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Federal Highway Administration Research and Technology
Coordinating, Developing, and Delivering Highway Transportation Innovations

 
REPORT
This report is an archived publication and may contain dated technical, contact, and link information
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Publication Number:  FHWA-HRT-15-072     Date:  December 2016
Publication Number: FHWA-HRT-15-072
Date: December 2016

 

State of The Practice on Data Access, Sharing, and Integration

 

CHAPTER 2. CURRENT INTEGRATION PRACTICES

This chapter identifies and discusses current State and local agency practices of collecting, sharing, integrating, and using transportation data across multiple organizations. The focus is transportation planning and operations data. To examine current practices, the study team conducted targeted outreach through the National Transportation Operations Coalition members via their e-newsletter and Transportation Research Board committees—such as the Regional Transportation Systems Management and Operations Committee—to ensure that all possible examples within the United States were captured. In addition, conversations were held with practitioners in several regions who were active in data integration and sharing to capture effective practices and obtain a better understanding of the qualifications of each region to be the potential subject of the proof-of-concept test.

DENVER, COLORADO

Effective Practices

Regional Concept of Transportation Operations

The Denver Regional Council of Governments (DRCOG) adopted the Regional Concept of Transportation Operations (RCTO), which describes a collaborative plan to improve operations performance across the region over the next 5 years.(2) This RCTO expands on the DRCOG Regional Transportation Operations Strategy—Action Plan. The RCTO presents a unified direction for transportation systems management and operations based on a holistic view of the whole region. It includes operations objectives and performance measures that can be used in the transportation planning process and specifies the roles and responsibilities of the partners in the collaborative effort. The goals, objectives, initiatives, and performance measures for the RCTO are presented in figure 1.

This figure, in the form of a table, provides a series of objectives, initiatives, and performance measures for a set of three goals for the Denver Regional Council of Governments’ Regional Concept of Transportation Operations. Goal 1 is to provide reliable transportation operations for regional travelers. It has the following objectives for daily operations: (1) increase trip travel time reliability on freeways and arterials for all modes and (2) reduce traveler stops and delay due to signal operations. It has the following objectives for incident management: 1) reduce average incident duration time and 2) reduce the occurrence of secondary incidents. There are four groups of initiatives associated with Goal 1. Daily operations initiatives are the following: (1) continue to coordinate signal timing system management across jurisdictional boundaries, (2) continue to coordinate freeway management, and (3) expand freeway management. The incident management initiative is to establish regional incident management process. The work zones and special conditions initiative is to improve work zone/special event management. Finally, cross-cutting initiatives are the following: (1) coordinate/integrate multimodal traveler information, (2) expand traffic monitoring capabilities and infrastructure, (3) establish shared monitoring between jurisdictions, (4) expand a shared communications network, and (5) establish a shared data warehouse or data management process. Goal 1 has the following performance measures: travel time index, planning time index, transit on-time reliability arterial progression index, average roadway clearance time, average incident clearance time, and number of secondary incidents.
©Denver Regional Council of Governments
VMT = Vehicle miles traveled.
Figure 1. Table. DRCOG RCTO goals, objectives, initiatives, and performance measures.

DRCOG recognized that the implementation of the RCTO must be consistent with and remain flexible to accommodate other Colorado Department of Transportation (CDOT) efforts, including the following:

The RCTO vision is to provide regional transportation management involving coordinated transportation monitoring, response, control functions, and traveler information. Regional partners collect local data and control their local transportation systems while sharing the data through a display system that offers a regional view of traffic operations. This view gives transportation managers the opportunity to cooperate and respond quickly with management strategies that benefit regional travelers. The data are viewed and shared via the CDOT’s CoTrip.org Web site (figure 2).

This screenshot of the Colorado Department of Transportation’s COtrip Web page depicts a map of the I-25 north-south corridor in the area of Centennial, within the City of Boulder. The map shows that traffic in the I-25 corridor is traveling at free-flow speeds of about 50 mi/h throughout the region. Traffic in the E-470 corridor, a major arterial that crosses I-25, is also traveling at free-flow speeds.
©CDOT
Figure 2. Screenshot. CDOT COtrip Web site.

RCTO includes several initiatives. The initiative that focused on data is Initiative C-5: Establish a Shared Data Warehouse and Performance Reporting Process. Ideally, this would involve a central repository where all data could be stored, managed, and accessed as needed. The current strategy involves the development of a virtual data warehouse: each jurisdiction will be responsible for collecting, storing, and managing its data and for transmitting its data in accordance with the Regional Integrated Traveler Information Display Guidelines.(3) CDOT’s Intelligent Transportation System (ITS) group has procured reporting software that is capable of communicating with multiple databases for performance reporting. Table 1 describes the various roles and responsibilities associated with the DRCOG’s RCTO data warehouse and performance reporting initiative.

Table 1. Roles and responsibilities for DRCOG’s RCTO data warehouse and performance reporting initiative.(2)
Organization Roles and Responsibilities
CDOT ITS
  • Maintain its reporting software and support the development of common regional reports.
  • Support the development of automated, real-time reporting for use by regional operators.
CDOT Division of Transportation Development
  • Inventory CDOT’s available data and define a performance data reporting structure.
Each jurisdiction
  • Collect, store, and manage its transportation data.
  • Arrange to transmit real-time traveler information to the Colorado Traffic Management Center.
  • Coordinate with CDOT ITS to allow access to its data by the reporting software.
DRCOG
  • Lead the development of a data management plan that will evaluate regional data management needs and requirements and determine if any improvements are required.

 

Colorado Department of Transportation

This review found that CDOT recognized that highway construction was changing and that the Department’s focus needed to shift from increasing capacity to managing and operating the existing system. Managing and operating the system requires detailed information about real-time current and past performance, as well as predictions of future performance.

Several internal CDOT offices are directly involved with collecting data and maintaining systems to store and analyze the information to support performance measures. CDOT performance data are reported regularly (both internally and externally) in the form of annual performance reports, annual reports, transportation deficit reports, strategic plans, and the FHWA-CDOT Stewardship Agreement. Various indicators are reported on by several internal offices, including regions, bridges, contracts and market analysis, project development, and maintenance. In addition, several related initiatives occurring within CDOT are aimed at improving access to data and information. In addition, CDOT has been collecting and using performance measures data to support long-range planning, policy, and investment analysis since the early 2000s.

In 2011, CDOT developed a performance data business plan. The project was sponsored by the Performance and Policy Analysis Unit within CDOT’s Division of Transportation Development. The plan recommends the following nine core performance measures, many of which are already generated (see figure 3):(4)

 

This screenshot is divided into four quadrants, each depicting a performance area. In the upper left is the Road Quality performance area. There are five tabs in this box, allowing the user to select from among Highways, Pavement, Maintenance, Snow and Ice, and Traffic Services. The Highways tab is selected, and depicts a graph entitled “Highway Grade Overall Roadway Condition.” The years on the graph span 2007 through 2011 and show that the performance area has ranked in the C grade area for the entire period. The text for the box reads “Highways: CDOT maintains 9,146 miles of highways across the State. Colorado motorists drove 27.4 billion miles on these roads in 2011. Learn more.” Two boxes below the text indicate the long-range goal grade is B, and that the actual grade for 2011 is C+. To the right of this box is the Bridges and Tunnels performance area. There are two tabs for this box, one labeled “Bridges” and the other labeled “Tunnels.” The Bridges tab is selected. A graph labeled “Percent of Bridge Deck Area in Good/Fair Condition” shows that from 2007 through 2011, Colorado bridges rank between 93 and 95 percent. Text reads, “Bridges: CDOT maintains 3,447 bridges statewide to keep them safe and in good repair for the traveling public. Learn more.” Two boxes below this text indicate that the long-range goal rating is 95 percent and that the actual rating for 2011 is 94.5 percent. On the lower left is a box for the Mobility performance area. This box contains the following three tabs: CoTrip.org, Transit, and Congestion, with the Congestion tab selected. The table in this box is labeled “Travel Delays in Congested Highway Segments” and shows that between 2007 and 2011, travel delay ranged from about 18 to 16 min. The associated text reads “Congestion: Traffic congestion in Colorado costs drivers $1.35 billion annually in delays and fuel; an average of $913 per person in the Denver area. Learn more.” Two boxes below this text indicate that the long-range goal for travel delay is less than 22 min and that the actual travel delay for 2011 is 17.3 min. Finally, on the lower left is the Fatalities performance area, with the following two tabs: Injuries and Fatalities. The Fatalities tab is selected. It contains a graph depicting the fatalities from 2007 through 2011, with the fatality rate ranging from a high of about 560 to a low of about 450. The associated text reads “Fatalities: A goal of CDOT’s safety program is to increase seat belt usage. 59% of highway automobile fatalities in 2011 were unrestrained vehicle occupants. Learn more.” The two boxes below the text indicate that the 5-year average for fatalities is 492, and the actual fatalities for 2011 were 447
©CDOT
Figure 3. Screenshot. Your CDOT Dollar Web page.(7)

The plan also addresses data management methodologies to support these measures and details best practices and recommendations related to data governance, performance measures, and dashboard development. Each aspect of performance measurement—data quality, data management, analysis tools and methods, dissemination, and use in business process—is important to the ultimate success of the effort. The products were generated with extensive internal stakeholder input. Figure 4 shows the current data flows and the recommended configuration for the future of the CDOT performance measures data system.

This diagram shows how the bottom-up data system configuration and its elements correspond to specific performance measures under both current and future configuration conditions. For the current condition, data flows from traffic data (which correspond to the number of fatalities performance measure), the Pontis data (which correspond to the bridge condition performance measure), and the SAP®, which is fed by a project system module (supporting on-time construction and on-budget construction performance measures) and a Business Planning and Simulation module (supporting roadside condition and snow and ice control performance measures). The dTIMS® element is fed by data from the Automated Data Loading Program element (in support of the pavement condition performance measure). Finally is the Integrated Roadway Information System (IRIS) element (which supports the roadway congestion performance measures). All of these feed data into various spreadsheets and reports, which feed through to the dashboard. For the recommended future configuration, again working from the bottom up, traffic data (which corresponds to the number of fatalities performance measure) flows directly into the dashboard. Elements that flow into the dTIMs® element include Fleet, pavement (corresponding to the pavement performance measure), ITS (corresponding to the pavement and the roadway congestion performance measures), maintenance level of service (corresponding to the roadside condition and snow and ice control performance measures), Pontis (supporting the bridge condition performance measure), and IRIS (supporting the roadway congestion performance measure). The dTIMS® element flows into the SAP® element, which flows directly into the dashboard. Also flowing into the SAP® is the Project System Module, which supports performance measures for on-time construction, on-budget construction, and strategic action item implementation.
©CDOT
Figure 4. Diagram. Recommended CDOT performance measures data system configuration.(4)

This review found that CDOT uses the Online Transportation Information System (OTIS) to share information frequently used for transportation planning and project development, including current and projected traffic volumes, State highway attributes, summary roadway statistics, demographics, and geographic data (see figure 5). It consists of the following individual applications:

 

This screenshot is the home page of the Colorado Department of Transportation’s (CDOT) Online Transportation Information System (OTIS). Across the top left half of the page is the page title and the following descriptive text: “This is the access point to information frequently used for transportation planning and project development. Information is provided on current and projected traffic volumes, state highway attributes, summary roadway statistics, demographics and geographic data.” To the right of this is a box entitled “What’s new?” that indicates MapView’s Print tool has been upgraded. Below this are three rows each containing three links to parts of OTIS and an explanation of what the user can do by accessing those elements. From left to right across the top row are Map View (described as “View and query Colorado transportation data and related images and documents. Create customized maps.”), Highway Data (described as “View highway features, traffic data, photos and documents. Export reports and data.”), and Traffic Data (described as “Traffic Data Explorer displays traffic counts and statistics, including AADT, Truck AADT and VMT.”). From left to right across the second row are Data Catalog (described as “Search for spatial and tabular data, documents, metadata and glossary terms.”), Maps (described as “View and download statewide and regional maps. View, download and order the Official Colorado Travel Map.”), and SL Diagram (described as “SLD (Straight Line Diagram) application displays selected highway features along the road displayed as a straight line and map.”). From left to right across the third row are Windshield (described as “Videolog application that plays highway images as if viewed from the windshield of a vehicle.”), Reports (described as “View and download annual highway, county and city mileage, Truck statistics, Volume/Capacity and Demographic data.”), and Your CDOT $ (described as “Your CDOT Dollar tracks CDOT performance and transportation expenditures.”). Links to other sections of the OTIS web page run across the top and the bottom of the screenshot.
©CDOT
Figure 5. Screenshot. CDOT OTIS.(8)

CDOT also shares data via CoTrip, its Web-based traveler information portal, over eXtensible Markup Language (XML) data feeds. Figure 6 presents the available XML data feeds and metaschema.

This screenshot of the COtrip XML Data Feeds Web page lists the available eXtensible Markup Language (XML) feeds schema and help files. For users to obtain access to the XML data feed itself, the Colorado Department of Transportation requires the user to complete an agreement. XML data feed names available for access include speed for all roads, destinations, road conditions, alerts, cameras, message signs, weather stations, speed segment polylines, speed route polylines, and weather route polylines.
©CDOT
Figure 6. Screenshot. CDOT CoTrip XML data feeds.(9)

Success Factors

The RCTO, which was adopted in August 2012, is still in the implementation stage.

A Regional Transportation Operations Working Group comprising transportation operators, public safety representatives, and other stakeholders in regional transportation operations developed the RCTO. The working group identified goals, objectives, and realistic targets shared by the regional stakeholders, providing a common sense of direction.

The FHWA research team found that the RCTO recognized that all of the transportation systems that operate in the region behaved as a single regional system. To manage the systems collectively, operators and other stakeholders must adopt a shared vision and objectives that have a regional perspective to achieve more effective operations and management practices.

Another positive factor for the RCTO is that there was good coordination among CDOT, DRCOG, local agencies, and consultants.

In addition, CDOT has found that CoTrip and its use of freeway and arterial traffic cameras is a successful tool for public information regarding operations. In conjunction with other operations equipment, the cameras have been a particularly effective traveler information tool, improving travel to and from recreational areas on weekends.

Gaps and Lessons Learned

The Regional Transportation Operations Working Group established that the region lacked much of the data required to measure operations performance. In response, the RCTO specifically emphasizes collecting that data and establishing baselines.

Improved management of regional operations and collaboration in terms of infrastructure, staff, and data sharing is required.

Transportation systems operations data have many uses, including performance metrics for real-time operations and management, traveler information, and performance trends that assist operators and planners. To effectively use transportation data, all users must have access to it.

As part of the Strategic Highway Research Program 2 L05 case study effort, both the purchase of statewide private sector data and the development of a portable monitoring and detection system provided CDOT with useful sources of travel time reliability data and to fill data gaps in rural areas.

The CDOT Performance Data Business Plan: Final Report outlines CDOT’s strategy for improvement as follows:(4)

As part of another study on operations performance measures and planning trends, CDOT identified the following additional gaps:(10)

Applicability to the VDA Framework

The review found that CDOT, DRCOG, and other regional agencies in the Denver, CO, area were developing and implementing systems and processes for combining and sharing data from multi‑agency/multijurisdictional sources to operate and manage the systems collectively.

LAS VEGAS, NEVADA

Effective Practices

The Las Vegas Freeway and Arterial System of Transportation (FAST) program has developed a data integration and archive platform and wants to share operations data broadly to support modeling. The review team found that FAST is one of the first truly integrated ITS organizations in the country, with the Regional Transportation Commission of Southern Nevada (RTC) acting as the official administrator. The Nevada Department of Transportation (NDOT) and the RTC are full-fledged funding partners, contributing to the operations and management of FAST. The Operations Management Committee—comprising representatives from the RTC; Clark County; NDOT; and the cities of Henderson, Las Vegas, and North Las Vegas—set the transportation strategies. The FAST system is composed of two major divisions: the Arterial Management Section and the Freeway Management Section.

FAST monitors and controls traffic. Traffic monitoring is accomplished using video image detection and inductive loop detection with visual verification through closed-circuit television (CCTV) cameras. Traffic signals, ramp meters, dynamic message signs (DMSs), and lane-use-control signals are used for traffic control.

FAST implemented a performance dashboard and data archive to help monitor the system as well as report performance. This Performance Monitoring & Measurement System (PMMS) was developed and written by in-house RTC FAST staff. It consists of a Web-based user interface (i.e., the dashboard) and is open to interested parties and the public to obtain real-time and historical freeway network monitoring and performance information in a wide variety of user-selectable and user-customizable displays. PMMS compiles and processes the enormous storehouse of raw data automatically gathered by NDOT’s freeway ITS, Nevada Highway Patrol dispatchers’ incident-specific data points, and other information generated by FAST’s technicians. Through this data integration, PMMS does the following:

This results in a system that serves the various needs and demands of the public, transportation professionals, researchers, and decisionmakers.

The PMMS consists of the following three critical aspects:

Figure 7 presents the opening screen for the PMMS.

This screenshot of the Nevada Freeway and Arterial System of Transportation Performance Monitoring and Measurement System is divided into two sections on the left and right and contains a traffic camera snapshot (still image) of a free-flowing segment overlaid on the center of the screen. The left section includes a map with a color-coded legend that indicates the speeds on various segments of the roadway. Currently, all traffic is moving at speed with no congestion. The right half of the screen comprises four boxes containing performance metrics charts. The box in the upper left contains a daily peak speed chart. It allows the user to select a.m. or p.m. periods; the a.m. period is selected in the image. Most of the chart is covered by the traffic camera snapshot, but the trend line indicates that speeds are fairly consistent at between 65 and 70 mi/h, with some additional variation in the 15 percent and 85-percent speeds. To the right of this is a time-of-day speed chart. As with the daily peak speed box, this box allows the user to select from AM Peak and PM Peak periods; AM Peak is selected. The moving average speed is consistently at about 65 mi/h, with some variation in the 15th and 85th percentile speed ranges. Below the time of day speeds box, in the lower right corner of the screenshot, is a box for congestion data. In it, the pie chart shows that 95.6 percent of the time there is no congestion. There is light congestion 3.4 percent of the time, moderate congestion 0.8 percent of the time, and heavy congestion 0.2 percent of the time. The box immediately to the left of the congestion box is mostly covered by the traffic camera snapshot but does show one whole speedometer and part of a second speedometer. The whole speedometer indicates p.m. peak speed is 60 mi/h.
©Nevada FAST
NC = No congestion.
LC = Light congestion.
MC = Moderate congestion.
HC = Heavy congestion.
Figure 7. Screenshot. FAST PMMS.(11)

The key elements and uses for the dashboard include the following:

Success Factors

The success of FAST is a direct function of the main objectives that FAST’s software designers used to guide the development of PMMS. These include the following:

Gaps and Lessons Learned

It is a NDOT priority for all corridors in the Las Vegas valley and between Las Vegas and the Nevada/California State line to be equipped with full ITS deployments. RTC/FAST does not have a formal process to identify corridors for operations investments, established methods for selecting operations projects, or the use of data to support performance-based planning. Currently, NDOT relies on program staff knowledge of investment needs, such as knowing what ITS/operations equipment are needed on corridors and identified needs based on Traffic Management Center operator observations. These are gaps NDOT wants to fill over time or is addressing now as it continues to educate and share the information and data.

FAST program staff members have developed an operations project assessment process to assess implemented projects, trends, and incident situations using performance data and have started a quarterly report, now at the anecdotal level, to test different data, performance metrics, and formats. FAST plans to establish targets once the staff completes more data processing to form a system-level benchmark. Program staff members stated that they wanted to be realistic in setting the targets and try to improve those issue areas. FAST also anticipates using performance goals and targets to help determine staffing needs with their next contract with NDOT.

Applicability to the VDA Framework

FAST was one of the first truly integrated ITS organizations in the country, with data integrated and shared among RTC; NDOT; Clark County; the cities of Henderson, Las Vegas, and North Las Vegas; and the Nevada Highway Patrol. The FAST interactive dashboard was developed by in-house staff and is being maintained and improved continuously. It has many of the data elements and data processing capabilities useful in planning for operations and encourages the use of the data for a variety of purposes, such as input and validation for simulation and modeling tools.

SEATTLE, WASHINGTON

Effective Practices

A leader in planning for operations, the Puget Sound Regional Council (PSRC) spends a lot of resources on compiling and analyzing information about the transportation system, demographics, economy, and land use patterns. PSRC generally collates data from other agencies rather than collecting information itself. Many agencies in the region expend significant resources on collecting data, and much of those data contribute in some way to PSRC’s Transportation 2040 Monitoring: Congestion and Mobility Report, which serves as an update of the region’s congestion management process (CMP).(13) PSRC uses the information in this report as a tool for monitoring system performance related to congestion and mobility. The report establishes a regional network of transportation facilities that will be monitored, including 12regional subareas, called “SMART Corridors.” Existing condition data are provided for freight, transit, automobile, bicycle, and pedestrian facilities, as well as for safety and security issues and special needs transportation. It also includes pavement and bridge condition.

The Washington State Department of Transportation (WSDOT) supplies a large amount of information necessary to support the report, and it also collects some traffic count data through the Highway Performance Monitoring System (HPMS) on non-State routes. PSRC collates arterial traffic count data from 20 to 30 member jurisdictions. Regular outreach is performed to see where traffic counts are being collected and to obtain that data. The arterials identified by PSRC’s Regional Transportation Operations Committee are used to define the geographic foundation for the arterial count data obtained from member agencies. PSRC is working to coordinate this with the local data to identify gaps in arterial detection.

Beyond the data in the Transportation 2040 report, PSRC is actively pursuing additional data to support performance monitoring across the region. The council acquires data at the regional level, sub-area level, and SMART corridor level.(13)

To support planning analyses, PSRC integrated its demographic, transportation demand, and land development modeling processes through implementation of a geospatial database data architecture. PSRC transitioned from a GIS based on proprietary spatial data “coverage” layers, in which attributes were accessed from separate databases maintained by several divisions within the agency, to a geospatial database data architecture in which spatial features and their attributes are maintained in indexed table records inside a single relational database management system. The PSRC regional integrated modeling system consists of the following elements:

The intended architecture for PSRC’s geospatial database is shown in figure 8. At present, some of the connections require human intervention for data handoffs. This is primarily true for the UrbanSim model.

This diagram provides an overview of the data architecture for Puget Sound Regional Council’s geospatial database. At the center of the image is the geospatial database. Within the geospatial database, layers are informed by both attributes (via associations and relationships) and topology. Data flowing outward only from the geospatial database go to the User Application, the Model Input/Output Application (which also receives data from the Travel Demand Model), the Maintenance Editor Application, and the Transit Editor Application. Data flowing inward into the geospatial database come from the Existing Transportation Improvement Program (TIP) application via the TIP Database and the Existing Metropolitan Transportation Plan (MTP) User Interface via the MTP database. Data flows bi-directionally between the geospatial database and the Urbansim Land Use Forecasting Model.
©PSRC
Figure 8. Diagram. Data architecture for PSRC’s geospatial database.(14)

Success Factors

Transition to an enterprise geospatial database system has allowed PSRC to achieve the following successful outcomes:(14)

Gaps and Lessons Learned

PSRC has a cooperative data gathering and sharing environment, but the staff realized that they needed to take up the topic more formally with their member agencies. As a result, they created the Interagency Data Group (IDG), which focuses on advancing data sharing among transportation agencies in the region. The purpose of the group is to “coordinate and share, when possible, existing transportation system performance data, measures and indicators” to support improved multimodal and freight mobility analysis in the central Puget Sound region.(15) The IDG maintains the following data-sharing tools:

Applicability to the VDA Framework

PSRC’s enterprise geospatial database provides a foundation for data integration and applications that bring data together in a user-friendly mapping interface. It also serves as a tool for data query, analysis, and editing. Issues and lessons learned regarding data migration, data conversion, legacy data issues, data development, and agencywide data integration are relevant to the development of the framework.

PITTSBURGH, PENNSYLVANIA

Effective Practices

The Southwestern Pennsylvania Commission (SPC) conducts an extensive data collection effort on a regular basis to support the analysis and evaluation of management and operations strategies as part of the CMP. To provide the necessary tools to assist with decisionmaking, SPC uses GIS tools and the ArcGIS™ Flex Viewer application to integrate various types of data into a common database and makes that database available online for use by staff, local governments, and planning partners. The following are examples of successful data integration efforts by SPC to support performance monitoring and data analysis for the CMP:

Success Factors

To promote data sharing, SPC makes all information related to the CMP available on its Web site, including a description of the CMP, description of the data collection process and performance measure calculation methods, current and archived data, maps for each CMP corridor, case studies of implemented strategies, and copies of the old paper-based CMP reports. As shown in figure 9, users can select individual CMP corridors to view a detailed map and performance measure results for travel time, speed, and delay. Links are provided to view congestion strategy evaluations for the corridor, archived data and maps from previous CMP reports, land use maps, historical crash information, typical park-and-ride utilization, PennDOT highway video log data, and private sector traffic data for the corridor.

SPC also maintains a data library where users can download data, maps, and other information pertaining to the southwestern Pennsylvania region. Specialized and custom data are also available through the online data store.

Gaps and Lessons Learned

A recent case study documented the following gaps and lessons learned for SPC:(19)

This screenshot is a Web page entitled “Congestion Management Corridors: Alleghany County; Corridor 2: US 19/McKnight Road.” The Web page contains a navigation menu on the left side, and the remainder of page contains a map, a legend for the map, part of a bar chart, contact information for the Southwest Pennsylvania Commission (SPC), and information related to the Download Center. The navigation menu on the left contains the following choices: “Transportation Home, Ops & Safety, Congestion Mgt, Glossary, System Definitions, Performance Measures, Performance Monitoring Data, Congestion Management Strategies, Strategy Implementation & Monitoring Effectiveness, Reports.” Also on the left-hand side of the Web page is the Congestion Management Contact: Dough Smith (412) 391-5590 x327. SPC, Two Chatham Center, Suite 500, 112 Washington Place, Pittsburgh, PA, 15219, (412) 391-5590 (P), (412) 391-9160 (F), comments@spcregion.org. There are also links to Directions and Translate Page. The map shows the US 19 corridor and several other major roads in the area. Franklin Park, West View, Emsworth, Avalon, Wildwood, Glenshaw, and Gibsonia are prominent area names on the map. The US 19 corridor is highlighted in orange. Numerous markers (or nodes) are shown along this corridor. The node names are listed in the legend on the right side of the webpage. Node A is Shenot Road. Node B is Wallace Road (SR 4069). Node C is SR 910 Underpass. Node D is Richard Road (SR 4053). Node E is Pine Creek Rd (SR 4086). Node F is Ingomar Rd Underpass. Node G is Cumberland Rd (SR 4024). Node H is Perrymont Rd (SR 4024). Node I is Johanna Drive. Node J is Siebert Road (SR 4016). Node K is Babcock Blvd Underpass. Node L is Nelson Run Road. Node M is Evergreen Rd (SR 4009). Below the map is a partially visible bar graph. The graph is titled AM Peak Hour Delay Locations. Visible y-axis labels include Nelson Run Rd to Evergreen Road, Babcock Blvd to Nelson Run Rd, Siebert Rd to Babcock Rd, and Perrymount Rd to Johanna Dr. Bars on the graph extend out from the y-axis toward the right. The x-axis is not visible, so units are unknown. The longest bars are Perrymount Rd to Johanna Dr., closely followed by Nelson Run Rd to Evergreen Road. Information about the Download Center is displayed in the lower right side of the page. Archived Corridor 2 PDF data are downloadable for fall 2009, spring 2007, and spring 2003. Land use maps available for download include the Shenot Road to Cumberland Road PDF and the Cumberland Road to I-279 interchange 11 PDF. Crash Analysis is available for download and includes Corridor 2 2007–2009 PDF and Corridor 2 2005–2007 PDF. There is also a link to Typical Park-n-Ride Utilization, Fall 2009.
©SPC Congestion Management Corridors Web site, http://www.spcregion.org/trans_cong_corr2.shtml
Figure 9. Screenshot. SPC Congestion Management Corridor Web site.(20)

Applicability to the VDA Framework

SPC has successfully integrated and shared various types of data to support its CMP and planning for operations efforts. Its Web-based system for organizing and sharing data has been very useful in engaging agency partners in the transportation planning process. The examples of SPC’s data integration efforts and their CMP Web-based content provide ideas for functionality that could be incorporated into the framework.

SAN DIEGO, CALIFORNIA

Effective Practices

The San Diego Association of Governments (SANDAG) has increasingly used archived data for planning purposes and worked closely with the California Department of Transportation (Caltrans), local cities, and the transit agencies. It has excellent models and arterial information because of integrated corridor management (ICM) and corridor system management plan projects. Much of the planning for operations and the associated data and analysis tools/methods are described in the companion report, Data Use in Planning for Operations: State of the Practice Review, and consequently that information is not repeated in detail here.(21)

The region has one model that all of the agencies use. SANDAG supports the State, county, cities, and other transportation agencies to add detail based on needs. Direct connections to the model are available to Caltrans so the agency can login remotely and run the model for its purposes. Cities and other interested parties can contract for analyses through the SANDAG service bureau. This provides consistency in the numbers, analysis approach, and results.

The regional arterial management system also uses the same software to enable multi-agency signal coordination. Using the same platform supports the entire region. If new TMSs come online, regional ITS architecture and standards are followed to ensure consistency and coordination.

The SANDAG Demographics and Other Data Web site allows interested parties to obtain a tremendous quantity of demographic, economic, land use, transportation, and criminal justice information about the San Diego region.(22) One element is the average daily traffic volumes count data. Each year, local jurisdictions and Caltrans collect traffic count data on significant roadways, State freeways, and highways, and SANDAG compiles this information to present average weekday traffic counts as two-way, 24-h volumes.

The San Diego region uses the Intermodal Transportation Management System (IMTMS) as a hub to tie together the management systems of the individual travel modes and to share data and functional capabilities. For example, IMTMS allows cities to share event management information as well as traffic video and camera control. The IMTMS network refers both to the communications network for sharing information and functional services, as well as the interfaces, equipment, and software. Data adhere to regionally adopted XML data standards for consistency and sharing purposes. The IMTMS network also provides data for SANDAG’s 511system, including freeway speeds, lane closures, transit schedules, bus arrival times, and regional traffic and transit incidents. The IMTMS has interfaces to various systems (i.e., Advanced Traffic Management System (ATMS), Regional Transit Management System, California Highway Patrol (CHP), etc.) and is the primary means of sharing data with agencies in the region. Each participating data provider in IMTMS has either a direct or indirect connection to the IMTMS network via an agency data server (ADS). The ADS takes legacy server data and converts it to a standardized XML format before passing it on to a set of Web servers that provide a platform to disseminate intermodal data via either HTML map pages for browser display or as a direct XML data stream for third-party applications.

The IMTMS is being used for the region’s ICM effort. Figure 10 presents the Integrated Corridor Management System (ICMS), its subsystems, and the systems to which it will be connected. Table 2 lists existing or planned systems and their owning agencies. The decision support system subsystem will integrate event management, multi-agency collaboration tools, multimodal response plans, and impact assessment (modeling) into the existing IMTMS network.

This web diagram shows a large central circle surrounded by smaller circles. Arrows point to and from the central circle and the smaller circles. Some arrows are dashed and some are solid. The arrows and circles are color coded. A legend describes the modal color scheme as follows: green is Freeway, yellow is Arterial, purple is Transit, blue is Public Safety, and orange is ATIS/511. The large circle is labeled “ICMS.” Below this are three lines of text. The first line is blue and reads as follows: “System Services.” The second line is green and reads as follows: “IMTMS.” The third line is red and reads as follows: “DSS.” The small circle directly north of the large circle is green and is labeled “ATMS 2005.” A green solid double-headed arrow connects this circle to the large central circle. In addition, a green dashed single-direction arrow points out of the large central circle and into the small circle. The second small circle, which is clockwise of the first, is green and is labeled “MLCS.” The text beside it reads as follows: “Managed Lanes Control System (currently implemented as an 8-mile Reversible Lane Control system (RLCS) until managed lanes construction is completed).” A green solid double-headed arrow connects this circle to the large central circle. In addition, a green dashed single-direction arrow points out of the small circle and into the large central circle. The third small circle clockwise is green and is labeled “CPS.” A green solid double-headed arrow connects this circle to the large central circle. The fourth circle clockwise is blue and is labeled “REMS.” The text beside it reads as follows: “Regional Event Management System—public safety CAD systems (currently CHP but others possible over time).” A blue solid double-headed arrow connects this circle to the large central circle. In addition, a blue dashed single-direction arrow points out of the large central circle and into the small circle. The fifth circle is blue and is labeled: “WebEOC.” The text beside it reads as follows: “County EOC maintains WebEOC as regional information exchange.” A blue solid double-headed arrow connects this circle and the large central circle. The sixth circle is blue and is labeled “3C’s.” The text beside it reads as follows: “Regional High-Bandwidth uWave Network.” A blue solid single-direction arrow points out of the large central circle and into the small circle. The seventh circle is purple and is labeled “RTMS.” A purple solid single-direction arrow points out of this circle and into the large central circle. In addition, a purple dashed single-direction arrow points out of the small circle and into the large central circle. The eighth circle is purple and is labeled “Sprinter TCS.” A purple solid double-headed arrow connects this circle to the large central circle. The ninth circle is purple and is labeled “SPS.” A purple solid double-headed arrow connects this circle to the large central circle. The 10th circle is orange and is labeled “ATIMS (511).” An orange solid double-headed arrow connects this circle to the large central circle. In addition, an orange dashed single-direction arrow points out of the large central circle and into the small circle. The 11th circle is yellow and is labeled “RAMS.” A yellow solid double-headed arrow connects this circle to the large central circle. In addition, a yellow dashed single-direction arrow points out of the center circle and into the small circle. The 12th position does not have a circle. In its place is text that reads as follows: “RIWS (HTML Pages + Browser) XML Data (3rd Party Applications).” A grey solid single-direction arrow points out of the center circle and into the loose text. The 13th circle is green and is labeled “PeMS.” The text beside it reads as follows: “3-PeMS eventually include aPeMS & tPeMS.” A green solid double-headed arrow connects this circle to the large central circle. The 14th circle is green and is labeled “LCS.” A green solid single-direction arrow points out of this circle and into the large central circle. The 15th and final circle is green and is labeled “RMIS.” A green dashed single-direction arrow points out of the large central circle and into this small circle.
©San Diego Pioneer Site Team
Figure 10. Diagram. ICMS context diagram.

 

Table 2. ICMS interfacing systems and owner agencies.(23)
Existing or Planned System Owning Agency Owning Agency
Advanced Transportation Management System (ATMS 2005) Caltrans District 11
Reversible [Managed] Lanes Control System (R[M]LCS) Caltrans District 11 (RLCS becomes MLCS)
Ramp Meter Information System Caltrans District 11
Lane Closure System Caltrans District 11
Regional Transit Management System SANDAG (MTS and NCTD are system operators)
Modeling System (TransModeler) SANDAG
Regional Arterial Management System SANDAG (local agencies are system operators)
Regional Event Management System CHP ( in future, other public safety agencies will be included)
Multi-Agency Collaboration (3Cs—Command, Control, and Communications Network) Regional Technology Partnership
Advanced Transportation Information Management System (or 511) SANDAG
Smart Parking System SANDAG (Planned)
Congestion Pricing System SANDAG (FasTrak®)

Success Factors

One of SANDAG’s key success factors in its several data sharing and integration efforts is its ability to work successfully in multi-institutional and multimodal partnerships and other cooperative efforts (e.g., Caltrans District 11, local cities, and transit agencies). These relationships foster trust, furthering the achievement of common goals and objectives as well as supporting the selection and development of transportation improvements that enhance system-wide performance. These relationships have also reduced the need to have formal agreements for many efforts.

SANDAG has had minimal technical data fusion issues because of the standards and processes established for the region.

Gaps and Lessons Learned

One challenge for the ICM effort was the lack of readily available arterial data for calibrating the microsimulation models. Data were usually at a daily level, but were needed in 15-min increments. In addition, the level of arterial coverage available varied. Additional detection equipment filled in the gaps, which will be incorporated into Caltrans’ Performance Measurement System (PeMS) so both it and processed and archived data will be available for other uses in the future. (PeMS is described in more detail in chapter 3 of this report.) SANDAG is also putting together a regional arterial detector plan to fill other arterial coverage gaps in the region. Another consideration explored by SANDAG was the use of third-party data. At the time of this review, no providers had been identified to meet SANDAG’s specific granularity needs for arterial operations and performance monitoring.

With respect to long-range planning, analysis is constrained by the need to be consistent with the capabilities of the regional travel demand model. SANDAG recognizes the challenges of demonstrating operations benefits at a regional level. It is developing an activity-based model in an effort to be able to better assess Transportation system management strategies in the future (e.g., signal coordination, active transportation management, automated vehicle location (AVL), etc.). SANDAG also recognizes the need to change its approach, as a planning agency, to how it uses traffic data to justify investments.

While a wealth of data are available for monitoring and reporting system performance from these ITSs, the results still require human review to determine whether the performance assessment is an accurate reflection of what the public experiences.

Applicability to the VDA Framework

Both SANDAG’s and its regional partner agencies’ prior and ongoing experiences with using and sharing data and systems could provide valuable insight into the VDA Framework from the perspective of multi-agency and jurisdictional data sharing, use of data for supporting model validation and calibration, and use of data for state of the region reporting, as well as aid in meeting standards, consistency, and collaboration goals.

SAN FRANCISCO BAY AREA, CALIFORNIA

Effective Practices

The San Francisco Bay Area Metropolitan Transportation Commission (MTC) has had considerable success in using data for planning for operations activities. Similar to SANDAG, MTC does the following:

The Data Use in Planning for Operations State of the Practice report describes several of the tools and methods for data collection and analysis performed by MTC in support of its planning for operations activities, so those are described here.(21) In addition, PeMS is discussed in more detail in chapter 3 of this report. This best practices discussion for MTC focuses on the data sharing, integration, and practices for the MTC data portal and 511.

MTC is the transportation planning, coordinating, and financing agency for the nine-county SanFrancisco Bay area. The Modeling and GIS team created a data portal on its Web site for local agencies and the public. The various data files, maps, and other graphics are public records, and MTC welcomes their use by the planning community throughout the region (unless restricted by licensing or nondisclosure agreements—site registration may be required for downloading). The following is a list of services provided by the data portal:

511 is a one-stop telephone and Web source for Bay Area traffic, transit, rideshare, and bicycling information (see figure 11). Led by MTC, the CHP, and Caltrans, 511 is managed by a partnership of nearly 70 public agencies with a mission to provide comprehensive, accurate, reliable, and useful multimodal travel information to meet the needs of Bay Area travelers. 511data are shared via several data feeds (see figure 12). Extensive documentation is available for users regarding the data feed. (See figure 13 for an example of a service architecture from the 511 Traffic Open Messaging Service Overview document.(24)) The 511 traffic data feed provides incidents, speed, and travel time data for individual links on the highways, freeways, and expressways in the region for free. Registered Internet service providers receive XML-formatted data via the Java Message Service over the Internet. Access to the data feed requires completion of the 511 Traffic Data Disseminator Registration Agreement.(25)

This screenshot of the home screen of the 511 SF Bay Area Web site is divided vertically into thirds. The top of the left panel contains two tabs allowing users to use a transit trip planner and real-time departures tool. Below it is a slider that features the FasTrak program. In the central panel is a map containing current traffic conditions. With the exception of a few indicated segments, there is no congestion on the system. In the upper part of the right panel is a box containing announcements that allows users to choose the type of announcements. They can select from New 511 Traffic, developer resources, a 511 transit app, and a link to enroll in the FasTrak program. In the lower part of the right panel are navigation options that allow a user to access real-time transit departures, get driving times, plan a transit trip, go mobile, join a carpool, or get real-time parking information. Cut off across the bottom of the screen capture is a box containing regional transportation tweets and a box containing a carpool quick match tool.
Source: SF Bay 511, ©Metropolitan Transportation Commission
Figure 11. Screenshot. SF Bay Area 511 Web site.(26)

 

This screenshot of the 511.org section of the 511 SF Bay Web page containing Developer Resources, which include the 511 traffic data feed, 511 transit data feed, the custom 511 RideMatch service, the 511 driving times API, and the 511 real-time transit departures API (beta). There is also a Google Groups™ signup area in which users may enter their email address to subscribe to the 511 SF Bay Developer Resource group. Two additional boxes contain links to and information about 511 mobile apps and 511 driving times. Across the bottom of the screen are additional site links to other tools and information.
Source: SF Bay 511, ©Metropolitan Transportation Commission
Figure 12. Screenshot. 511 SF Bay available data feeds.(27)

 

This two-part diagram depicts the interrelationships of the TravInfo open messaging service architecture followed by the link status information architecture. Working from left to right, other data sources and Caltrans interface data flow via link data to the ADLF Server. Link data flows down to the Data Fusion Server and the Data Dissemination Server. The Data Fusion Server receives addition information from the Enhanced Data Fusion Workstation and flows incident data down to the Data Dissemination Server, which proceeds to flow both incident data and link data down into the JMS Server. This disseminates data via JMS/XML connections to ISPs. Next, flowing from right to left, the element link or node contains links and nodes, which flow traffic information data out to the traffic element.
©SAIC
Figure 13. Diagram. TravInfo open messaging service architecture and link status information.(24)

Success Factors

MTC has been very successful in using data in planning for operations efforts, integrating data from a variety of sources, and sharing the data it has available. As described previous and in the Data Use in Planning for Operations State of the Practice report, success factors include the following:(21)

Gaps and Lessons Learned

As mentioned in the Data Use in Planning for Operations State of the Practice report, some data barriers and gaps include the following:(21)

A gap identified by the operations staff at MTC was the desire to use archived data from a variety of sources to develop their annual State of the System Report. They have been exploring the use of archived data for regional congestion monitoring purposes instead of the floating car method because it is costly and usually provides only a few days’ worth of data. MTC recently decided to obtain third-party travel time and speed data to help fill gaps in the system to support its planning, operations, management, and reporting needs. It has also been promoting improvements to PeMS by enhancing its usability, data extraction, capability to analyze and quantify nonrecurrent congestion, and inclusion of arterial data. A challenge that it has had in using the PeMS data in particular is that the data are not in a usable format and are not processed to filter bad data (e.g., malfunctioning detectors, outliers, etc.). Unlike SANDAG, MTC does not have a data hub in place for compiling and processing the various traffic and asset data available.

Applicability to the VDA Framework

MTC’s prior and ongoing experiences could provide a variety of ideas for viable planning for operations activities at a regional level to test the VDA Framework.

 

 

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