U.S. Department of Transportation
Federal Highway Administration
1200 New Jersey Avenue, SE
Washington, DC 20590
202-366-4000
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 |
|
 |
| Publication Number: FHWA-HRT-15-071 Date: January 2016 |
Publication Number: FHWA-HRT-15-071 Date: January 2016 |
RTC of Southern Nevada, which serves as the MPO and transit authority for Southern Nevada, collects a wide variety of transportation operations data from freeways, arterials, and transit facilities for use in planning and operations activities. RTC records transit boardings, ridership, and operational efficiency but does not maintain regular route-specific transit travel patterns. The Freeway and Arterial System of Transportation (FAST) is a regional ITS organization administered by RTC and funded by RTC and the Nevada Department of Transportation (NDOT). FAST aggregates large quantities of data for both planning as well as distribution to travelers. Using side-fire radar, inductive loops, closed-circuit televisions (CCTVs) and video image detection, FAST collects the following data elements:
RTC currently uses the data collected by FAST to generate a graphic that depicts relative congestion (as volume-to-capacity ratio) to automatically identify the most congested corridors to define systems/networks of interest, which is depicted as step 3 in the congestion management process (CMP) framework in figure 5.(17) Although FAST has no formal process to identify corridors for operations investment, relying instead on operators observations, the next Long Range Transportation Plan and Transportation Improvement Plan updates will incorporate congestion reliability data in investment decisions. Furthermore, FAST has begun tracking performance using performance-based goals and targets and recently completed its first benefit-cost analysis of operations and mobility projects.
View long description of this figure
FAST collects travel time, speed, VMT, incident, delay, classification, volume, and occupancy data, which are streamed to the Nevada FAST Web site, where an interactive dashboard displays live and archived conditions for use by travelers, as shown in figure 6.(18)
View long description of this figure
To supplement the volume-to-capacity ratio, RTC plans to incorporate analytical tools such as travel forecast models, signal optimization tools, and simulation models to quantify and validate the benefits of the proposed projects.
The CMP prioritizes improvements that address congestion at a list of sites that have been determined to be in need of improvement based on the following four components of congestion:(19)
Current evaluation of strategies in planning does not include quantitative considerations of collected data but rather a qualitative hierarchy to prioritize demand reduction and mode shifts over highway capacity expansion.
In the 2011–2014 Transportation Improvement Report, RTC recommends periodic assessment of implemented strategies but has not yet completed these evaluations. RTC has enlisted contractors to conduct several before-and-after studies to examine the effectiveness of transit, arterial, and parking projects. Many of these projects are still being implemented, and data collection has not yet concluded.(20,21)
H-GAC houses the MPO for an eight-county region surrounding Houston and serves more than 6 million people across 7,800 mi2.(22) H-GAC has access to various sources of real-time and archived operations data and is updating its CMP to incorporate this data to support better long-term planning activities.
H-GAC is advancing in its use of operational performance data available in the region. It has access to fairly extensive freeway data collected and used in real-time by TranStar, a multi-agency coalition and traffic management center (TMC) focused on the management and operation of the region’s transportation system. The region has a network of toll-tag readers that collect speed and travel time data on freeways and toll roads. The Texas Department of Transportation (TxDOT) is in the process of replacing the toll-tag readers on its freeways with Bluetooth® readers because they provide comparable data collection capabilities at a significantly lower cost.
H-GAC also has access to freeway incident management information from TxDOT’s Regional Incident Management System (RIMS). RIMS is the central database where data for the majority of freeway incidents within Houston and surrounding areas are recorded. The information gathered by TxDOT on incidents includes incident detection, verification, response time, and clearance time.
Like most MPOs, H-GAC has faced more challenges in measuring arterial performance than freeway performance. H-GAC purchased 2009 travel time and speed data from a private, third-party data provider to help assess system performance. H-GAC has been working with TTI to conflate the third-party data with H-GAC’s modeling network, which would assist in validation of travel speed for the model. This model will allow for a before-and-after review of how specific projects may have affected travel times and speeds.
Efforts in the region have been underway to implement Bluetooth® technology to provide continual real-time traffic data on its roadway network not just to capture and use traffic information but also to control the quality of the data. Houston has implemented Bluetooth® readers on a 60-mi2 test section on its arterial system to capture arterial speeds and has been approved for funding through H-GAC to fund deployment of additional Bluetooth® readers across the remainder of its arterial network. Several other communities have also implemented Bluetooth® readers on arterials in the region but not to the extent of the City of Houston’s proposed network. Once fully operational, H-GAC and the city will be able to capture real-time traffic conditions on arterials. In addition, the information from the system will benefit real-time operations by helping operators identify system slowdowns, determine causes, and dispatch resources to address the problem.
H-GAC has access to traffic count data primarily from TxDOT, which usually collects 5-year saturation counts with tubes. TxDOT is the primary count source for H-GAC’s travel demand model, and the data ares provided to H-GAC at no cost. However, a location’s count is only registered for a single 24-h period, and any construction, incident, or counter malfunction will not result in a second attempt to collect a count. It would be cost-prohibitive for the region to conduct a continual, comprehensive count program with a road network as extensive as the Houston-Galveston area, which has more than 50,000 network links.
Much of the travel time data collected on the performance of the transportation system are used for real-time management and operation of the system by TranStar at the TMC and for providing traveler information on the public Web site. TTI collects the toll-tag reader and Bluetooth® data from passing vehicles and calculates the freeway speeds and travel times. The results are fed into a color-coded speed map (figure 7) that displays system performance. This map is used for real-time monitoring and incident management at the Houston TranStar control room and for public dissemination on the Houston TranStar Web site.(23)
View long description of this figure
These data are archived and, to a lesser extent, used for monitoring performance measures and objectives. H-GAC is reviewing options for conducting performance reporting using these datasets. Houston TranStar produces an annual report that documents measures of annual average incident clearance time, changes in measured congestion, and traveler use of information.
The use of data in evaluating M&O strategies in the planning process has been limited thus far. In early 2012, H-GAC made its first attempt to quantitatively evaluate some of its ITS project submissions to the Transportation Improvement Program (TIP) through IDAS. H-GAC worked to evaluate several ITS components in IDAS, including traffic signal system improvements, CCTVs, and dynamic message board installations, among others, but had difficulty with the process and initially obtained some questionable results.
The modeling group at H-GAC has used the data from TranStar (i.e., toll-tag readers and other detectors) to validate the speed data in its freeway models and has used arterial data from a third-party vendor to validate its arterial models.
H-GAC will be looking at private third-party data to identify before-and-after impacts of project implementations. While some pertinent information may be gathered from this exercise, results could be affected by variables that may not be project-related. For example, the economic downturn and recovery from 2009–2012 may influence both safety and mobility metrics. H-GAC is planning for better post-implementation evaluation as part of its current CMP modifications.
H-GAC recently used a contractor to perform a benefit-cost analysis of the region’s incident management program investments, and the results showed a high benefit-cost ratio. H-GAC intends to use these results to inform decisionmakers in the region about investments in TIM.
The incident management data housed in TxDOT’s RIMS is used to monitor the effectiveness of incident management activities on the system. Those data are reviewed on a monthly basis to analyze incident management activities on the freeways. Current efforts to enhance incident management activities in the region involve increased use of this database to monitor performance and provide information to partners and policymakers.
The Houston TranStar partnership conducts surveys on and tracks usage of the TranStar traveler information Web site. Surveys show that the Web site has a high benefit to the public, with approximately half a million viewers each month. Understanding travelers’ use of information enables TranStar to tailor its programs more effectively.
H-GAC has worked with operations data to learn about how the timing of evacuation decisions affects traffic patterns. Evacuations are difficult to model because destinations are often unknown and may change en route. In addition, other factors can play into an evacuation, such as the impact Hurricane Katrina had on Hurricane Rita’s evacuation over a month later. Archived operations data was used to identify issues related to the both the Hurricane Rita and Hurricane Ike evacuations. They detected a dual wave of evacuation traffic that could be better addressed with additional coordination between evacuation announcements and employer early dismissals.
H-GAC has encountered several barriers when trying to use data for planning for operations. Travel time and speed data are predominantly generated by toll-tag readers, which are too expensive to implement throughout their region of about 8,000 mi2. Regional operators, including TxDOT and various communities, are investigating or implementing Bluetooth® readers to capture travel data, which are much more affordable. In the meantime, H‑GAC is using third-party data to look at system performance, but the data are proprietary, and there are concerns that it may not accurately depict normal traffic. Furthermore, traffic count data provided by 5-year saturation counts conducted by TxDOT are not accurate enough for operations and planning activities.
MTC is the transportation planning, coordinating, and financing agency for the nine-county San Francisco Bay Area. It has extensive experience in the evaluation of management and operation strategies in planning. At the long-range planning level, MTC has evaluated M&O strategies such as arterial signal coordination, ramp metering, congestion pricing, and express lanes using the region’s travel demand model and other post-processing methods. Since 2007, MTC’s Freeway Performance Initiative (FPI) has been involved with the development of corridor studies that will be used to develop a roadmap for the selection of the best projects and operational strategies in the region based on performance and cost-effectiveness. With respect to performance monitoring, MTC has been evaluating the potential to use the Caltrans PeMS to support their state-of-the-system reporting.(24) In addition, MTC supported the development of the Traffic Operations System Equipment Management System (TEMS), a central database for inventory and status information for the San Francisco Bay Area ITS and traffic operations devices operated and maintained by Caltrans. The following sections describe these examples with a focus on the data used for these efforts.
MTC has multiple sources of transportation performance data for monitoring the operational performance of the area’s highways. These sources include the following:
PeMS is an Internet-based data archive system that collects historical and real-time traffic data in California to compute freeway performance measures. It collects traffic data from freeway detectors such as counts and occupancies and can automatically compute speeds, VMT, vehicle hours traveled (VHT), delay, travel time index, and productivity for every detector location every 5 min. PeMS also aggregates several of the performance measures in time and space. Figure 8 presents a screenshot of the PeMS online system. Users can retrieve data using the standard query forms within the system.(25)
View long description of this figure
The MTC 511 system (figure 9) is a one-stop source for traffic, transit, ridesharing, and parking and bicycling data for the nine-county San Francisco Bay Area.(26) The traffic section ingests real-time traffic speed and travel time information on highways and major arterials from a private sector data provider. This information is checked against several quality filters that help ensure the data are as accurate as possible before it is used by the 511 system to provide traveler information to Bay Area travelers. In addition, Traveler Information Center operators input into the 511 Traffic/Transit Reporting and Management System information on incidents, planned closures, and event data coming from several external data sources, including Caltrans, the California Highway Patrol, and the media. The 511 system then provides travel time and incident information for user-selected routes based on real-time and historical information. The system reports both typical travel time and current travel time as well as roadway incidents and closures to aid travelers in determining whether they should consider route, departure time, or mode changes. The transit section of 511 provides information about routes, schedules, real-time departures, and transit trip planning. Users can obtain complete information, including maps for a multimodal trip spanning multiple agencies. The rideshare section of 511 provides users with the ability to save time and money by accessing information on traditional, dynamic, and casual ridesharing. The bicycling section of the 511 system provides information about bike maps and biking infrastructure as well as bike sharing facilities. The parking section of 511 provides static and real-time data (when available) about parking at train stations, park-and-ride lots, and public and private parking lots and garages.
View long description of this figure
The HICOMP report has been produced by Caltrans since 1987. The HICOMP report is produced annually and contains a compilation of measured congestion data reflecting conditions on urban freeways in California. Over several years, MTC produced a state-of-the-system report and shared this data with Caltrans for the HICOMP report. The data was collected by driving specially equipped vehicles along congested freeway segments during peak travel periods. Caltrans also performs floating car runs at least twice per year on freeway segments with HOV lanes. The HICOMP report includes maps illustrating the congested locations, the duration of congestion, and the hours of delay for each congested segment.
TASAS is a traffic records system containing an accident database linked to a highway database. The highway database contains description elements of highway segments, intersections and ramps, access control, traffic volumes, and other data. TASAS contains specific data for accidents on State highways.
MTC purchases private sector speed data collected over a large geographical area for most roadways on a regional scale. The private sector data provider aggregates traffic from GPS-enabled vehicles, mobile devices, traditional road sensors, and many other sources.
Regional Transportation Plan
MTC and the Association of Bay Area Governments adopted Plan Bay Area in 2013. Plan Bay Area integrates a new sustainable communities strategy element into the Regional Transportation Plan, resulting in an integrated transportation, land use, and housing plan targeted to reduce greenhouse gas emissions for cars and light-duty trucks.(27) MTC relied on a performance-based approach, focusing on measurable outcomes to help understand how potential transportation investments could advance the region’s goals. Regionally significant transportation projects and scenarios were evaluated based on their level of support for adopted targets and based on their cost-effectiveness (benefit-cost assessment). The adopted performance targets for Plan Bay Area predominantly focused on the sustainability goals of the region. Unlike previous regional transportation plans, for the first time the majority of the performance targets dealt with issues beyond the scope of traditional transportation planning, incorporating issues such as public health impacts, the potential for greenfield growth, and economic development.
Travel Model One, the region’s activity-based travel demand model, was leveraged to evaluate the performance of both projects and scenarios. For all non-committed projects (i.e., projects either lacking full funding or environmental clearance), two assessments were performed to determine their usefulness and efficiency in achieving the plan’s objectives. First, each transportation project was qualitatively evaluated based on its level of support for the adopted targets. Projects could receive an overall targets score ranging from +10 (strongly supporting all targets) to -10 (strongly adversely affecting all targets). Second, all major capacity-increasing transportation projects with costs exceeding $50 million and/or with regional impacts were evaluated using a quantitative model-based benefit-cost analysis. This process went beyond the adopted performance targets to consider as many quantifiable benefits as possible, seeking to determine which projects are most cost-effective in providing benefits to users and society.(28)
The qualitative criteria for each of the 10 performance targets included the following:
All projects were assessed using Travel Model One, creating a level playing field across all of the region’s analyzed projects. A no-build model run was conducted to determine the baseline future year conditions (e.g., total regional travel time, tons of airborne emissions, non-recurring delay, collisions, etc.). After changing the baseline conditions to represent project-related improvements, the model was then run again to analyze with-project future year conditions.
Table 2 lists the project benefits and costs that were quantified and monetized in the project-level benefit-cost assessment. Benefits were based on year 2035 travel model output for a typical weekday and, therefore, had to be multiplied by an annualization factor to determine the annual benefits. Capital costs were annualized based on the expected useful life of the corresponding transportation asset type and then combined with a net annual operating and maintenance cost.
| Project Benefits | Project Costs |
|---|---|
|
|
By combining the targets assessment and benefit-cost assessment, MTC staff were able to inform policymakers about the merits and limitations of projects. The results of the project-level performance assessment, including some operations and management strategies, are summarized in figure 10 and figure 11.(28) Each bubble chart shows the benefit-cost ratio (on the vertical axis) and the targets score (on the horizontal axis).
View long description of this figure
View long description of this figure
The following themes emerged from the assessment related to M&O strategies:
FPI
Because of limited opportunities for highway expansion, MTC initiated the FPI to try to maximize the capacity of the existing roadways through the use of M&O strategies based on performance and cost effectiveness. Several corridors were analyzed as part of the FPI program, each including a quantitative assessment of existing freeway conditions and development and assessment of short-term and long-term congestion relief strategies and projects. The results from these FPI corridor studies have been incorporated into Caltrans’ Corridor System Management Plans (CSMPs). The California Transportation Commission requires that all corridors with a project funded through the Corridor Mobility Improvement Account have a CSMP that is developed with regional and local partners. The CSMP recommends how the congestion reduction gains from the Corridor Mobility Improvement Account projects will be maintained with supporting system management strategies.
Existing Conditions Analysis: The goal of the existing conditions analysis was to perform a comprehensive assessment of the existing traffic performance in a corridor, including the following:
New and archived data were used for assessing the traffic performance in the existing conditions analyses on the FPI corridors to varying extents, including data from the following:
Figure 12 through figure 14 present some of the performance measures generated for the FPI existing conditions analysis using archived traffic data.(29)
View long description of this figure
View long description of this figure
View long description of this figure
Analysis of Strategies: FPI corridor studies have involved several types of analysis tools, including sketch planning tools (e.g., IDAS, California Life-Cycle Benefit/Cost Analysis Model, postprocessing), travel demand modeling, macrosimulation, and microsimulation (e.g., Paramics). Models were validated using speeds, queue lengths, and travel times identified during the existing conditions analysis and traffic count data from PeMS or other sources. Prior to identifying potential strategies for the corridor, the cause(s) of the existing congestion were assessed. This aids in developing strategies that will actually address the congestion problems. The performance measures generated and the project prioritization methods used as part of the analysis varied but generally included VHT, VMT, speed, travel time, total delay, miles of congestion, reliability, and benefit-cost. As an example, recommended strategies for the I-580 East FPI corridor in Alameda County, CA, studies included the following:
TEMS
Cost-effective management of transportation infrastructure is a critical challenge facing transportation agencies, particularly for traffic control devices, ITS, and operations equipment, which are now relied upon heavily for real-time traffic and incident management. Compounding this challenge for MTC as the entity responsible for traveler information and the freeway service patrol is Caltrans’ responsibility for deploying, operating, and maintaining the majority of the ITS equipment and systems. As such, MTC and Caltrans District 4 initiated the development of TEMS, a central database and equipment management system for San Francisco Bay Area ITS and traffic operations devices. TEMS consolidated the several existing traffic operations system databases and spreadsheets and ensures the following:
It includes a database and mapping of inventory and status information for the ITS and traffic operations equipment deployed and planned, including changeable message signs, ramp meters, mainline meters, detector stations, CCTV, extinguishable message signs, highway advisory radio, control cabinets, and associated communications. Figure 15 presents a sample screen from TEMS.(31)
View long description of this figure
Data Barriers
Some data barriers and gaps include the following:
MTC is committed to advancing the use of real-time and archived data for a variety of planning for operations activities. One example is that since 2012, it has switched to using a private sector data provider as the primary source for regional congestion monitoring instead of using the floating car method because it is costly and usually only a few days’ worth of data are obtained. MTC has also been promoting enhancements to PeMS that would improve the system’s usability, data extraction capabilities, and ability to analyze and quantify non-recurrent congestion as well as expand the system to include arterial and ramp data.
SPC is the MPO for the greater Pittsburgh 10-county area. Its regional operations planning efforts were originally based on the Pennsylvania Department of Transportation (PennDOT) Transportation System Operations Plan and are integrated into the regional long-range transportation plan. SPC’s 2011 Regional Operations Plan identified the following seven priority areas:
SPC has traditionally collected most of its own traffic data for use in planning for operations, including 6,600 traffic counts in a 10-year period from roadways throughout the region.(32) SPC also collects travel time, speed, and delay data by conducting travel time runs along regional corridors every 3 years and uses GPS receivers and on-board diagnostic tools to inform investment decisions and support CMP.
SPC collects its own park-and-ride utilization data as well as most of its traffic counts. The organization also receives additional traffic count data and incident data from PennDOT.
In recent years, SPC has moved away from the traditional floating car method of data collection and has integrated new sources of data such as private third-party vehicle probe data, which is made available through PennDOT and through the use of Bluetooth® detection devices. These Bluetooth® devices are used in temporary installations much like traffic counters, but their function is to gather travel time and speed data rather than traffic volume data.
SPC’s corridor travel time run data were aggregated by corridor and evaluated based on forecasted congestion levels for a.m. and p.m. peak periods. Comparing observed speeds with the posted speed limit, SPC reported performance measures, including delay per vehicle and per vehicle mile, as well as total delay and total delay per mile.
Collected data are used to customize the 25 congestion management strategies in the SPC toolbox for targeted corridors to address both recurring and non-recurring congestion.(33)
Rather than setting specific thresholds for acceptable levels of congestion or explicit targets, SPC aggregates regional rankings of congested corridors that are addressed in the Regional Operations Plan, developed in conjunction with PennDOT.(34) These regional rankings are depicted in various graphics to show relative performance in accordance with the specified metrics.
Figure 16 depicts delay data for various corridors, enabling SPC to identify congested segments and to select targeted operational improvements for congestion mitigation.(35)
View long description of this figure
Figure 17 illustrates the variability in speeds caused by incidents as a measure of non-recurring congestion.(35) This graph identify patterns and problem regions and can help inform regional strategies for incident management, special event planning, and work zone management without actively measuring non-recurring congestion.
SPC has begun to update its CMP analysis methodologies and data products in order to use new technologies and new sources of data. A comprehensive update of the CMP Web site is underway and will incorporate freeway and expressway reliability performance measures that account for non-recurring congestion.
View long description of this figure
SPC recognizes that a stronger connection is needed between the CMP and project selection to encourage the implementation of M&O strategies. Engaging outside partners has been a major challenge to strengthening connection.
The commission uses 10 years of traffic counts and real-time information to improve traffic flows and reduce transit travel times through adaptive traffic signalization strategies.(34) It also uses microsimulation and visualization modeling software to facilitate detailed traffic analysis for proposed projects and to generate animated model results for demonstrations to policymakers and other relevant stakeholders.
SPC’s Traffic Operations and Safety Committee focuses at least once a year on evaluating implemented strategies from the Regional Operations Plan initiatives. It also compares M&O strategies with capital improvement projects to assess cost effectiveness and overall performance improvement of the system.(34)
SPC has used traffic count data from loop detectors to conduct before-and-after studies of implemented M&O strategies to evaluate performance, such as the regional traffic signal program, but not all projects have enough money in the budget for these assessments. The organization, in conjunction with its planning partners, also plans to assess a recently completed ramp management study to determine its applicability for other sites.(34)
SPC has documented a lack of available transit operations data from 10 regional transit agencies as a barrier to using data for the CMP, but it is currently developing a regional transit smartcard program that may enable better collection and coordination of data in the future. The commission is also looking to improve its measures of non-recurring congestion through a partnership with the new TMC to increase data sharing and develop additional performance measures to support the CMP, such as incident clearance time.(34)
SANDAG is the MPO for San Diego County, serving as the regional decisionmaking body for 18cities and county government within its jurisdiction.(36) SANDAG compiles traffic count data for significant roadways and all Caltrans routes in the San Diego region, which are collected annually and maintained by the 18 jurisdictions, Caltrans, and the county. SANDAG published this information online from 2006 to 2010 to provide average weekday traffic counts, which are two-way, 24-h volumes.(37) SANDAG is in the process of acquiring arterial data from a private sector data vendor.
SANDAG also uses Caltrans’ PeMS to access data. PeMS is a real-time archive data management system for transportation data in California. It collects raw detector data in real time, stores and processes these data, and provides a number of Web pages that provide analysis of the performance of the freeway system.
The following information is available in PeMS (plots, tabular, and/or mapped):
SANDAG uses the PeMS tool for visualization of the data, as well as GIS mapping applications available to visualize the traffic information compiled from Caltrans sensors. SANDAG generally uses the PeMS tool for planning activities such as post-analysis, planning analysis, identification of bottlenecks, and identification of critical areas of travel time savings. Through the I-15 ICM project, SANDAG worked with Caltrans to develop a PeMS module entitled Corridor-PEMS that allows many of the planning and operations activities conducted with PeMS to be done using real-time data.(38) SANDAG has developed a smart decision support system as part of the ICM project that will look at real-time model performance, operational data, and historical offline model data to develop, recommend, and implement corridor-level response plans in real time. For example, the decision support system will allow the coordination and use of freeway ramp meters with arterial signals to manage congestion levels during an incident based on real-time and archived operational data.
SANDAG also developed the initial prototype PeMS module, entitled Arterial PeMS, to extend PeMS capabilities and functionalities beyond the State Highway System to collect arterial data and report performance.
SANDAG produces an annual State of the Commute report for the public on the usage and performance of freeways, transit facilities, and local roads. The report provides regional travel trends, traveler delay data, and key statistics for several travel corridors in the region. The reports also include before-and-after analysis for recently deployed projects. Data used for the State of the Commute reports include freeway and arterial data from PeMS and transit data from area transit providers.(39) The 2013 State of the Commute report introduced arterial corridors to the report.
SANDAG monitors regional bottlenecks on an ongoing basis using Caltrans’ PeMS bottleneck algorithm, which allows SANDAG to identify major bottlenecks. Recent expansions of the detector network for PeMS were developed specifically for the San Diego region to incorporate real-time transit and arterial data. SANDAG uses transit travel time and ridership data to track improvement toward general target objectives.
SANDAG currently uses archived operations data to validate its models. The organization plans to expand the data used to develop its models to include pedestrian and bicycle data to support and advance pedestrian and bicycle modal improvements through the Active Transportation Grant Program and the Regional Bicycle Plan.(40,41) Along with the County of San Diego and a local college, SANDAG developed a bike counter program to begin capturing bike data. Current efforts using this system have focused on establishing initial baseline data collection efforts and developing a long-term strategic plan for incorporating bicycle data in future State of the Commute reports to support ongoing bicycle data collection efforts.
SANDAG is making the transition to an activity-based model, including an active transportation component, and plans to use archived operations data to baseline the new model. SANDAG is also looking to advance a performance-based management approach to corridor management based on real-time and historic data.
As part of the I-15 ICM initiative, SANDAG led the AMS for various ICM strategies for the corridor. The AMS approach used by the San Diego team consisted of extracting a subarea of the macro regional travel demand model for use in a corridor-level micro-simulation model.
In cooperation with Caltrans, SANDAG has used a similar analysis, simulation, and modeling approach to develop CSMPs for other major corridors, including I-805, to identify, evaluate, and plan corridor-based system management strategies.(42)
SANDAG has not historically performed evaluations of implemented operations projects and strategies but has begun to aggregate data from the newly constructed express lanes on the I-15 corridor and will be using that data to compare FasTrak (an electronic toll collection system in California) users to normal traffic. SANDAG will use these data to monitor the performance of the 20-mi express lanes system and determine operational improvement strategies to best maximize overall corridor mobility and operational efficiencies that can be considered over time. SANDAG provides before-and-after study results for a few significant roadway infrastructure and transit improvements in the region as part of each annual State of the Commute report.
The greatest challenge SANDAG has with data in planning for operations is arterial collection where there is not yet sufficient infrastructure, although new equipment is being added to supplement the data from PeMS. Currently, SANDAG only has access to enough freeway travel data to provide consistent and complete performance reporting.
SANDAG also has had challenges in obtaining data in small enough time increments to validate its models. It also has occasional issues with roadway sensor failures when conducting travel time analyses.
Metro is the MPO for the three-county area surrounding Portland, OR. Metro’s 2014 Regional Transportation Plan continues the performance-based, outcomes-driven planning approach established with its 2035 Regional Transportation Plan, adopted in 2010.(43) Metro recently implemented its first round of performance-based planning with a call for projects for the TIP.
Metro has access to primarily freeway operations data sources but is working on implementing a plan for data collection on arterials and determining how this data can be used for planning. Metro has developed a strong partnership with Portland State University through which they are working on new methods for collecting, analyzing, and archiving transportation system performance data in the Portal system, an archived data user service for the Portland-Vancouver metropolitan region. Through Portland State University’s Portal, Metro has access to traffic counts, speed, occupancy, incident, weather, transit, and freight data. The traffic data are collected by loop detectors across the freeway. Metro has also obtained travel time data from a private third-party data provider.
During the 2035 Regional Transportation Plan development, Metro and its partners established several performance targets, shown in figure 18, to monitor and report out during each plan update.(44) As a key element of its CMP, Metro began a monitoring program to assess the performance of 24 mobility corridors every 2 years and use that information to inform incremental land use and transportation project implementation decisions. Metro developed a Mobility Corridor Atlas to serve as a baseline that includes land use and performance data for regional measures such as travel time, safety, and bike and pedestrian network completion.(45) This publication will be updated in 2015. Metro uses data from Portal to track performance measure targets that are part of the regional transportation plan.
View long description of this figure
As part of the process for developing its TIP, Metro provides a Data Resource Guide that contains updated performance data on several measures implemented along Metro’s 24 mobility corridors along with its request for projects. The applicants are advised to use this data in completing their project applications and are encouraged to move those performance indicators in a positive direction through their projects. The Data Resource Guide includes performance information on regional travel options, transit, safety, roadways, and active transportation.(46)
Metro has also developed a Regional Transportation System Management and Operations (TSMO) Plan to maximize the benefits of new investments and to facilitate data collection and analysis for use in planning—for example, through installation of automated vehicle locators to provide transit data and the use of media access control address-reading technology to provide traffic data.(47) Congestion data on the system are used to help determine which projects will be funded using the funds allocated specifically for TSMO. Metro relies on before-and-after data to establish projected impacts from the proposed TSMO projects.
The data used to validate Metro’s transportation models include traffic count data collected by local jurisdictions in the region along cuts or screenlines to verify auto assignments. Metro also uses an online archive of freeway count data, and the Oregon Department of Transportation recently purchased private third-party data for comparing travel times and speeds. The purchased data cover a large number of arterials, but they are fleet-based and limited to whatever facilities those vehicles are traveling on.
Metro is also moving from experimentation to broader use with a DTA model. The organization’s DTA model is still fairly new and is in a transition period from development to application.
Metro participated in an FHWA demonstration project on the use of a data integration hub to easily transfer model and limited field data between AMS tools. The demonstration focused on NW 185th Avenue in the Portland area and helped evaluate ITS in the corridor, including ramp metering, adaptive signal control, and transit/truck priority. The data sources for the demonstration included field-measured 24-h link volumes and speeds, peak period turning movement counts, signal timing data, regional travel demand model data, and Bluetooth® travel time data.(7)
Metro is not currently assessing implemented M&O strategies, but the latest version of the Regional Transportation Plan notes that they will be evaluated in the near term.
Loop detectors have only been deployed on the region’s limited-access freeways, which restrict the amount of data available on the system, and the data must be supplemented with simulated data from model output for the rest of the system. Evaluation of strategies as part of the larger planning process depends on the expansion of data capture along the rest of the freeway and arterial system. Metro is now working with private third-party data and using it to update its performance measures.
Assimilating data from a wide variety of sources can be challenging; for example, private third-party travel time has to be heavily postprocessed before it can be used. Also, the resolution of the volume data Metro receives from local jurisdictions is not sufficient for the needs of newer modeling tools: tube count data are aggregated into hourly figures, whereas 5-, 10-, or 15-min intervals are more appropriate. There are also significant postprocessing needs for the count data.
In working with the DTA model, Metro has found that it had to spend a great deal of time to get Synchro® data into its network. Metro also wants better methods of visually depicting data to demonstrate trends and analyze problems.
WILMAPCO is the MPO for Cecil County, MD, and New Castle County, DE. It collects or acquires data from its planning partners to use for monitoring transportation system performance as part of its CMP and to illustrate congestion visually through maps that highlight areas of improved or degraded performance. The area is moving forward in expanding its data collection efforts. The area is moving forward in expanding its data collection efforts, including the installation of many Bluetooth® devices covering freeways and arterials in 2015. These devices are provided and managed by the Delaware Department of Transportation (DelDOT) TMC.
DelDOT is the primary source of the archived data for WILMAPCO. Along the expressway, there are radar traffic detectors about every 0.5 mi collecting speed and volume data. The University of Delaware Center for Transportation provides WILMAPCO with freeway and arterial data that it collects through annual GPS travel time runs. The region also collects traffic count data from a variety of sources, which it uses for level of service analysis. In addition, WILMAPCO has access to transit usage data from the Delaware Authority for Regional Transit, including park and ride utilization data, ridership, and monthly seating capacity. Soon, WILMAPCO will have Bluetooth® travel time data along the arterials from the DelDOT TMC. WILMAPCO supplements archived data from DelDOT with arterial travel time data from a private third-party data provider. WILMAPCO intends to collect more data in-house to reduce cost.
WILMAPCO uses arterial travel time data to focus on improving the flow of traffic through coordinated signal timing. The data are used to analyze volume-based level of service to identify congested intersections and target regions for improvement. Two other performance measures are monitored using the data, including travel speed versus free-flow speed and the number of crashes along a corridor. The performance measures are visually depicted (as in figure 19 and figure 20) and tracked over time to identify areas in need of improvement.(48)
View long description of this figure
View long description of this figure
In 2012, WILMAPCO began including in its congestion management system report an Intersection Operational Analysis section that examines the performance of signalized intersections and prioritizes congested intersections that can be alleviated by adjusted signal timing methods such as traffic responsive signalization (TRS) and those that require capital improvements to increase performance.(49) WILMAPCO’s 2014 version of the Intersection Operations Analysis section has expanded beyond signal timing adjustments to include the following:
Beyond this effort, WILMAPCO’s strategy evaluation is more qualitatively focused, favoring those strategies that eliminate trips, create a mode shift, and effect operational efficiency over capital improvements. WILMAPCO found that the outputs of cost-benefit analyses were not useful for informing decisionmakers, and WILMAPCO no longer conducts the analyses.
WILMAPCO’s congestion management system does not yet include conducting assessments of individually implemented M&O strategies, but it is working with State transportation departments on coordinating the data necessary for the analysis. Regional performance improvements and degradations can be seen from its system monitoring section (figure 21), which can be used to inform future decisions. In the assessment of individual strategies, WILMAPCO has experienced difficulty isolating the effects of a particular strategy in conjunction with local development, where business closures and signal retiming activities impact system performance but are not regularly reported to the agency.
View long description of this figure
WILMAPCO’s barriers include a lack of funding and resources to collect before-and-after data necessary for the assessment of individual strategies.
PAG collects much of its own data, supplemented by data from member jurisdictions and contractors. Collecting and compiling data on system performance is part of PAG’s work program. PAG aggregates all traffic volume counts on arterials and collectors in the region and collects turning movement data. It receives performance data on freeways and other State roadways from the Arizona Department of Transportation (ADOT).
Travel time, speed, and intersection delay are used to generate a travel time index and the annual delay per traveler to measure system performance as well as traffic counts to track volume-to-capacity ratios, which are depicted graphically to show relative congestion throughout the region (figure 22).(50)
View long description of this figure
PAG has set several system performance objectives and performance measures as part of the CMP monitoring process in its current regional transportation plan, and the organization has begun monitoring those objectives as part of its 2045 Regional Transportation Plan development process. PAG will continue to evaluate the relevance of its CMP objectives as it implements revisions to the CMP and assesses the requirements associated with performance measure reporting required as part of MAP-21 legislation. PAG is working with the University of Arizona to launch a travel time collection effort using Bluetooth® technology.
M&O strategies are generally prioritized based on objectives, goals, and resources that have been agreed upon by consensus among member jurisdictions and supplemented by user input, such as survey input from participants in the rideshare program.
The PAG region uses a Synchro® model for 600 signals, and this model is used to evaluate signal timing projects. In conjunction with the University of Arizona, PAG has begun to develop a mesoscopic model that will have the potential to evaluate more M&O strategies. To prove the concept, researchers and PAG staff have tested the new model on a bus rapid transit evaluation that involves the regional travel demand model, the mesoscopic model, and the microscopic model (Vissim).
Recently, Tucson and Portland participated in an FHWA demonstration project on the use of a system to integrate data between AMS tools as well as limited field data. In addition to demonstrating aspects of the AMS system, the test in Tucson focused on the following
two objectives for the I-10 corridor and its interchanges:
The data sources for the test in Tucson are defined in table 3, which is taken from the FHWA AMS integration project report. The AMS test used a DTA and VISSIM model.
Table 3. Data sources for AMS integration test in Tucson.(7)
| Source Data Type | AMS Tool | Source |
|---|---|---|
| Regional travel demand model | TransCAD | PAG |
| 24-h segment counts, intersection turning movement counts, I-10 mainline speed data | Collected by quality counts for ADOT | |
| Regional Synchro® models | Synchro® | PAG |
Blank cell = Not applicable.
Due to limited staff and funding, most performance evaluation is conducted using Synchro® analysis rather than post-implementation studies. However, PAG has evaluated selected corridor timing projects through a comparison of before-and-after volume and speed data.
Performance measures established in the CMP report were focused on low-cost options because of funding limitations. PAG would like to conduct more before-and-after studies but lacks the staff required to cover all activities.
