Chapter 8 – Congestion Management System
The mobility and access provided by the Richmond region’s transportation system is a critical component in the quality of life for local residents and visitors. Whether traveling to work, shopping, school, or for social purposes, we all value the ability to reach our destinations in a way that meets our expectations. However, like so many other metropolitan areas across the country, increasing traffic congestion along the region’s highways regularly threatens our ability to get to destinations in a timely manner.
While the causes of congestion are varied and difficult to fully understand, we all recognize it when we are sitting in traffic. To put a “face” on congestion, below are some facts generated by the Texas Transportation Institute’s 2007 Urban Mobility Study.
The good news is that there are congestion management solutions – more roadways and transit, ramp metering, High Occupancy Vehicle (HOV) lanes, incentives to make trips at different times, and better incident management (for example, clearing crashes and vehicle breakdowns more quickly) – that can make a difference in addressing the effects of congestion.
The dominant mode of transportation in the Richmond area is the highway system. Many transportation modes utilize the region’s roadways. For example, roads provide transportation access for buses, carpools, bicycle and pedestrian travel, and freight movement.
Although there is a need to reduce vehicle emissions to improve air quality, roadways remain as the primary component in the Plan’s recommendations. Included in the LRTP are a variety of improvements planned for the roadways in the Richmond region. Some of these improvements are meant to reduce vehicle-miles of travel and improve traffic operations, which would then improve air quality and reduce energy consumption.
This chapter of the LRTP highlights the results of the Richmond Area Metropolitan Planning Organization’s (RAMPO’s) efforts to understand and manage traffic congestion through the fourth update to the region’s Congestion Management System, or CMS. A separate CMS technical document contains detailed information about the analyses presented throughout Chapter 8.
- The delay per traveler has climbed across the country from 14 hours in 1982 to 38 hours in 2005.
- In 2005, congestion resulted in 4.2 billion hours of delay and wasted 2.9 billion gallons of fuel – costing urban areas $78.2 billion.
2007 Urban Mobility Study
Background
In an attempt to address the problems caused by congestion, the federal government, through the passage of the 1998 Transportation Equity Act for the 21st Century (TEA-21) and its predecessors, the 1991 Intermodal Surface Transportation Efficiency Act (ISTEA) and the 2005 Safe, Accountable, Flexible, Efficient Transportation Equity Act: A Legacy for Users (SAFETEA-LU) have required that a Congestion Management System (CMS) be in place in all Transportation Management Areas, which are metropolitan areas with populations over 200,000. Based on the 2000 Census, the Richmond Urbanized Area population – which includes the City of Petersburg – is 818,836. The urbanized area within the Richmond Area MPO study area is estimated at 707,270.
Federal regulations state the following required elements of a CMS:
1. Methods to monitor and evaluate the multimodal transportation system, identify the causes of congestion, identify and evaluate alternative actions, and evaluate the efficiency and effectiveness of implemented actions;
2. Definition of parameters for measuring the extent of congestion and for supporting the evaluation of the effectiveness of congestion reduction and mobility enhancing strategies;
3. Establishment of a program for data collection and system performance monitoring;
4. Identification and evaluation of the anticipated benefits of both traditional and nontraditional congestion management strategies;
5. Identification of an implementation schedule, implementation responsibilities, and possible funding sources for each strategy; and
6. Implementation of a process for periodic assessment of the efficiency and effectiveness of implemented strategies, in terms of the area’s established performance measures.
To comply with these regulations, the Richmond Area MPO established a CMS that includes major highways and roads, as well as some minor roadways. The goal of the CMS is to reduce congestion and improve traffic safety through the use of technology, improving roads, and increasing vehicle occupancy. The CMS is an ongoing effort to identify, develop, and implement the most effective transportation strategies to enhance travel in the Richmond region.
This report is the fourth in a series of Congestion Management System (CMS) studies for the Richmond Area MPO. The goal of the MPO’s CMS is to provide an ongoing process that allows for a periodic assessment of the region’s transportation network, in addition to identifying where congestion regularly exists along area roadways. The process also identifies appropriate mitigation strategies that focus on improving transportation efficiency,
The Richmond Area MPO has been developing and refining a CMS for the region since the mid 1990’s. The first report, published in 1997, and the three subsequent reports published in 1999, 2001, 2003 all have included a comprehensive transportation system analysis, identification of performance measures, and an inventory of existing congestion management strategies.
CMS Process
The CMS is a vital part of the MPO transportation planning process that results in serious consideration of strategies resulting in the most efficient and effective use of existing and future transportation facilities. Congestion management solutions can come in a variety of forms, such as: building park and ride lots, improving bicycle/pedestrian facilities, carpooling, adding lanes to existing roadways, increasing transit service, and coordinating traffic signals.
For a detailed listing of the various strategies available, refer to the CMS Toolbox of Strategies found in the CMS Technical Document.
Based on currently available data sources, the CMS is organized to provide an assessment of roadways in the Richmond area. As it is included in the 2031 LRTP, the CMS update is an attempt at a system-wide approach to identify, analyze, and address congestion in our region.
The CMS process can be described in terms of four activities that seek to define, identify, mitigate, and monitor both recurring and non-recurring congestion. The four work tasks are summarized below.
System Definition and Data Collection
Identification of the transportation mode(s) and network(s) for incorporation in the CMS study network. Development of the program for data collection that will include evaluating how often data should be collected, on which modes it should be collected, and defining the CMS network (existing and future congested corridors for all transportation modes being studied in the CMS).
Congestion Definition and Identification
Develop indicators of congestion that can be quantified through the use of performance measures (for example, travel time and speed for roadway segments) and apply the congestion indicators to the regional network determined in Step 1. The result will be the identification of locations where recurring and non-recurring congestion exist along the CMS network. The resulting list of congested corridors is included in the CMS component of the 2031 LRTP.
Strategy Evaluation
Compile a “toolbox” of congestion mitigation strategies and a methodology for applying strategies to the congested corridors, bottlenecks, and other areas identified in Step 2.
System Monitoring and Evaluation of Recommendations
Outline data collection and monitoring efforts to identify trends in the overall transportation system and, over time, attempt to gauge the effectiveness of strategies that are implemented along congested corridors. This step results in the development of two products:
1. The development of a state-of-the-transportation system report and;
2. Individual reports, where applicable, that provide analyses of traffic flow both before and after strategies have been implemented along the identified CMS corridors.
Relationship to the Transportation Planning Process
Starting with the 2026 LRTP, all CMS work tasks have proceeded in conjunction with LRTP work tasks. The two documents, which previously have been developed and printed as separate reports, are physically merged together in the LRTP document. The updated CMS consists of two parts.
The component contained in the LRTP provides:
• A state of the transportation system analysis for the region. This includes a historical analysis of several factors related to transportation system performance and a comparison of Richmond data to both national trends and trends identified for similar regions throughout the U.S.
• An identification of congested corridors found throughout the region.
• Recommendations
As derived from both the LRTP and the CMS study group, the CMS outputs described above then flow into the region’s transportation planning and project development processes. For example, the menu of appropriate strategies developed for a corridor can be utilized during the project development process; specifically, the Development and Evaluation of Alternatives step in the National Environmental Policy Act (NEPA) process or the State Environmental Review Process (SERP).
Additionally, information developed by the CMS study group can be used by state and local agencies undertaking detailed corridor studies. In both cases, the CMS study group’s recommendations can help streamline the project development process by filtering out strategies for a corridor that are not viable and selecting strategies that will warrant more detailed analysis during the NEPA and SERP processes.
Projects resulting from state and local corridor studies and the evaluation of alternatives undertaken during the NEPA or SERP process are eventually considered as recommendations in the LRTP. The LRTP establishes the transportation policy framework for the region and
CMS Roadway Network
The CMS study network of roadways covers major roadways included within the CMS study network. This network consists of approximately 4,0701 lane-miles of existing roadways. All interstates, expressways and principle arterials within the Richmond MPO study area are included in the network. Map 8-1 through Map 8-5 shows the locations of the roadways included in the CMS/LRTP study area.
The five maps break the MPO study area into the following geographic sections:
Map 8-1: Northwestern MPO Area Map 8-2: Northeastern MPO Area Map 8-3: Southeastern MPO Area Map 8-4: Southwestern MPO Area Map 8-5: Central Richmond Area
State of the Transportation System
A comprehensive assessment of factors related to transportation network performance is an essential component of a complete congestion management system. In combination with the other CMS components, it helps to provide decision makers with a better understanding of the many influences that affect the performance of the transportation network. Results can help to prioritize competing strategies to maintain an efficient and safe transportation system.
The state of the transportation system analysis for the region includes, for each transportation factor, an analysis over time for the measure and a comparison, where applicable, of Richmond data to both national trends and trends identified for similar regions throughout the U.S. This section of Chapter 8 is divided into three main categories:
1. Historic Transportation Trends 2. Public Transportation Trends
3. Comparing the Richmond Area to Other Urbanized Areas
1 Only includes roads functionally classified as interstates, expressways and principle arterial
Historic Transportation Trends
The first part of this section looks at historical trends for the following four factors that relate to the performance of the highway network in the Richmond area:
• Population
• Vehicle Registrations
• Vehicle-Miles of Travel (VMT)
• Vehicle Occupancy
For each of these factors, their change over time was analyzed and Richmond area trends were compared to Virginia and national trends. Time periods and geographic extent used in the study were directly related to the availability of data. For population and vehicle registrations, data was collected and analyzed for all of the nine localities found within the Richmond Regional Planning District Commission (RRPDC).
The historic trend analysis for both population and vehicle registrations covered the thirteen year period from 1992 to 2005. For vehicle-miles of travel, we looked at data for the Richmond urbanized area from 1994-2005.
Trends in Population and Vehicle Registrations
In the Richmond region, growth in vehicle registrations far outpaces the region’s population growth. Figure 8-1 indicates that, from 2000 to 2006, the region’s average annual growth rate for vehicle registrations (2.9%) is more than double that of the area’s average annual growth in population (1.4%). Richmond area’s vehicle registration is increasing at a faster rate than that of the state and nation.
In 2006, there were 925,988 registered vehicles in the Richmond region, or .98 vehicles for every area resident. By comparison, Virginia had .87 vehicles per person and nationally there were .82 vehicles per person.
2.9% 1.6% 1.6% 1.4% 1.3% 0.6%
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
3.0%
Average Annual Growth Rate
Vehicle Registration Population Figure 8-1
Average Annual Growth Rate in Vehicle Registrations and Population: 2000-2006
RRPDC Virginia United States
Sources: Virginia Dept. of Motor Vehicles, Weldon Cooper Center for Public Service, U.S. Bureau of the Census, FHWA Highway Statistics Reports (2000-2006)
Trends in Vehicle-Miles of Travel
Referred to as VMT, vehicle-miles of travel is a measure commonly used to gauge the daily demand residents and visitors place on a region’s transportation network. VMT is the sum of the number of miles every vehicle travels on an average weekday in the Richmond region. Average annual daily traffic counts (AADT) and centerline road length are used to calculate VMT.
Figure 8-2 shows that VMT in the Richmond region has grown by an average of 9.4 percent annually from 2000 to 2005 and this is outpacing state and national VMT growth trends for the same time period. For the Richmond urbanized area, there was approximately 26.2 million vehicle-miles of travel per day in 2005. To put that number into perspective, every day, the total vehicle-miles traveled in the Richmond area is equivalent to driving a vehicle completely across Virginia – from the eastern shore to Cumberland Gap – approximately 44,407 times.
These VMT figures coincide with the increases in population and vehicle registrations discussed earlier. Over time, more people continue to move to the Richmond region in addition to the rapid rise in people registering to use vehicles along the region’s roadways – this all adds up to increased levels of vehicle-miles traveled.
9.4% 0.2% 2.8%
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2.0%
4.0%
6.0%
8.0%
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Average Annual Growth Rate
Richmond Urbanized Area
United States Virginia Figure 8-2
Average Annual Growth Rates in Vehicle-Miles of Travel: 2000-2005
Using available data from FHWA’s 2005 Highway Statistics Report, the Richmond urbanized area’s daily VMT per capita in 2005 was compared to other urbanized areas in the south and southeast. Of the cities shown in Figure 8-3, Charlotte came in with the highest amount at 34 vehicle-miles of travel per person and Washington, D.C. and Hampton Roads had the lowest figure of 23 VMT per person. Richmond’s 29 vehicle- miles of travel per capita fits roughly in the middle of the field and this figure outpaces both Hampton Roads and Washington, D.C.
Source: FHWA Highway Statistics Reports (2000-2005)
Charlotte Knoxville Raleigh Jacksonville Nashville Orlando Atlanta Greensboro Richmond - 29 Greenville, SC Tampa-St.Pete-Clearwater Louisville Memphis Baltimore Norfolk-VA Beach-Newport News Washington, D.C.
0 5 10 15 20 25 30 35 40
Figure 8-3
Daily VMT per Capita in Southeastern Urban Areas: 2005
Vehicle Occupancy Trends
An assessment of vehicle occupancy rates (VOR) helps to paint a picture of how efficiently a region’s roadway network moves people. Vehicle occupancy rates are calculated by counting both passenger vehicles and the number of people at a given location along a roadway. The number of people divided by the number of passenger vehicles is the average vehicle occupancy for that location.
Vehicle occupancy data reflects on the roadway network’s efficiency since it measures the number of people moving along a highway in comparison to the number of vehicles. A decrease in VOR means that more vehicles are needed to move the same number of people along a roadway. Adding more vehicles to a roadway can stress the road’s ability to efficiency move vehicles and people, resulting in traffic congestion. On the other hand, increasing the VOR can result in increasing person movement without necessarily adding more cars to the roadway.
Vehicle occupancy data has been collected on various roadway segments throughout the Richmond region in the years 1995, 2000, 2002 and, most recently, in 2006/2007. For the 2006/2007 survey, vehicle occupancy information was collected by the Virginia Department of Transportation (VDOT) at 27 locations throughout the Richmond metropolitan area from October 2007 to March 2008. The 2006/2007 Vehicle Occupancy Study found in the CMS Technical Report identifies the locations of the 27 count stations.
The methodology for the 2007/2008 survey differs slightly in comparison to the previous surveys. This survey measures vehicle counts only for the peak direction of travel for both AM and PM peak period whereas the previous surveys measured bi-directional vehicle counts. As a result, the total vehicle count for 2007/2008 is lower than that of 2002.
Figure 8-4 shows the resulting 2007/2008 VOR for the AM, PM, and combined AM/PM periods. The results follow typical trends in vehicle occupancy data. Specifically, the PM VOR of 1.14 is higher than the AM VOR of 1.13. The PM time period has a slightly higher VOR, likely due to a greater percentage of discretionary trips – such as shopping – where drive alone trips are less prominent than during the typical peak morning commute period.
Figure 8-4
2007/2008 Vehicle Occupancy Rates
Time Period Total Autos Total Passengers 2007/2008 VOR
AM 197,568 223,539 1.13 PM 159,023 181,830 1.14 Combined AM/PM 356,591 405,369 1.14
Figure 8-5 depicts historical peak period vehicle occupancy rates derived from four VOR surveys conducted by VDOT for the Richmond MPO. An initial VOR survey was conducted in 1995, the next surveys were conducted in 2000 and in 2002, and this report details the results of the 2007/2008 survey. The first two surveys collected data at 17 count locations and the last two surveys collected data at 27 locations.
Historical comparison generally shows a decreasing trend in average VOR. However, the most recent survey reveals that even though the vehicle occupancy rate for PM is following the decreasing trend, the AM vehicle occupancy rate has increased to the 2000 level of 1.13 after a drop in 2002. The combined AM/PM rate has also increased slightly from 2002. (Note: 2007 counts for peak direction of travel only)
1.24 1.19 1.15 1.2 1.16 1.13 1.2 1.13 1.08 1.14 1.14 1.13
1 1.05 1.1 1.15 1.2 1.25
Vehicle Occupancy Rate (VOR)
Figure 8-5: Vehicle Occupancy Rates: 1995-2007
PM
Combined AM/PM AM
Figure 8-6 shows a historical comparison between Richmond MPO data (combined AM/PM rates) and USDOT average vehicle occupancy rates for work trips, as derived from the National Household Travel Survey (NHTS). The comparison indicates that declining vehicle occupancy rates are occurring at both the national level and in the Richmond metropolitan area. However, the 2007/2008 vehicle occupancy rate seems to break the declining trend by increasing slightly from 2002. This slight increase could be attributed to the 2007 vehicle occupancy counts which were taken for peak direction of travel only.
Figure 8-6: Comparison Between Richmond MPO and U.S.
Department of Transportation Data
1.1 1.12 1.14 1.16 1.18 1.2
1990 1995 2000 2001 2002 2007
Survey Year
Vehicle Occupancy Rate
US DOT Richmond MPO
Public Transportation Trends
The second component of the State of the Transportation System report analyzes the following data related to the performance of the Richmond area transit system:
• GRTC Ridership from 2002 to 2007
• RideFinders vanpool and carpool participation from 2002 to 2007
• Annual transit passenger miles for the Richmond region, the nation, and southeastern urban areas from 2000 to 2005
• Transit passenger miles per capita in southeastern urban areas, 2005
GRTC Ridership Trends
Functioning as the principal public transportation provider for the Richmond region, the GRTC Transit System (GRTC) is the focus of the public transportation trends analysis.
Concerning performance, GRTC’s 2007 Comprehensive Operation Analysis indicates that, in general, GRTC performs better than the 11 peer transit systems used throughout the report for comparison purposes. In the cost effectiveness and revenue generation measures GRTC ranks near the top in most of the rankings such as farebox recovery, cost per passenger, subsidy per trip, revenue per revenue mile and so on. GRTC’s Service Span measures show that it provides an average service span on weekdays and Saturdays and above average on Sundays. However, the general transportation efficiency which
measures the average speed of buses ranks GRTC last among its peers. This shows that GRTC buses spend more time in areas of congestion and have more boardings and alightings per stop than those of the peer group. The slow operating speed could be the result of GRTC operating primarily during peak hours when the level of congestion is the highest during the day. Therefore, the good financial efficiency exhibited by GRTC comes at the expense of providing more service during off peak times and reducing headways during peak times.
The 2007 COA analysis also indicates excess bus capacity during AM and PM peak times.
It indentifies several underperforming routes and excess bus stops on major corridors.
Some of the major recommendations of the COA are providing new bus services, better signage and schedules, transfer centers and bus rapid transit along the Broad Street corridor.
The graph in Figure 8-7 looks at GRTC ridership trends for the 2002 to 2007 period. The graph indicates that growth for the 2002 to 2007 time period has fluctuated but shown a steady increase, settling on 10,306 total annual trips for 2007.
8000 8500 9000 9500 10000 10500 11000
Total Annual Trips (1000s)
2002 2003 2004 2005 2006 2007
Year Figure 8-7
GRTC Ridership: 2002-2007
Carpool and Vanpool Trends
Looking at vehicle sharing trends, RideFinders’ carpool and vanpool participants were combined and growth trends were identified from 2002-2007. Under the management of GRTC, RideFinders provides services to foster carpool and vanpool participation throughout the Richmond region.
Figure 8-8 shows that, for the six year period, vanpool and carpool participation grew at
0 1000 2000 3000 4000 5000 6000 7000 8000
Participants
2002 2003 2004 2005 2006 2007
Year
Figure 8-8
Richmond Area Vanpool &
Carpool Trends: 2002-2007
Total Annual Transit Passenger Miles, 2000-2005
Annual transit passenger miles represent, at the regional level, the total amount of passenger miles traveled using public transportation. The graph shows average annual growth rates in total passenger miles from 2000 to 2005. In 2005, approximately 45 million passenger miles were traveled on all modes of public transit in the Richmond region. For the 5 year period, Richmond’s average annual transit usage growth rate of 2.7 percent is higher than that of the nation (0.9%) but is only half as much as that of the southeastern urban areas (5.4%). A detailed listing of the urban areas used in the analysis can be found in the CMS Technical document.
Figure 8-9
Average Annual Growth in Transit Passenger Miles: 2000 - 2005
0 0.01 0.02 0.03 0.04 0.05 0.06
Richmond Urbanized Area
Southeastern Urban Areas
Nation
Average Annual Growth Rate
Transit Passenger Miles per Capita in Southeastern Urban Areas, 2005
Transit passenger miles per capita represent, on average, the number of miles each person travels using public transportation for a given year. The graph in Figure 8-10 depicts a comparison of 2005 transit passenger miles per capita for the Richmond urbanized area and a selected group of large southeastern urban areas. The comparison shows that Washington, D.C. came in with highest figure at 475 transit miles per person, and Knoxville, Tenn. came in with lowest figure at 27 transit miles per person. The Richmond area’s 55 transit miles per person places the region in the lower half of the study group.
The three largest southeastern metropolitan areas in the analysis: Washington, D.C.;
Baltimore, Md.; and Atlanta, Ga. had from four to seven times as many passenger miles per person than the Richmond area. Washington, D.C., Baltimore, Charlotte and Atlanta are the only four urban areas in the comparison group with light rail transit or a subway system in place. The light rail in Charlotte started in November 2007 and therefore, the 2005 transit passenger miles data is pre light rail era. All of the remaining urban areas found in the graph show slight differences in passenger miles per person for 2005.
Figure 8-10
Transit Passenger Miles Per Capita in Southeastern Urban Areas, 2005
0 100 200 300 400 500
Washington D C
Baltim ore
Atlanta Charotte
Jacksonville Hampton R
oads Memphis
Durham RIC
HMOND Tallahassee
Roanoke Greensboro
Chattanooga Charleston
Knoxville
Urban Area
Transit Passenger Miles per Capita
Comparing the Richmond Area to Other Urban Areas
Since 1982, the Texas Transportation Institute at Texas A&M University (TTI) annually publishes a report highlighting information from the university’s ongoing Urban Mobility Study. Each annual report provides information on congestion and mobility for 85 metropolitan areas. Richmond is included in the study under the classification of a medium
Source: National Transit Database
Figure 8-11
2007 Urban Mobility Report Medium Urban Area Information
Population Growth 2005 Urban Area
1982-2005 Size Population Density
Urban Areas 2005 Population Change (%) (sq. miles) (persons/sq. mile)
Jacksonville, FL 990,000 61 750 1,320
Nashville-Davidson, TN 990,000 89 755 1,311
Salt Lake City, UT 970,000 42 400 2,425
Raleigh-Durham, NC 950,000 53 695 1,324
Richmond, VA 920,000 18 470 1,926
Louisville, KY-IN 905,000 15 550 1,618
Hartford-Middletown, CT 890,000 146 580 1,483
Bridgeport-Stamford, CT-NY 870,000 109 450 1,900
Charlotte, NC-SC 860,000 69 410 1,976
Austin TX 855,000 67 505 1,485
Oklahoma City, OK 850,000 24 140 5,036
Tulsa, OK 810,000 15 615 1,112
Tucson, AZ 750,000 50 255 2,647
Dayton, OH 745,000 4 350 1,900
Honolulu, HI 705,000 28 245 2,612
Birmingham, AL 690,000 78 195 3,154
El Paso, TX-NM 675,000 31 280 2,054
Rochester, NY 665,000 6 375 1,413
Springfield, MA-CT 660,000 45 205 2,289
Omaha, NE-IA 640,000 23 475 1,832
Sarasota-Bradenton, FL 640,000 160 725 1,310
Allentown-Bethlehem, PA-NJ 620,000 97 500 1,280
Fresno, CA 615,000 41 335 1,851
Akron, OH 615,000 30 350 1,600
Grand Rapids, MI 595,000 45 445 1,337
Oxnard-Ventura, CA 580,000 33 555 1,532
Albuquerque, NM 575,000 7 285 1,825
New Haven, CT 560,000 19 390 1,577
Albany-Schenectady-Troy, NY 530,000 25 480 1,552
Toledo, OH-MI 520,000 25 470 1,404
85 area average 1,804,000 37 763 2,363
Very large area average 6,023,000 33 2,125 2,835
Large area average 1,666,000 40 756 2,205
Medium area average 741,000 43 441 1,680
Small area average 321,000 50 188 1,705
Notes: Very large urban areas - over 3 million population
Medium urban areas – between 500,000 and 1 million population
Large urban areas – between 1 million and 3 million
population Small urban areas - less than 500,000 population
The primary source of information for the Urban Mobility Study is the Federal Highway Administration’s Highway Performance Monitoring System (HPMS) database, with supporting information from various state and local agencies. The Urban Mobility Study only includes data for freeways and principle arterials in each metropolitan area, thus, some statistics may be conservative.
For the CMS, three sets of information were selected and analyzed from the 2007 Annual Urban Mobility Report:
• Travel Time Index
• Traffic Congestion Cost per Person
• Annual Hours of Delay per Person
Travel Time Index
The travel time index is a ratio calculated by TTI to analyze the effect that both recurring and non-recurring (i.e., accidents, construction) delay have on travel times during daily peak travel periods. It measures the amount of additional time needed to make a trip during a typical peak travel period in comparison to traveling at free-flow speeds.
The travel time index is computed by dividing the average of all peak period trip times for a region by the average of all free flow (non-peak) travel times for the region. If an average trip in a region took 26 minutes during the peak travel period, but only 20 minutes under free-flow conditions, the travel time index would be 26/20 = 1.30. This can also be expressed by stating that the delay penalty for driving during the peak period is approximately 6 minutes.
As shown in Figure 8-12, the travel time index for the Richmond region was 1.09 in 2005.
This means that, on average, it would take a Richmond region commuter 9 percent longer to make a trip during peak travel periods than it would for the same trip at times of the day when travel occurs at free-flow speeds. Stated another way, an average 20 minute trip under free- flow conditions would take approximately 22 minutes during peak travel period conditions.
The graph in Figure 2 also shows that the travel time index for the Richmond region has grown steadily over time by 4.8 percent – from a low of 1.04 in 1982 to the currently reported 1.09 in 2005.
1.00 1.02 1.04 1.06 1.08 1.10 1.12 1.14 1.16 1.18
Travel Time Index
1982 1985 1988 1991 1994 1997 2001 2003 2005 Figure 8-12
Historical Richmond Region Travel Time Index, 1982-2005
Richmond Area TTI Medium Area Average
The average travel time index for medium metropolitan areas was 1.16 in 2005, which is a 10.5 percent increase over the 1982 value of 1.05. The Richmond region ranked 24th of the 30 medium metropolitan areas studied in 2005. The highest travel time index for medium metropolitan areas in 2005 was Austin, Texas at 1.31, and the lowest travel time index was Springfield, Mass. at 1.06.
Based on the TTI analysis, the Richmond region travel time index – or the penalty for driving during peak travel periods – has been increasing at a slightly slower rate than the average growth rate observed for all of the medium metropolitan areas studied. Additionally, the Richmond region 2005 travel time index was one of the lowest of the 30 medium metropolitan areas.
Traffic Congestion Cost Per Peak Traveler
TTI’s analysis of congestion costs primarily accounts for the amount of wasted time and fuel due to traffic congestion. Congestion costs per peak traveler is the value of travel time delay (estimated at $14.60 her hour of person travel and $77.10 per hour of truck time) and excess fuel consumption (estimated using state average cost per gallon). The value of time for 2005 is estimated for passenger vehicles and trucks and the fuel costs are the per-gallon average price for each state as reported by the American Automobile Association. The value of a person’s time is derived from the perspective of the individual’s value of their time, rather than being based on the wage rate. Only the value of truck operating time is included in TTI’s calculations; the value of the commodities carried by the trucks is not. The value of time is the same for all urban areas.
As shown in Figure 8-13, the average annual cost of traffic congestion per peak traveler in the Richmond region for 2005 was $362. This value has increased by 535 percent since 1982 when the annual congestion cost per peak traveler was $57. TTI estimates that in 2005, the entire Richmond region incurred congestion costs of approximately $181 million. This figure
has increased by 217 percent since 1982, when the total regional congestion cost was estimated at $57 million.
In 2005, the average congestion cost per capita for all of the medium metropolitan areas was
$512. This value has grown by 532 percent since 1982, when the annual congestion cost was
$81 per traveler. In 2005, the highest estimated congestion cost of all the medium metropolitan areas studied was Austin, Texas at $909 per peak traveler, and the lowest was Rochester, N.Y. at $176 per peak traveler. The Richmond region ranked 21st of the 30 medium metropolitan areas studied (i.e., 20 other areas had higher cost of congestion.)
$0
$100
$200
$300
$400
$500
$600
Annual Cost per peak Traveler
1982 1985 1988 1991 1994 1997 2001 2003 2005 Figure 8-13
Historical Annual Traffic Congestion Cost per peak Traveler for the Richmond Region, 1982-2005
Richmond Area TTI Medium Area Average
Annual Hours of Delay per Person
Annual delay per traveler is computed for the urban mobility report as the extra time required to travel in the peak period divided by the number of travelers who begin a trip during he peak period (6:00 to 9:00 a.m. and 4:00 to 7:00 p.m.) This measure illustrates the effect of the per- mile congestion as well as the length of each trip. This is an annual measure indicating the sum of all the per-trip delays.
As shown in Figure 8-14, there was approximately 20 annual hours of delay per peak traveler in the Richmond metropolitan area for 2005. This value has increased by 233 percent since 1982 when TTI recorded six annual hours of delay per traveler in the Richmond region.
By comparison, the 2007 Urban Mobility Report identified an average figure of 28 annual hours of delay per capita for all of the medium urban areas studied. This value has grown by 211 percent from the 1982 value of nine average annual hours of delay per traveler. The highest annual delay per capita for the medium urban areas in 2005 was Austin, Texas at 49 hours of delay per traveler. The lowest annual delay per capita was found in Rochester, NY and Akron, Ohio at 10 hours of delay per traveler. The Richmond region ranked 21st of the
0 5 10 15 20 25 30
Annual Hours of Delay per peak Traveler
1982 1985 1988 1991 1994 1997 2001 2003 2005 Figure 8-14
Historical Annual Hours of Delay per peak Traveler for the Richmond Region, 1982-2005
Richmond Area TTI Medium Area Average
Regional Congestion Analysis
Traffic congestion. Where does it regularly occur along our region’s roadways? How long does it take us to get to and from work everyday? Where are traffic incidents – such as broken down vehicles and crashes – located so that we can plan to travel along an alternative route to avoid delays? We all ask these questions ourselves everyday.
This report undertakes a regional congestion analysis for the Richmond metropolitan area in order to understand where and why congestion exists, in addition to providing an ongoing assessment of the metro area’s transportation system. The analysis goes through the following steps:
1. Defining congestion
2. Establishing congestion performance measures to quantify congestion 3. Geographically analyzing the congestion performance measures
The regional congestion analysis covers major roadways included within the CMS study network shown in Maps 8-1 through 8-5. This network consists of approximately 4,0702 lane-miles of existing roadways. All interstates, expressways and principle arterials within the Richmond MPO study area are included in the network.
2 Only includes roads functionally classified as interstates, expressways and principle arterial
Defining Congestion
The U.S. Department of Transportation offers a general definition of congestion:
“The level at which transportation system performance is no longer acceptable due to traffic interference. The level of acceptable system performance may vary by type of transportation facility, geographic location, and/or time of day.”
The National Highway Institute provides a slightly different definition of congestion:
“A situation that results in unacceptable travel conditions, including travel time delays, user discomfort, frustration, or unsafe conditions.”
Depending upon how traffic congestion is defined or measured, it should be evaluated both for the present and the future in any urban area. As defined above, the level of congestion and the tolerance for it, whether real or perceived, varies between urban areas of all sizes.
There are several elements that need to be considered when defining and analyzing traffic congestion:
• Duration, or how long the congestion lasts
• Spatial definition of the areas effected
• Intensity or severity of congestion
• Variability of congestion on a daily basis, that is, recurring or nonrecurring congestion.
As stated above, there are two types of congestion – recurring and nonrecurring – that can occur on a daily basis. Recurring congestion, caused by a physical lack of roadway capacity, is usually predictable and occurs regularly. This results when the capacity of the transportation system deteriorates to an unacceptable level of operation. This type of congestion generally occurs in the morning or afternoon peak hour, but may happen during holiday seasons or scheduled special events.
Traffic incidents – such as vehicle crashes and breakdowns – regularly influence the ability of our region’s highway network to move people and goods in an efficient and safe manner.
These nonrecurring events dramatically reduce the available capacity and reliability of our region’s entire transportation system. Aside from the obvious time delays that are synonymous with traffic incidents, other problems can be related to their occurrence. These include: increased air pollution, wasted fuel, and secondary crashes resulting from changes in traffic speed caused by major incidents.
Quantifying Congestion
In order to quantify congestion for the purposes of this report, a set of objective performance measures is needed. Based on the availability of data through processes that are already in place, the following two performance measures were selected:
• Roadway segment operating speed versus posted speed
• Roadway segment volume to capacity ratio
Roadway Segment Operating Speed Versus Posted Speed
RAMPO initiated the collection and analysis of travel time and speed data along the region’s major travel routes with the development of the 2006 Regional Travel Time/Speed Technical Document. The operating speed and travel time data collected mostly in the spring of 2006 is the third in a series of travel time and speed studies to support the MPO’s CMS activities.
To collect the travel time and speed data, RAMPO staff conducts a regional travel time survey utilizing global positioning system (GPS) and geographic information system (GIS) technologies. The GPS/GIS data gathering and analysis technique efficiently records data for spatial analysis of travel speeds that result in the provision of an overall picture of regional travel, and it helps in the identification of possible problem areas.
The GPS units are used to map and track speeds along identified corridors. Data collection involves a vehicle equipped with a GPS receiver. Vehicle drivers drive with the flow of traffic while not exceeding the posted speed. During each travel time run, the GPS unit collects geographic position, time, and speed data once every five seconds and this data is then fed into a GIS system for analysis. The GIS software is used to conduct data analysis and provide visual displays of the study results. The results show operating speeds and travel times for all of the roadways analyzed in the survey.
Between February 2006 and June 2006, travel time and speed data was collected on approximately 796 centerline miles of roadway, including all of the interstate routes and expressways and selected primary routes that make up a portion of the CMS network for the MPO. Twenty three corridors (up from 16 corridors studied in the 2003 report) were selected and divided into 46 road segments representing eastbound/westbound or northbound/southbound travel.
These corridors were selected following the rationale established in previous travel time and speed studies. Namely, corridors were chosen to meet the goal of providing travel time and speed data for, at a minimum, all of the interstates and expressways found within the MPO study area. Additionally, selected primary routes are included as they represent major facilities which serve to connect the Richmond region together – they represent heavily traveled routes between the central city and its surrounding localities.
For each of the 46 road segments, travel time and speed data were collected utilizing the following parameters:
1. Data was mostly collected in the spring of 2006 and the collection effort was resumed and completed during the fall of 2006.
2. Sampling was conducted during the peak travel periods of 7:00 a.m. to 9:00 a.m.
and 4:00 p.m. to 6:00 p.m. when most people commute to and from work.
3. The a.m. and p.m. peak travel periods were divided into four 30-minute increments. The a.m. runs were conducted from: 7:00-7:30, 7:30-8:00, 8:00-8:30, and 8:30-9:00 and the p.m. runs were conducted from: 4:00-4:30, 4:30-5:00, 5:00- 5:30, and 5:30-6:00.
4. For each of the four time periods, a single data collection run was conducted in the peak direction of travel only. This resulted in each of the 46 road segments being driven four times. For the 2006 study, approximately 3184 miles were driven to cover the four evening or four morning peak periods identified for each of the 46 directional segments.
5. Data was collected on Tuesdays, Wednesdays, and Thursdays only as these days are considered most representative of average weekday travel behavior and commute conditions.
6. Study results are summarized in a manner generally consistent with previous studies.
The spatial analysis of the GPS data assigned a different color for each collected data point based on the following classification scheme:
• Operating speed of less than 5 mph under posted speed limited (labeled as “non- congested”)
• Operating speed of 5-20 mph under posted speed limit (labeled as “impaired”)
• Operating speed of 21 mph or more below the posted speed limit (labeled as
“congested”)
Once the classification scheme was applied to the data points, the information was mapped and interpreted with the goal of identifying corriders that are ‘congested’ and
‘impaired’. The mapping analysis showed a.m. and p.m. results both separately and combined to identify the congested corridors. The 21 mph threshold was due to the fact that locations with this classification were considered as operating under adverse conditions and were identified as “Areas of Concern.”
Map 8-6 and 8-7 show the morning and afternoon analysis respectively. The roadway segments highlighted in red on the map represent corridors that are ‘congested’ and the roadway segments that are highlighted in orange represent corridors that are ‘impaired’for the respective length of roadway. May 8-8 shows the combined morning and afternoon analysis. In order to analyze the combined morning and afternoon congestion the
• Operating speed of 21 mph under posted speed limit for both AM and PM (thick red highlight labeled AM and PM Congested)
• Operating speed of 21 mph under posted speed limit for either AM or PM (thin red highlight labeled AM or PM Congested)
• Operating speed of 21 mph under posted speed limit for either AM or PM and operating speed of 5-20 mph under posted speed limit for either AM or PM (thin purple highlight labeled AM and PM Congested/Impaired)
• Operating speed of 5-20 mph under posted speed limit for both AM and PM(thick orange highlight labeled AM and PM Impaired)
• Operating speed of 5-20 mph under posted speed limit for either AM or PM (thin orange highlight labeled AM or PM Impaired)
Delineation for each of these corridors can be found under the Section six appendix of the CMS Technical document
Several congested areas identified in the 2006 analysis were also identified in the previous 2003 travel time and speed analysis; they are:
• Segments of Interstate 95 and Interstate 64 in the vicinity of the Bryan Park interchange
• Interstate 64 in the vicinity of the Shockoe Valley Bridge
• Laburnum Avenue (Rte 197) in the vicinity of the Williamsburg Road intersection
• The Powhite Parkway (Rte 76) just around Chippenham Parkway
• Parham Road from Patterson Avenue (Rte 6) to Staples Mill Road (Rte 33)
• Mechanicsville Turnpike (Rte 360) from I-295 to Rte 615
Some new congested areas identified in the 2006 analysis are:
• Midlothian Turnpike just east of Rt 288
• Williamsburg Road from I-295 to New Kent CL
• Staples Mill Road from Overhill Lake Road to Rte 670 West
Roadway Segment Level of Service (LOS)
LOS is commonly used in transportation planning to gauge how a roadway is operating.
LOS assigns a letter grade from A to F to indicate the roadway’s performance – A is excellent, F is failure. To get the LOS letters, transportation planners traditionally look at a roadway’s V/C ratio and other factors such as intersection delay and operating speed.
The LOS data used for the CMS report is based on traffic counts as reported in the Virginia Department of Transportation Statewide Planning System. Refer to Figure 8-15 below for a graphical depiction of the six LOS categories.
The CMS technical report includes a twenty-four hour period (2005) Level of Service (LOS) analysis of the CMS roadway network for the Richmond MPO study area. LOS measures congestion by looking at what percentage of the roadway’s capacity is being used by traffic.
Figure 8-15: Roadway Levels of Service
Source: Developed for the US DOT Bureau of Transportation Statistics by the MIT Department of Urban Studies & Planning.
Map 8-9 shows the results of the LOS analysis for the Richmond region. Delineation for each of these corridors can be found under section six appendix of the CMS Technical document.
Once both of the performance measures have been analyzed, a final map was developed to provide a combined view of congestion as identified through both data sources.
The results of the combined LOS and operating speed versus posted speed congestion analyses can be seen in Map 8-10. It is important to note that the operating speed versus posted speed data covers only a subset of the roads analyzed by the LOS data; therefore, this is not intended to be a perfect analysis of two data sets that cover the same extent of roadways. Map 8-10 was created to simply provide one viewing source for the findings taken from the two studies.
The results presented here represent the congestion analysis process that will continually work toward having two data sets that cover the same network of roadways. Compared to the 2003 operating speed versus posted speed study, this study has additional 212 miles of roadway network; however, this study still does not cover the entire CMS road network.
Once data from both sources are available for the entire CMS roadway network, a new step can be added to the regional congestion analysis. This step would seek to identify congested areas common to both data sets.
In conclusion, more study of travel and commute patterns – as well as increased data collection efforts – are recommended to continue to get a better understanding of the predominant travel corridors and commuter routes throughout the region; and to ensure that all major trip movements are being captured in the CMS congestion analysis.
Safety
In 2006, there were nearly 3,141 crashes3 reported on Richmond region interstates and expressways with 1,304 injuries and 25 fatalities. The number of crashes grew by 42 percent in the Richmond region between 2000 and 2006. During this period, the number of injuries resulting from traffic crashes increased by 9 percent and the number of fatalities increased by 47 percent. This large increase in fatalities includes a big jump (155%) from 2001 to 2002.
During this time period, the vehicle-miles of travel in Richmond region increased by 33 percent when the average annual growth rate of vehicle-miles of travel from 2000 to 2005 was only nine percent.
In 2006, out of 3,141 crashes that took place in the region, 118 (3.8%) were alcohol related causing 78 injuries and 2 deaths.
Figure 8-16
Crashes, Injuries, and Fatalities in Richmond Region's Highways and Expressways, 2000 to 2006
0 500 1000 1500 2000 2500 3000 3500
2000 2001 2002 2003 2004 2005 2006
Crashes/Injuries per year
0 10 20 30 40 50 60 70 80 90 100
Fatalities per year
Total Number of Crashes Number of Injuries Number of Fatalities
Source: Virginia Dept of Transportation, 2007. This data set is for VDOT maintained roads only and does not include Urban roadways (i.e. City of Richmond and Town of Ashland roads) and Henrico County secondary roads). Therefore, for the purpose of consistency, the analysis was limited to the interstates and expressways.
Regional Crash Locations
Map 8-11 shows reportable crashes on interstates and expressways of the Richmond region in 2006. The number of crashes in the region increased 42 percent from 2000 to 2006; however the data on the individual jurisdictions show that that New Kent has experienced the most increase (122%) in the number of crashes while Chesterfield has experienced the least (11%).
Map 8-12 indentifies location of crashes involving fatalities in 2006. Although the data was not analyzed to determine specific location of fatalities, it is clear that fatalities have increased significantly on I-95 around I-295 east of I-95 in the City of Richmond.
Map 8-13 shows the total number of crashes in the region involving injuries in 2006 respectively.
Using Technology to Manage Traffic Congestion
Up to this point, Chapter 8 has focused on evaluating the overall Richmond area transportation system and identifying where congestion currently exists along the region’s major travel routes. This section of the chapter seeks to identify the use of current technology – or Intelligent Transportation Systems – as an available solution to help solve congestion problems without many of the monetary and environmental costs associated with creating new or expanding existing roadways.
What is ITS?
ITS is an acronym that stands for Intelligent Transportation Systems. The basic goal of ITS programs across the country is the use of modern computer and communications technologies to aid in the management of our existing transportation systems. When integrated into the transportation system infrastructure, and in vehicles themselves, these technologies help monitor and manage traffic flow, reduce congestion, provide alternate routes to travelers, enhance productivity, and save lives, time, and money.
Intelligent transportation systems provide the tools for skilled transportation professionals to collect, analyze, and archive data about the performance of the system during the hours of peak use. Having this data enhances traffic operators' ability to respond to incidents, adverse weather, or other capacity constricting events.
Examples of intelligent transportations systems include:
Advanced Traveler Information Systems deliver data directly to travelers, empowering them to make better choices about alternate routes or modes of transportation. When archived, this historical data provides transportation planners with accurate travel pattern information.
Advanced Traffic Management Systems employ a variety of relatively inexpensive detectors, cameras, and communication systems to monitor traffic, optimize signal timings on major arterials, and control the flow of traffic.
Incident Management Systems, for their part, provide traffic operators with the tools to allow quick and efficient response to accidents, hazardous spills, and other emergencies.
Redundant communications systems link data collection points, transportation operations centers, and travel information portals into an integrated network that can be operated efficiently and "intelligently.”
