between the raw cumulative curve and a line V=v0t´ has been plotted, where v0 is the skew slope

and t´ is the elapsed time from the beginning of the curve). The dashed lines represent a linear

approximation of the speed (estimated by eye). At 7:21:20 a.m. the speed dropped from 60 to 49

mph, followed by an increase to 58 mph. At 2:55:20 p.m. the speed dropped to 49 mph, but at

4:01:20 p.m. the speed dropped further to 36 mph. This speed continued until 7:36:20 p.m. when

the speed increased to 59 mph. It is clear that plotting the speed data cumulatively reveals

changes in freeway conditions in a remarkably clear manner.

Travel Time

Derived from speed, but of more relevance to the user (in practice and as modeled in travel

demand forecasting systems), the total corridor travel time was calculated by summing the

estimated travel time for each station. For estimation purposes, the influence area of each

detector was assumed to be the distance between the upstream and downstream midpoints

between each detector pair. The travel time was obtained by dividing the length of each


influence area by the speed measured at the respective detector. The (raw) total travel time was

plotted versus time for November 1 in Figure 4. Increases in travel time during the a.m. and p.m.

peak periods can be observed, as well as the free flow travel time of about 11.7 min. This value

was estimated using data during the off-peak periods (overnight) when vehicles travel

unimpeded. Figure 4 also shows the travel time plotted cumulatively (the slope of the curve

represents the travel time normalized by the 20-sec time interval), which allows an observer or

traffic manager to see clearly when travel time is changing by tracking the slope of the curve as it

deviates from the free flow travel time curve. An increase in travel time is observed at 7:21:20

a.m. (11.7 to 17 min.) following by a decrease at 8:16:20 a.m. (17 to 11.7 min.). The travel time

increased again at 2:55:20 p.m. (11.7 to 25.9 min.) until 7:56:20 p.m. (25.9 to 11.7 min.) when

the travel time decreased. The two periods when the travel time increased were identified as the

peak periods of the day being studied.

Vehicle Miles Traveled

The total VMT was estimated for the study corridor (on weekdays). We assumed that the

vehicles counted on the mainline traveled those entire segments and that the vehicles using the

ramps traveled over half of each segment. The length of each section represented by each

detector was multiplied by the count measured at each detector to obtain the VMT, which was

then summed to arrive at the total. Table 2 shows the results of this calculation.

Person Miles Traveled

The PMT was also calculated for the study corridor by multiplying the VMT by the average

vehicle occupancy, depending on the composition of the traffic. The traffic composition,


comprised of the percentages of each type of vehicle traveling through the corridor, was obtained

from ODOT and the following average vehicle occupancies were assumed: 1.42 for passenger

cars (source: ODOT), 1.0 for motorcycles, 25 for buses, and 1.1 for any kind of truck. The final

PMT for the whole corridor for the weekdays can be observed in Table 2.

Mobility Index

The measures presented thus far have been derived unambiguously from real archived data.

Some performance indices are composites, combining a variety of measures into a single value.

This is often done to reduce the complexity and volume of the performance measures and to

compare the performance of different facilities and among different modes. As one example, a

Mobility Index was generated by dividing PMT by VMT and multiplying by average speed. The

Mobility Index, a multimodal index, is shown in Table 2. The Mobility Index used in this paper

is proposed by NCHRP 8-32(2) (NCHRP 1999), and reflects a weighted speed where the

weighting coefficients are the vehicle occupancies. In regions such as Portland, multimodal

considerations are becoming increasingly important.

Vehicle Hours Traveled

The total VHT was calculated for the study corridor (on weekdays). The mainline volumes and

ramp volumes were multiplied by the travel times (using the process described above) to obtain

the total time spent in the system. The total VHT corresponds to the sum of the VHT at every

station. Table 2 shows the results of this calculation.


Person Hours Traveled

The PHT was calculated for the study corridor by multiplying the VHT by the average vehicle

occupancy as above. The final PHT for the corridor on weekdays can be observed in Table 2.

Vehicle Miles Traveled By Congestion Level

Occupancy is directly measurable, and can be used as an indicator of congestion level.

Occupancy is the percent of time that a loop detector is occupied by a vehicle. Table 2 shows the

standards of occupancy that are used to classify the level of congestion. They are uncongested,

near-capacity, and congested flow conditions (May 1990). Table 2 shows the VMT by level of

congestion of the corridor for weekdays.

Person Miles Traveled By Congestion Level

Use the same process as above, Table 3 shows the PMT by congestion level for the study

corridor.

Percent of VMT at a Particular Level Of Service

Since freedom to maneuver within traffic and proximity to other vehicles are issues of concern,

different levels of service are often used to reflect these measures. As an example, the percent of

VMT at a particular LOS (based on the V/C ratio) was calculated for November 1, as shown in

Table 3. The highest proportion of VMT was for LOS D (39%) where speed began to decline

and density began to increase. Of the VMT, 21% occurred at LOS E and F.


Percent of The Freeway Uncongested During Peak Hours

If we assume that the congested periods occur when the occupancy is higher than 28%, we can

calculate the percent of the highway that is uncongested during the peak hours. This analysis

(for November 1) showed that more congestion occurred between 5:30 to 6:30 p.m., where only

54% (6 miles) of the freeway corridor was uncongested.

Number And Percent Of Lane-Miles Congested

The total number of lane-miles that were considered corresponds to the length of the segment

multiplied by the number of lanes, for a total of 22.4 lane-miles. Figure 5 outlines the peak

periods with high degrees of congestion for November 1. The highest value (85%) was observed

at 6:48:40 p.m., corresponding to a total of 19 congested lane-miles in the corridor.

Lost Time Due To Congestion

Figure 6 shows the time lost due to congestion for November 1. The lost time is the difference

between actual travel time and free-flow travel time. Sometimes the lost time due to congestion

can be nearly 1 hour, which leads to high delay costs for passengers and freight.

Demand Vs. Capacity

Demand was considered in relation to capacity (assumed to be 2,000 veh/hr/lane) for November

1 as an example. Using data from stations 1 through 9 (the two-lane section), Figure 7 shows the

demand versus time where one can observe that the only station that exhibits demand that

exceeds the assumed capacity is station 7 during the a.m. peak; during most of the day the

demand remains close to the assumed capacity. An even more useful tool would be to construct


an electronic superimposition of many days’ worth of these plots and to develop a capacity

profile by looking at the upper envelope. An automated procedure to generate such a graphic

could help in the daily activities of the region’s traffic managers and planners. The V/C ratio is

also shown on the right-hand axis. As shown, using this day’s data, stations 1 to 4 are operating

at LOS B and C from 6:00 a.m. to 8:00 p.m. whereas stations 5 to 10 are between D and E. The

only station that presents LOS F is station 7 during the a.m. peak.

Percent of VMT Which Occurs On Facilities With Particular V/C Ratio

While this performance measure may be more meaningful for comparing one facility to another,

we can calculate the percent of VMT (by station) that is greater than V/C=0.68 as an example.

Table 3 shows the results calculated for November 1. The station with the largest percentage

was station 6, with 84%. In addition, it is worth noting that stations 5 to 9 all exhibit more than

70% of VMT at V/C more than 0.68.

Delay Per Vehicle Miles Traveled

The delay was calculated for the freeway lanes (not including the ramps) for November 1. In

order to obtain the delay the following operations were performed by segment of the corridor and

by time of day:

Pace (minutes/mile) = 60/Average Speed

Delay Rate (minutes/mile) = Pace − (60/Free Flow Speed)

Total Delay (veh-hr) = Delay Rate × VMT/60


Having the delay and the total VMT for each time period, the delay per VMT is the total delay

divided by the VMT. Table 3 shows the results of these calculations for November 1. The

highest delay per VMT is observed at station 4, with a value of 1.05 minutes/mile. The total

delay per VMT for the entire corridor is 4 minutes/mile.

Reserve Capacity

The reserve capacity is the total VMT “capacity” minus the VMT for the entire day. As an

example, the reserve capacity was calculated for November 1 for every station (not shown).

Between 6:00 a.m. to 8:00 p.m. the difference between the VMT capacity and the actual capacity

was very low particularly for stations 7, 9, and 10. At these stations the corridor was operating

very close to capacity, especially during the a.m. peak.

Economic Development, Quality Of Life, Environmental And Resource Conservation

In this paper some performance measures are calculated in order to gain an understanding of the

economic impacts of congestion on eastbound US 26. Using an automated procedure, traffic

managers could receive a running tab of costs, using some of the assumptions described below.

Cost of Delay

Figure 4 shows the cumulative travel time curve for November 1. Delay is the difference

between actual travel time and free flow travel time, so the total vehicular delay is just the area

between the two curves in the figure. The cost of delay can be estimated by multiplying an

average value of time ($17.87 per person-hour in Oregon, according to ODOT) by the total delay

in person-hours. The total delay for November 1 was estimated to be about 2,950 veh-hr, or


4,390 person-hours. Thus the cost of lost time for this corridor was about $78,000 for one day.

Assuming that a year has 250 working days and that this delay is close to the average, the annual

cost of lost time would be over $19 million. The impacts on the movement of freight could also

be considered in a similar fashion.

Fuel Cost

Fuel costs were approximated using the following simple General Motors model (Daganzo and

Newell 1995):

E = k5 * L + k6 * T (1)

E = additional fuel consumed per vehicle

k5 = 90 ml/km = 0.038 gallons per mile

L = distance traveled in queue

k6 = 0.44 ml/sec = 0.418 gallons per hour

T = travel time in queue

This model estimates the additional fuel consumed by vehicles moving slowly in traffic (the

average speed for the a.m. and p.m. peak was 39 mph), such as in a queue. For this estimate, it

was assumed that the cost of fuel (in November 2000) was $1.65 per gallon. Therefore,

Equation 1 can be simplified to:

E = $3.15 per hour per vehicle (2)

Thus, the total additional fuel cost due to delay for the corridor on November 1 was 3.15 times

the total veh-hrs of delay, or $9,300. On an annual basis this might equate to about $2.3 million.

Thus the total cost due to congestion on eastbound US 26 might be about $21.3 million.


Safety

Every agency wants to maintain a high degree of mobility on the facilities it is responsible for,

while keeping them safe as well. Accidents, breakdowns, and other kinds of incidents have an

adverse economic impact on society. Portland’s ATMS includes an incident management

program with a computer-aided dispatch system (CAD), which archives all incident data. We

tested the fusion of incident data with loop detector data to determine whether there are benefits

to be gained from combining data from multiple sources.

As an example of the impact of non-recurring congestion on US 26, a major accident identified

through the CAD system was analyzed. In order to understand the impact of the accident on

congestion, two speed contour maps were generated displaying the data for the same location as

where the accident occurred, but on different days. The accident occurred on October 31 on

eastbound US 26, between stations 9 and 10 (Figure 1). The confirmed time of the accident was

6:18 p.m., and the estimated end time of the accident was 7:06 p.m.

In order to identify the temporal and spatial extents of congestion, speed contour plots were used

(occupancy contour plots can also be used). Figure 8 shows speed contour plots for the US 26

corridor on October 31 and November 1. The x-axis represents the time of the day (24 hours),

the y-axis represents the stations, and the color variation represents speed.

Figure 8 shows that the speed drops at the accident location on October 31, and displays the

speeds at the same location on November 1. The speed was reduced to a range of 0 to 10 mph


due to the accident, when on a different (normal) day the speed at the same location and at the

same hour was between 30 and 50 mph.

Fusion Of Incident Response AVL Data With Loop Data

The Portland ATMS includes an incident response program (COMET) that includes motorist

assistance trucks equipped with AVL equipment. In this section we explore the fusion of AVL

data with loop detector data. Figure 9 shows the trajectory (in the time-space plane, where the

slope of the trajectory at a point is the speed of the vehicle at that point) of one COMET vehicle

and a hypothetical trajectory of a vehicle according to the loop detector speed data. The dashed

line represents a linear approximation of the vehicle’s speed, estimated by eye. This COMET

trip indicates that the truck’s average speed was about 52 mph between stations 1 and 9 (the loop

detector speeds would predict an average speed of about 59 mph). Next, the truck stopped for a

few seconds, starting again with an average speed of 14.61 mph, finally reaching a speed of 64

mph, and finished the trip through the study corridor. The travel time for the COMET vehicle

along US 26 was 16 minutes, while for the other vehicles it was about 11 minutes. The fusion of

the loop data with the AVL data should be explored in the future, but as is clear here, the

COMET vehicles are traveling more slowly, which is consistent with their mission to observe the

speed limit and be on the lookout for incidents.

CONCLUSIONS

Based on a review of recent literature, some valuable performance measures were generated for

one freeway corridor in the Portland metropolitan area. This experiment has shown that using

archived loop detector data, it is indeed possible to obtain information assessing the functionality


of the facility. In accordance with the data available, a limited number of sample performance

measures were chosen in this paper. The information can assist in determining the best

performance measures for obtaining simple, quick and accurate information describing the

functionality of the corridor, enhancing decision-making in both real time and over longer

planning horizons. With simple, directly measurable variables such as vehicle count, speed,

occupancy and incident information, it is possible to set some standards and determine some

performance measures that can be generated for the transportation systems around Portland and

in that way keep track of the general performance of the network.

Additional information could help generate additional performance measures, such as aerial

photography, video surveillance, license plate matching, floating car/probe vehicle information,

and cellular phone location data, to name a few. The US 26 corridor is a multimodal one with a

parallel light rail transit line and extensive bus service. Performance measures relating to other

modes should be considered as well. Further, spatial data such as density, income and

population can be combined with transportation data to obtain some interesting performance

measures. Some examples include the percent of population that can access services with a given

travel time and a given speed and the average travel time for different employment centers.

ODOT has made solid decisions to implement an ATMS system and has embarked upon a

statewide process to design a performance measurement and evaluation system. Now, it is

important to consider using the ATMS system to facilitate the development of performance

measures for the Portland freeway system, and more broadly, for the entire transportation

network. As a starting point, it would be useful to automate the generation of many of the


performance measures and graphs presented in this paper. This automation could serve several

useful purposes. For example, without interfering with current management systems, we envision

a stand-alone computer terminal that will display all of these graphs and performance measures

as it extracts the data directly off the data feed. By putting this information in the hands of

ODOT traffic managers, incident responders, planners, etc., for their daily activities, it will be

possible to observe which ones get used for which purposes. This system could collect

suggestions for improvement and ultimately be integrated into ODOT’s standard operating

procedures and computer systems. This somewhat modest approach allows for the possibility

that researchers cannot predict a priori which elements of a largely human intelligence-based

decision mechanism can best be supported by automated data.

As an integral part of a performance measurement generator, appropriate trends can be tracked

over weeks, months, and years and graphical presentations and summary tables can be

constructed automatically. Examples include averages and outer envelopes of characteristics

such as the physical extent of congestion from the occupancy plots like Figure 8, and a capacity

profile from plots like Figure 7. These data can be incorporated into the new data-intensive

activity-based planning models (such as Metroscope and TRANSIMS), avoiding costly

independent data collection efforts. The traffic surveillance system allows for the monitoring of

queue location, which is valuable information for use in calibration and validation of simulation-

based planning and operational models.

With regard to capacity, it would be necessary to augment the graphical presentation of an outer

capacity envelope with some other intelligence to indicate the degree to which we are confident


that we have observed capacity conditions. It is possible that the outer flow envelope could just

represent the highest flows seen to date, but with more capacity in reserve. As an example, our

confidence improves where we have seen combinations of high flows and high densities.

The value of such an ongoing generator of performance data is that it eliminates the need to

make assumptions/estimates about time-varying behavior that find their way into aggregate

performance metrics. With our proposed system, the data are being archived every day, so these

averages can be calculated, rather than estimated. Finally, in cooperation with ODOT, it would

be possible to make some of the performance information available via the Internet. Using an

automated procedure, we could observe hit rates usage statistics. Finally, for research purposes,

an automated means of soliciting customer suggestions would be included.

ACKNOWLEDEMENTS

Dennis Mitchell and Jack Marchant of the Oregon Department of Transportation Region 1 for

generously provided the data used herein. Andrew Tang of Cambridge Systematics provided

access to the NCHRP materials and the Departments of Civil and Environmental Engineering at

Portland State University and the University of Maryland provided funding for this work.


REFERENCES

Daganzo, C.F. and Newell, G.F. 1995. Methods of Analysis for Transportation Operations.

Berkeley, CA: University of California, Institute of Transportation Studies. Garrison, W.L. and J.D. Ward. 2000. Tomorrow’s Transportation: Changed Cities, Economies,

and Lives. Boston, MA: Artech House. Ishimaru, J. M., and M. E. Hallenbeck. 1999. FLOW Evaluation Design Technical Report.

Seattle, WA: Washington State Transportation Center. Ishimaru, J., M. E. Hallenbeck, and J. Nee. 2001. Central Puget Sound Freeway Network Usage

and Performance. 1999 Update. Seattle, WA: Washington State Transportation Center. May, A.D. 1990. Traffic Flow Fundamentals. New Jersey: Prentice Hall. National Cooperative Highway Research Program (NCHRP). 1999. Multimodal Transportation:

Development of a Performance-Based Planning Process, Project 8-32(2). Washington, DC: National Academy Press.

Nee, J., J. Ishimaru, and M. E. Hallenbeck. 2001. HOV Lane Performance Monitoring: 2000

Report. Seattle, WA: Washington State Transportation Center. Oregon Department of Transportation (ODOT). 1999. Oregon State Highway Transportation

Volume Tables. Salem, OR: ODOT Transportation Systems Monitoring Unit.


LIST OF TABLES

Table 1 ODOT Statewide Performance Measures

Table 2 Sample Corridor Performance Measures

Table 3 Sample Corridor Performance Measures

LIST OF FIGURES

Figure 1 Site Map

Figure 2 Average Daily Traffic (ADT)

Figure 3 Average Speed

Figure 4 Travel Time

Figure 5 Percent Of Lane-Miles Congested

Figure 6 Lost Travel Time Due To Congestion

Figure 7 Demand Vs. Capacity

Figure 8 on-Recurring Congestion Example

Figure 9 Fusion Of AVL Data With Loop Detector Data


TABLE 1 Potential Oregon Safety and Mobility Performance Measures Safety 1. Maximize the safety of system operating conditions at all times

a. Number of incidents on system per yearly VMT b. Customer perception of safety on transportation system

2. Minimize transportation conflict points for all modes a. Number of incidents/injuries near conflict points per number of conflict points b. Number of correctable crash sites funded for improvement

3. Improve the clarity and design of operations delivery to system customers. a. Annual survey questionnaire response b. Percent of system route miles of with basic/advanced/predictive travel time information available. c. Number of ATIS calls and website visits

4. Improve incident detection verification and response. a. Average time between notification and response/arrival and clearance b. Total duration of incidents

Mobility 1. Maximize system throughput

a. PMT per system lane mile per station population b. VMT per passenger vehicle c. Urban/rural roadway miles at V/C greater the 0.70 by functional class d. Customer perception of quality of service, timeliness, and effectiveness e. Number or percent of signals verified or re-timed

2. Improve travel time reliability a. Hours of delay for non-recurring congestion b. Delay per accident

3. Minimize hours of delay experienced by customers a. Hours of stopped time per system user b. Hours of delay per system user

4. Enhance inter-modal connections a. Number (or percent) of inter-modal connectors improved by operational strategies b. Accident rates at major inter-modal connections c. Time to access inter-modal facilities d. Customer perception of connection convenience

5. Maximize modal choice a. Percent of state highways with bicycle lanes b. Percent of urban state highways with sidewalks c. Percent of total person miles of travel that are made in HOV lanes (on sections with HOV lanes) d. Percent of state residence with access to TDM program


TABLE 2 SAMPLE PERFORMANCE MEASURES EASTBOUND US 26 WEEKDAYS 10/30/00-11/03/00

VMT by Congestion Level PMT by Congestion Level

VMT PMT Mobility VHT PHT Percent Occupancy %

Percent Occupancy %

Sta. Miles veh-mi person-mi Index veh-hr person-hr 0-5 5-8 8-12 12-17 17-28 28-42 >42 0-17 17-28 >28

Uncongested Near-

capacity Congested

1 1.22 105,800 157,000 95 15,259,700 22,635,600 80,400 11,900 7,300 800 0 0 0 148,900 0 02

1.63 192,900 286,000 92 19,218,100 28,507,400 134,500 28,400 19,000 3,300 100 0 0 274,700 170 03 1.07 177,900 263,900 85 26,149,200 38,788,700 98,100 29,100 27,600 8,200 3,400 4,000 1,400 241,800 5,000 8,0004 0.7 100,000 148,300 89 19,466,200 28,875,400 58,600 17,600 15,400 3,700 1,100 1,400 800 141,300 1,600 3,3005 1.4 290,300 429,600 78 31,513,400 46,628,200 110,200 46,200 60,700 29,200 23,500 10,500 1,600 364,200 34,700 18,0006 1.33 325,700 481,900 78 34,340,000 50,810,500 99,800 54,000 74,100 28,700 29,900 30,900 2,200 379,700 44,200 49,0007 0.96 273,500 404,700 61 38,697,800 57,258,400 48,800 23,200 66,000 88,700 38,600 3,200 900 335,500 57,200 6,1008 1.18 242,500 358,800 79 35,041,700 51,907,000 88,000 35,800 47,500 26,700 16,100 13,700 3,300 293,400 24,000 25,1009 1.03 253,000 384,400 78 29,474,900 44,843,400 37,000 17,600 42,700 84,500 60,500 8,800 1,800 276,500 92,000 16,200

10 0.47 127,200 191,900 81 29,140,800 43,948,700 93,300 23,700 7,600 500 9 0 0 188,800 13 0 TOTAL 2,088,900 3,106,600 278,301,800 414,203,300 848,600 287,500 368,000 274,100 173,200 72,600 12,000 2,644,800 258,900 125,700


TABLE 3 SAMPLE PERFORMANCE MEASURES EASTBOUND US 26 NOVEMBER 1, 2000

Percent of VMT at LOS "X" >0.68 Total Delay Delay per VMT Sta. VMT A B C D E F % veh-hr min/mi

1 25,300 15 58 26 0 0 0 0 8 0.022

44,200 9 35 56 0 0 0 0 2 0.003 39,700 8 7 64 22 0 0 22 502 0.764 20,100 14 43 44 0 0 0 0 247 0.745 63,400 6 6 9 65 14 0 79 745 0.706 68,900 6 6 4 64 20 0 84 580 0.517 57,400 6 2 10 5 65 13 83 184 0.198 56,900 5 8 9 58 20 0 78 557 0.599 59,700 5 8 4 57 25 0 83 209 0.2110 32,200 6 9 15 55 15 0 70 135 0.25

TOTAL 467,800 32,800 63,000 91,300 182,300 91,000 7,500 3,170 4PERCENT 7% 13% 20% 39% 19% 2%


FIGURE 1 Site Map

STATION 1 - HELVETIA EBSTATION 2 - CORNELIUS Ps Rd EBSTATION 3 - 185 th Ave NB to EBSTATION 4 - 185 th Ave SB to EBSTATION 5 - CORNELL Rd EBSTATION 6 - MURRAY Rd EBSTATION 7 - CEDAR HILLS Blvd EBSTATION 8 - ORE 217 NB to EB - PARKWAY EBSTATION 9 - CANYON Rd EBSTATION 10 - SKYLINE Rd EB

N

LOOP DETECTOR


FIGURE 2 Average Daily Traffic (ADT)

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

60 62 64 66 68 70 72 74MILE POST

ADT

(vpd

)

Data Eastbound ODOT two directions Directional Distribution 50/50-ADT 1999

*

MILEPOST

Station 1 - 61.25 Station 2 - 62.47 Station 3 - 64.50 Station 4 - 64.60 Station 5 - 65.90 Station 6 - 67.40 Station 7 - 68.55 Station 8 - 69.31 Station 9 - 70.90 Station 10 - 71.37


FIGURE 3 Average Speed

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

12:00 AM 3:00 AM 6:00 AM 9:00 AM 12:00 PM 3:00 PM 6:00 PM 9:00 PM 12:00 AMTime

V(x,

t)- v

0 t'

, vo=

9500

mph

per

hou

r

20

30

40

50

60

70

80

90

100

110

120

Spee

d (m

ph)

60 mph

49 mph

58 mph

49 mph

36 mph

59 mph

7:21:20 am

8:25:00 am

2:55:20 pm

4:01:20 pm

7:36:20

Free flow speed

Best linear approximation on the curve where the slope is the speed

Re-scaled cumulative Speed Average Speed


FIGURE 4 Travel Time

-10,000

0

10,000

20,000

30,000

40,000

50,000

60,000

12:00 AM 3:00 AM 6:00 AM 9:00 AM 12:00 PM 3:00 PM 6:00 PM 9:00 PMTime

Cum

ulat

ive

Trav

el T

ime

10

20

30

40

50

60

70

Trav

el T

ime

(min

utes

)

7:21:20 AM

8:16:20 AM

2:55:20 PM

7:36:20 Morning peak

Afternoon peak

Free flow travel time

17 min

25.92 min

11.69 min

11.68 min

11.67 min

Free-flow Travel Time

Free-flow Travel Time

Travel Time


FIGURE 5 Percent Of Mile Lanes Congested

0

10

20

30

40

50

60

70

80

90

100

12:00:00 AM 3:00:00 AM 6:00:00 AM 9:00:00 AM 12:00:00 PM 3:00:00 PM 6:00:00 PM 9:00:00 PM 12:00:00 AM

TIME


FIGURE 6 Lost Travel Time Due To Congestion

0

10

20

30

40

50

60

12:00:00 AM 3:00:00 AM 6:00:00 AM 9:00:00 AM 12:00:00 PM 3:00:00 PM 6:00:00 PM 9:00:00 PM 12:00:00 AM

Time


FIGURE 7 Demand Versus Capacity

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

4,500

TIME (hours)

Station 1 Station 2 Station 3 Station 4 Station 5 Station 6 Station 7 Station 8 Station 9

0.40

0.60

0.80

1.00

0.20

V/C


FIGURE 8 Non-Recurring Congestion Example

High congestion Low Congestion

Low congestion


FIGURE 9 Fusion Of AVL Data With Loop Detector Data

60

61

62

63

64

65

66

67

68

69

70

71

72

73

10:55:12AM

10:57:12AM

10:59:12AM

11:01:12AM

11:03:12AM

11:05:12AM

11:07:12AM

11:09:12AM

11:11:12AMTIME

MIL

EPO

ST

STATION 1

STATION 2

STATION 3

STATION 4

STATION 5

STATION 6

STATION 7

STATION 8

STATION 9

STATION 10

Total travel time - Comet = 16.20 minutesTotal travel time -Other vehicles = 11.29 minutes

52.43 mi/hr

14.61mi/hr143

stop

63.62 mi/hr

58.65 mi/hr

57.01mi/hr

Slope of the curve which represents Average Speed

Other Vehicles Comet

generating performance measures from portland’s archived ...€¦ · average daily traffic per...

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