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Showcasing Visualization Tools in Congestion Management

Visualization Methods Used in CMP Activities

The following section of this report provides specific examples of different types and methods of visualizations that are utilized by MPOs around the country as part of their CMP activities. These examples range from simple graphs and color-coded maps to complex location-time-delay diagrams, travel time contour maps, photographic simulations, and hybrid combinations of maps and tables.

Displays of measured congestion based on observed data

One common type of visualization used as part of the CMP is the mapping of data collected during CMP system monitoring. There are several types of data that are collected by MPOs as part of this monitoring, and there are several ways in which to display the data.

Several examples of visualization techniques are presented in the following section, through a series of graphic examples, in order of increasing complexity. There are examples from other MPOs that could have also been selected, as some of the same techniques are used by different MPOs. For example, the schematic maps of freeway level of service shown in Figure 5 for the Washington area could also have used displays prepared for the North Central Texas COG, the Atlanta Regional Commission, or the East-West Gateway Coordinating Council, all of which have similar displays that have been tailored to their MPO area and needs.

Figure 1: Many MPOs collect speed and delay data for corridors as part of their CMP activities. These are often collected by conducting travel time samples, sometimes using GPS technology. These types of data can be displayed on both corridor-level maps and region-wide maps. Corridor maps can be simple color-coded displays, such as those used in the Metroplan CMP for the Little Rock, Arkansas region.

Metroplan utilizes detailed congestion/speed maps such as these in their CMP in order to assess likely sources of congestion and location-specific congestion mitigation strategies for each segment. These strategies may include implementing operational improvements or capacity-adding projects, applying access management (AM) techniques, or using intelligent transportation systems (ITS) technologies.

Highway 10 travel time samples. Three maps show the speed of a vehicle making travel time runs along Highway 10 on three different mornings around 7:30 A.M. Colors are used to illustrate speeds along the route and red dots indicate where the vehicle was stopped. Major roads are labeled on the map for reference. The maps also list the travel time runs start and end times.

Source: "2008 CMS Report", Metroplan (Little Rock, AR), 2008

Figure 2: This example from the Capitol Region Council of Governments (CRCOG) in Hartford, Connecticut uses similar color-coded schematic maps to display data collected as part of the regional freeway ITS system. The basic color-coded maps are a simple, easily-comprehended method of visualizing this information. More complex maps, such as speed-time-location visuals, are more effective at showing detailed information available from ITS, such as the locations of bottlenecks, the extent of backups, and the duration of congestion, but may be more difficult for non-practitioners to understand.

This map allows CRCOG planners to assess segment-level performance and determine how conditions vary within each corridor; ultimately, specific areas can be pinpointed that require attention.

Average speed on area freeways, inbound direction for the average weekday, 7:00 to 8:00 A.M. This map has five freeways radiating out from the center of the region. Different colors show the average speed of segments.

Source: "Transportation Monitoring and Management Report: Metropolitan Hartford Area, 2005", Capitol Region Council of Governments, 2007.

Figure 3: This is an example of using regional-scale maps with color-coding to display measured speed and congestion data and metrics derived from these data. This example from the Ohio-Kentucky-Indiana Regional Council of Governments for the Cincinnati, Ohio region uses data derived from travel-time surveys conducted throughout the region over a period of three years.

This map is useful in terms of providing a large picture, generalized depiction of delay: planners can focus in on the most concentrated areas of red, or high delay areas, and determine sources of congestion and methods to minimize it.

Observed Congestion, 2004-2007. Levels of observed delay on major roads in the region, with none or low congestion in green, moderate congestion in yellow, and high congestion in red.

Source: "OKI Congestion Management Process Findings and Analysis", Ohio-Kentucky-Indiana Regional Council of Governments, 2007

Figure 4: This is an example of a region-wide congestion visualization from the Baltimore Metropolitan Council that uses a color-coded palette to display travel time data and metrics. The data source for this display used seven GPS-based travel time samples for the PM peak-period for each corridor, for both directions. Data was averaged across the samples every 1/10th of a mile, or about every 500 feet in both directions, using a GIS-based database management system, which also prepared the display. Data for freeways and arterials are shown on the display but different speed ranges are used to reflect differences in link-speed performance for freeways and arterials.

The data collected and the map below allowed the Baltimore Metropolitan Council (BMC) to conduct an analysis highlighting particular segments of roadways in areas that show severe congestion during the evening commute. The observed conditions and causes of congestion on each severely-congested segment are then discussed, enabling segment-specific mitigation to be proposed when BMC decides to act.

Figure 4: Region-wide congestion visualization from the Baltimore Metropolitan Council. Click for more information.

Source: "Congestion Monitoring Using GPS in the Baltimore Region: Travel Speeds in the Evening Peak, Spring 2004", Baltimore Metropolitan Council, 2004

Figure 5: At the Metropolitan Washington Council of Governments (MWCOG) in the Washington, DC region, the CMP uses data that is generated from low-level aerial photographic surveys of major corridors. Several passes are made on each photograph run, collecting longitudinal data through each three-hour peak period. The photographic data are then converted into a measure of level-of-service, which can be mapped schematically. Schematic maps are developed to show the congested areas along each corridor, supplemented with level-of-service data in the unique tabular format presented here in Figure 5. An advantage of this technique is that it presents the main message (highly-congested segments of the freeway network) in a clear, easy-to-understand format, while also conveying aspects of the detailed technical information. This is also an example of a combined spatial-temporal display as it shows the variation in congestion by three hourly time periods within the morning 3-hour peak-period. A similar graphic is also available for traffic traveling in the opposite direction around the Capital Beltway. This map allows MWCOG to break apart each congestion location, and define the type of congestion, frequency, direction, estimated speed, and speculated cause of congestion in their CMP.

Figure 5: Low-level aerial photographic surveys of major corridors. Click for more information.

Source: "Traffic Quality on the Metropolitan Washington Area Freeway System", National Capital Region Transportation Planning Board, Prepared by Skycomp, Inc., May 2009.

Figure 6: This example from the Chicago Metropolitan Agency for Planning is a detailed display of a travel speed contour based on archived ITS detector data from a private sector data provider. The graph shows average directional speed by location along the expressway corridor as well as by time of day throughout a selected sample day. This display shows, for example, that the westbound PM congestion conditions occurred over a two hour period between 4 and 6 PM, and were concentrated between about milepost 4.0 and milepost 5.5, with the most concentrated congestion being between mileposts 4.5 and 5.0. This display enables planners to focus in on the most densely congested areas and assess the extent and seriousness of congestion around each area.

Figure 6: Detailed display of a travel speed contour based on archived ITS detector data from a private sector data provider. Click for more information.

Source: "Traffic Quality on the Metropolitan Washington Area Freeway System", National Capital Region Transportation Planning Board, Prepared by Skycomp, Inc., May 2009.

Figures 7a-c: In the Wilmington Area Planning Council (WILMAPCO) CMP, a wide range of congestion-related measures is displayed in a series of maps, of which three are included here. These displays show observed data, such as travel time and speed in Figure 7a, derived data, such as roadway and intersection level of service in Figure 7b, and gathered data, such as archived crash location/crash rate data in Figure 7c. The regional-scale maps are consistent from metric to metric and from year to year, making them easy to read and making patterns and trends easy to identify.

On WILMAPCO’s website, users can click on each of the maps below to view data collected for the CMP through Google Maps technology. These links allow users to explore the details regarding the materials analyzed in determining the CMP corridors and their possible mitigation strategies.

Figure 7a: Collected Speed Data. Click for more information.

Source: WILMAPCO CMS Website: http://www.wilmapco.org/cms/

Figure 7b. Derived Level of Service Data. Click for more information.

Source: WILMAPCO CMS Website: http://www.wilmapco.org/cms/

Figure 7c: Gathered Crash Data

Source: WILMAPCO CMS Website: http://www.wilmapco.org/cms/

Figure 8: The Baltimore Metropolitan Council (BMC) uses interactive mapping to make available map-linked information on the Baltimore region, allowing users to navigate, explore, manipulate, and customize BMC spatial data. The maps are designed to provide information about a particular topic, such as projects in the Transportation Improvement Program, crashes, or traffic counts. This image below overlays BMC color-coded data on level of service (based on observed travel speeds on regional highways) with an image from Google Earth mapping service.

This is a bird's-eye view of a 3-D model built in Google Earth, with roads shown in the aerial image and buildings shown (in 3-D) at their approximate heights. Colored lines are superimposed on the roadways to illustrate levels of service.

Source: BMC Website: http://www.baltometro.org/content/view/726/496/

Displays of forecasted or modeled conditions

One common analysis approach in many CMPs is to use model results (whether current or forecasted) as a primary information source. Since models themselves are built using geographic data, this information can be easily displayed graphically in the form of maps. There are several ways that modeled results can be shown, including color-coded maps rating corridors and facilities by performance measures, travel time contour maps, maps using model area-based features (such as traffic analysis zones) to display the resulting forecast information, and time-space diagrams. Figures 9 through 13 provide examples of these types of visualizations.

Figure 9: The Miami Valley Regional Planning Commission (MVRPC) in the Dayton, Ohio region uses model results of both current and future forecasted conditions as part of its CMP analysis. The results of this analysis are displayed in simple color-coded maps showing the performance of the facilities as measured against several performance measures. This figure shows an example of a map developed by MVRPC, showing level of service for the year 2030, highlighting those areas anticipated to be congested. This allows MVRPC planners time to determine where they need to prepare for minimizing future congestion before it overwhelms the area. They can do this through targeting strategies at certain segments with an "F" level of service in 2030 in a proactive instead of responsive way.

Level of Service, Existing + Committed (2030): Based on the 2030 Long Range Transportation Plan, this map shows major roadways in the region with level of service D (green), E (blue), or F (red). There is an inset map that zooms in on the downtown Dayton area at the center of the region.

Source: "Congestion Management Process Technical Report", Miami Valley Regional Planning Commission, 2007.

Figure 10: The Wasatch Front Regional Council (WFRC) in the Salt Lake City region uses 3-D graphics as a method of communicating additional information, beyond simple color-coded line maps. This figure is an example of a graphic using both color and vertical height to display regional delay patterns in modeled 2006 and 2030 information, creating an effective visual representation of the locations with significant delay estimates.

Maps quantifying future travel demand such as this one calculate how the existing transportation system would perform in the horizon year 2030, and allow WFRC to identify future transportation system needs, and act now to address them.

2030 PM Peak Delay: This is a bird's-eye view of a 3-D drawing of congestion on the region's major roadways during a typical afternoon peak period in 2030. The roadways are displayed as fins (lines with height), whose height and color distinguish the varying degrees of congestion along arterial and freeway facilities. The length of the fin matches the length of the roadway section.

Source: "Adopted 2007-2030 Regional Transportation Plan", Wasatch Front Regional Council, 2007.

Figure 11a: Travel time contour maps can be made using any type of speed/travel time data, but are typically made using modeled results rather than observed data (or sometimes with a combination of the two) to ensure full coverage of the region. Figure 11a from the Atlanta Regional Commission shows the travel time during the peak period between downtown Atlanta and all points within the MPO area, as well as the non-congested free-flow condition, using 15-minute color-coded bands. This provides a quick snapshot of the effects regional congestion have on regional travel times.

Figure 11a: Travel time contour maps can be made using any type of speed/travel time data. Click for more information.

Source: "Congestion Management Process Update 2005: Technical Memorandum 5", Atlanta Regional Commission, 2005.

Figure 11b: Similar to the example from Atlanta above, Figure 11b is a travel time contour map from the Mid-Region Council of Governments in Albuquerque, New Mexico. In this example, the contour display is not constrained to the area within the MPO boundary, but is constrained to only show those areas within sixty minutes of the point of destination. This results in a map with a different visual appearance than the Atlanta example, but is based on similar forecasted peak period travel time estimates.

Figure 11b: Travel time contour map from the Mid-Region Council of Governments in Albuquerque, New Mexico. Click for more information.

Source: "2030 Metropolitan Transportation Plan", Mid-Region Council of Governments, 2007.

Figure 12: In the Dallas-Fort Worth area, the North Central Texas Council of Governments (NCTCOG) has developed maps showing areas of congestion, as opposed to congested corridors or facilities. These areas are defined based on various performance measures, applied to geographic areas (such as traffic analysis zones in the regional model). Figure 12 shows an example of this type of map.

This map demonstrates what will result if the projects, programs, and policies in Mobility 2030: The Metropolitan Transportation Plan are implemented. It leads NCTCOG to conclude that if they are to meaningfully reduce congestion levels, they must pursue additional congestion mitigation strategies to reduce SOV travel and make the transportation system more efficient. The 36% increase in congestion in 2030 shows that while construction of new facilities will take place, effective and practical solutions to address the air quality and traffic congestion challenges will need to be identified and implemented.

Figure 12: Result if the projects, programs, and policies in Mobility 2030: The Metropolitan Transportation Plan are implemented. Click for more information.

Source: "Congestion Management Process Document", North Central Texas Council of Governments, http://nctcog.org/trans/cmp/

Figure 13: The Puget Sound Regional Council (PSRC) in the Seattle, Washington region has developed time-space diagrams, similar to those developed in Chicago using observed data (see Figure 6). However, at PSRC model results are used to develop a before-and-after picture of the congestion along a facility due to the presence of proposed improvements. This is a key link between the CMP and the project development process at PSRC.

Compelling visions such as Figure 13 have helped PSRC communicate to elected officials, the media, and the public complex information about the transportation system and congestion. When data is presented in an appealing way, message disseminations can be increased as stakeholders use graphics in their communications to convey the importance of improvements.

Figure 13: These graphics show what a typical weekday morning drive is like on I-405 between SR 522 and SR 520. Click for more information.

Source: "Congestion Management Process Document", North Central Texas Council of Governments, http://nctcog.org/trans/cmp/

Displays of congestion trends and variability observed over time

Figures 14a-b: The ongoing data collection that occurs as a result of the CMP can also be a source of information on congestion trends over time. Several MPOs have developed methods of displaying this trend data on maps. There are two common approaches: to display results for several different years side by side on the map, as is done in the North Central Texas Council of Governments (NCTCOG) example in Figure 14a; and to display the relative change in results between two time periods, as is done in the Wilmington Area Planning Council example in Figure 14b. The NCTCOG method provides more detailed information than the Wilmington method, but may also be more difficult for laypeople to understand. Both methods are useful for tracking change over time, and can be useful tools in determining whether implemented CMP strategies are effective at addressing congestion concerns in certain corridors.

Figure 14a-b: ongoing data collection that occurs as a result of the CMP can also be a source of information on congestion trends over time. Click for more information.

Source: "2007 Traffic Conditions in the Dallas-Fort Worth Metropolitan Area", North Central Texas Council of Governments, 2007.

Figure 14a-b: ongoing data collection that occurs as a result of the CMP can also be a source of information on congestion trends over time. Click for more information.

Source: "2009 WILMAPCO Congestion Management System Summary", Wilmington Area Planning Council, 2009.

Figure 14a presented above shows the average temporal-variability within each year, the spatial variability along the roadway corridor, as well as the year-to-year trends of both. Most of the earlier visualizations of congestion have focused on the spatial variation in congestion levels from roadway to roadway. In addition to the year-to-year temporal variability, the temporal variability within the hours of a typical weekday, by day of the week, month of the year, or by season is also of interest to the general public, planning staff, and decision makers. The ability of the MPOs to gather such temporal variability of congestion is only recently becoming feasible through the use of archived data from various traffic management and/or traveler information systems.

Figures 15a-c: Figure 15a is one of a series of visuals from the draft 2010 CMP Technical Report being developed by staff of the Metropolitan Washington Council of Governments (MWCOG) in the Washington, DC area that is based upon the summarization of archived data from the I-95 Vehicle Probe Project purchased from INRIX, Inc. The visual shows the spatial variability of calculated travel time indexes per roadway segment and direction for the AM peak period for all workday-weekdays in 2009. It focuses on the spatial variability for the "covered highways", which includes many of the area freeways and some arterials. The primary purpose of the operational, real-time data set is for interregional traveler information and as such not all of the roadways that could be sampled were actually sampled. Over time, more complete spatial coverage is being anticipated.

Figure 15a: Sample Display of Travel Time Index for Weekday A.M. Peak for 6 to 10 A.M. for 2009 for the I-95 Corridor Coalition Covered Highways

Figure 15a: Sample Display of Travel Time Index for Weekday A.M. Peak for 6 to 10 A.M. for 2009 for the I-95 Corridor Coalition Covered Highways. Click for more information.

Source: "2010 CMP Technical Report (Draft) April 30, 2010", Metropolitan Washington Council of Governments, 2010, Fig 19.

The same set of archived data was summarized to develop the Figure 15b that shows the temporal variability by time of day and day of the week, and Figure 15c that shows the month-to-month variability of the two peak periods compared to the daily totals. It is noted that this is the first attempt that we are aware of to use this new data source for CMP travel monitoring purposes. Doing so required a considerable effort to "mine" millions of data records and to appropriately organize and summarize them in such graphical and GIS-based map formats.

Figure 15b: Travel Time Index by Time of Day and Day of Week in 2009 for the I-95 Corridor Coalition Covered Highways

Travel Time Index by Time of Day and Day of Week in 2009 for the I-95 Corridor Coalition Covered Highways. Click for more information.

Source: "2010 CMP Technical Report (Draft) April 30, 2010", Metropolitan Washington Council of Governments, 2010. Fig. 23.

Figure 15c: Travel Time Index by Month in 2009 for the I-95 Corridor Coalition Covered Highways

Travel Time Index by Month in 2009 for the I-95 Corridor Coalition Covered Highways. Click for more information.

Source: "2010 CMP Technical Report (Draft) April 30, 2010", Metropolita Washington Council of Governments, 2010., Fig 21.

Displays of reliability data

Figure 16: Information on the reliability of transportation systems is often collected as part of the Congestion Management Process. Several MPOs have developed ways of displaying reliability data on maps. Some, such as those used by the Capital District Transportation Committee (CDTC) in Albany, New York, use derived metrics such as the planning time index. The CDTC maps use line widths to display base travel time and the additional travel time built into travel time planning to account for non-recurring congestion and delay. Many MPOs also develop simple maps displaying high-crash locations as a way to address the issue of non-recurring congestion. Figure 16 shows examples of these reliability visuals developed by CDTC.

Figure 16: Information on the reliability of transportation systems is often collected as part of the Congestion Management Process. Click for more information.

Source: "The Metropolitan Congestion Management Process", Capital District Transportation Committee, 2007.

Displays of multimodal and transit data

Many MPOs include transit service and bicycle/pedestrian facilities in their analysis of congestion, both in terms of system performance and as a potential congestion management strategy. There are many ways in which this multimodal information can be visually displayed, ranging from simple maps of the locations of transit routes or bicycle/pedestrian facilities to detailed analyses of the congestion, level-of-service, and quality of these services and facilities.

Figure 17: The Hillsborough County MPO in Tampa has an effective way of displaying information on the availability of multimodal facilities and services, in comparison with areas of highway congestion, through a series of strip maps shown side by side. These maps are well-suited for analysis of whether the multimodal system is aligned with the congestion-mitigation needs of the highway system. Therefore, these maps can be utilized to identify those areas where needs are not met, and plan for future construction of bus routes or increased bicycle/pedestrian facilities necessary for congestion mitigation.

There are three schematic maps in this figure, showing different modes serving a particular corridor. All three maps use the same base map that shows local roads. The first map shows bus routes serving the corridor, the second map shows the volume to maximum service volume ratio for road segments, and the third map shows bicycle and pedestrian facilities. Pedestrian facilities are shown as the percent of the road segment with sidewalks.

Source: "Congestion Management System Performance Report", Hillsborough County MPO, 2005.

Displays of recommended strategies for implementation

Figure 18: Many MPOs develop graphics to show the strategies that are recommended in the CMP. This provides an easy-to-read and understand one-stop source for location-based information on the strategies in the CMP. Maps can be developed to cover specific spot locations, corridors, or entire regions. The example shown below, from the Miami-Dade MPO in Miami, Florida, shows the strategies recommended as the result of a corridor analysis in their CMP.

Figure 18: An easy-to-read and understand one-stop source for location-based information on the strategies in the CMP. Click for more information.

Source: Miami-Dade MPO LRTP Interactive Project Tool www.miamidade2035transportationplan.com/ProjectGuide/

Charts, graphs, and tables

Charts, graphs, and tables are a clear, easy to understand way of visualizing data and analysis results. Many MPOs use these as part of their CMP-related reporting. A few notable examples of these types of displays are shown in Figures 19-22 below.

Figure 19: This table from the Washington Metropolitan Area Transit Authority, used in the Metropolitan Washington Council of Governments’ CMP, clearly shows the relative levels of congestion on several transit lines in an easily-understood manner. It alerts the public to future capacity problems along certain lines, and can be used to help show policy makers the need for more funding or management and operations strategies for the Metrorail system to meet the needs of its ridership growth.

Figure 19: Metrorail AM Line Capacity at Maximum Load Segments. Click for more information.

Source: MWCOG CLRP Website, http://www.mwcog.org/clrp/performance/congestion.asp

Figure 20: This bar graph from the Southwestern Pennsylvania Commission (SPC) in the Pittsburgh region shows detailed delay data for a specific corridor in the CMP analysis network, based on collected travel time and delay data. The fact that delay per vehicle per mile is significantly greater between Saxonburg Boulevard and Kittanning Street than in other segments allows SPC to conclude that targeted operational improvements may be an appropriate congestion mitigation strategy for this segment.

Figure 20: AM Peak Hour Delay Locations. Click for more information.

Source: SPC CMP Website, http://www.spcregion.org/trans_cong.shtml

Figure 21: The bar graphs below from the Wasatch Front Regional Council (WFRC) in Salt Lake City use color to highlight time and extent of congestion. They are a visually impressive means of showing the change in congestion between 2008 conditions (left side) and 2009 conditions (right side). This allows WFRC to assess whether any congestion management strategies they implemented in 2008 have alleviated any congestion, and if so, exactly when during the day the strategies are most effective.

Figure 21: Bar graphs from the Wasatch Front Regional Council (WFRC) in Salt Lake City use color to highlight time and extent of congestion. Click for more information.

Source: "WFRC Traffic Congestion Report", Wasatch Front Regional Council, 2009.

Figure 22: This graph from the Southwestern Pennsylvania Commission (SPC) in Pittsburgh shows variability in speeds caused by incidents, as a measure of non-recurring congestion. While SPC’s CMP does not currently measure non-recurring congestion, graphs like this help inform regional strategies for it, such as incident management, special event planning, and work zone management.

Figure 22: I-579 Veteran's Bridge. Click for more information.

Source: SPC CMP Website, http://www.spcregion.org/trans_cong.shtml

Use of visuals to differentiate among CMP strategy options

Beyond simply using visualizations to convey data, several MPOs use photo-simulation and other visual tools to conceptually convey the ideas presented as potential CMP strategies. The Capital District Transportation Committee, the MPO for the Albany area, uses photographs and photo-simulations to show the public what different CMP strategies would look like on the ground. This effort is tied in with the MPO’s focus on issues of livability. An example of a before-and-after simulation showing potential multimodal facility improvements as a CMP strategy is shown in Figure 23.

Figure 23: These visuals show an existing corridor in the Albany area (top) and an example of what this corridor could look like with improvements to the bicycle and pedestrian infrastructure. The MPO uses these visuals to help the public understand differences between strategies outlined in the CMP.

This graphic shows two photographic images. The first is of the existing conditions along a wide corridor with a wide travel lane in each direction and a striped median. The second image is a photographic simulation of the concept for the corridor. There is now a large, landscaped median in the center of the road with grass and a tree, much narrower travel lanes, a bicycle lane, and on-street parking on one side of the street.

Source: Capital District Transportation Committee

Figure 24: The Green Bay MPO has used Google SketchUp, a three-dimensional computer drawing program, to develop graphics showing the potential impacts of improvements along corridors to neighboring properties, as part of a CMP-related corridor study. The example below shows the potential improvements in relation to one of the affected properties - additional graphics were developed for each of the affected properties in the corridor.

A 3-dimensional computer model shows a bird's-eye view of a roadway with a median, a couple of buildings set back from the road, and a path running alongside the roadway, separated by a grassy strip from the roadway. Lines on the image note the distance from the front of the main building to the path and to the roadway and from the back of the main building to the back of the lot. Some trees, cars, and pedestrians are also shown.

Source: Green Bay MPO, http://www.co.brown.wi.us

Use of video or animation in visuals

Figure 25: This video from Evans City in the Southwestern Pennsylvania Commission (SPC) region demonstrates the result of SPC’s regional traffic signal program, which is an outgrowth of its CMP. It utilizes an appealing visualization to make the results of the program more tangible and real, allowing drivers to see exactly how change will affect them. The display shows traffic conditions before and after the implementation of several CMP strategies at this location, and highlights the vast improvement in travel time through the corridor (as both videos are played simultaneously). SPC has found this video to be very useful in encouraging other municipalities to pursue similar types of operational improvements.

Figure 25: Still frame from video Evans City in the Southwestern Pennsylvania Commission (SPC). Click for more information.

Source: SPC Transportation website: http://www.spcregion.org/trans_ops_traff.shtml

Updated: 07/25/2011
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