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31 This chapter presents a discussion of gaps in tools and data to support multimodal mobility, safety, pavement, and bridge and air quality measures in terms of collection, analysis, reporting, application, and data programs in general. This discussion reflects the survey results, follow-up interviews, and case examples, as well as the literature review. In addition, researchers used a review of all comments received on the NPRMs prepared by Cambridge Systematics, Inc., for NCHRP Project 20-24 (104). In general, data and tools to support pavement and bridge condition are well developed. Direct field data collection and well-vetted predictive models have helped states improve their asset investment decisions over several decades. Based on the survey results, many states cannot rigorously assess gaps in asset condition relative to targets, identify and mitigate risks, set priori- ties, and project how investments will affect future condition. Gaps in the development of performance measures for congestion, reliability, and freight will require additional support; of the national goal areas identified in MAP-21, these measures have the least amount of applied experience behind them. Until only recently, agenciesâ ability to measure congestion and reliability directly lagged behind other goal areas due to lack of data. Mobility performance measurement has had to rely on surrogate measures, such as demand levels and estimates of available capacity, to infer performance. Data are now available to permit direct measurement of mobility, but the data landscape continues to undergo rapid change and new data sources are likely to be introduced continuously to the market. Taking full advantage of this information, however, will require investments in training, hardware, and software. Based on the survey results, a significant area of concern among State DOTs and MPOs is setting performance targets. Even states and regions that are well along with performance mea- surement may have spent little time setting actual performance standards or targets outside of the traditional goal areas of asset management and safety. Implementation of MAP-21 and the FAST Act will generate a flurry of activity in target setting, requiring greater coordination and cooperation among agencies on technically challenging material. In addition, coordination of targets between State DOTs and MPOs is relatively unprecedented and so requires guidance. The following sections present research needs and gaps related to data collection, analysis, and tools in the following categories: bridge, pavement, mobility, and safety. Direct comments from survey respondents are shown in quotes. Data Collection Gaps Gaps in Bridge Data States have no major gaps in data, but have some concerns about the data being collected. MPOs are relying heavily on the states to provide bridge condition data. Ten open-text responses were received regarding issues, needs, and concerns. They included one related to collection C H A P T E R 4 Gaps in Data and Tools to Support Performance Measurement
32 Analyzing Data for Measuring Transportation Performance by State DOTs and MPOs every 2 years being a challenge, one asking for clear direction on how to calculate the area of a culvert, and a few were concerned about the good/fair/poor rating. For example, one state indi- cated, âThe criteria for Good-Fair-Poor conditions is not better than it is for Structurally Defi- cient. A more robust method should be used to make the determination for Good-Fair-Poor.â Concerns from the comments on the NPRM included not having data on non-state-owned facilities. Gaps in Pavement Data Some states reported data gaps, especially in relation to cracking data on NHS Interstates and non-Interstate roadways. MPOs are relying heavily on the states to provide them with pavement condition data. In answering the survey, DOTs may have been considering the requirement to collect the pavement data in both directions; however, the final rule only requires the data in one direction. Issues and concerns related to collection of pavement data were as follows: â¢ Eleven states commented on issues related to collecting cracking data. They included: âthere is a lack of national standard on rutting, cracking or faultingâ and âprocessing data in a timely manner is difficult and a shortage of resources causes gap in coverage.â â¢ Processing at 1/10-mile increments will create an extra burden. â¢ The proposed cracking and rutting test methods are a significant departure from the DOTsâ processes. â¢ Some states are missing data that creates challenges for forecasting conditions. â¢ Coverage may be spotty for IRI and rutting on the non-Interstate and on the local portions. â¢ One state reported, âIn terms of Cracking on the Interstate and non-Interstate, we have a potential concern about the translation of MDOT protocol to HPMS Field Manual definitions in terms of accuracy. Cracking also has a spotty history on the non-Interstate.â â¢ An MPO concern about pavement data included not having faulting and cracking data. â¢ Concerns from the comments on the NPRM included not having data on non-state-owned facilities. Gaps in Mobility Data States and MPOs have many concerns about collecting data to support mobility and air qual- ity measures. Nineteen DOTs and four MPOs report concerns with the emissions and tailpipe measures. These concerns include insufficient expertise and data. These measures are no longer required in the final rule. The comments and concerns related to the mobility measures are more significant and include conflation, segmentation, and PSL. They are described in more detail below. Conflation Twenty-nine State DOTs and nine MPOs anticipate having issues with conflation. Figure 4-1 shows percentages. Conflation is the process of combining data sets from different sources so that a travel time/ speed and traffic volume are assigned to all roadway links. This process, typically accomplished in GIS, establishes the segmentation relationships (e.g., âcrosswalk tableâ) between the public agency roadway network and the private sectorâs roadway network. The result is that speeds and traffic volumes are available for all roadway links.
Gaps in Data and Tools to Support Performance Measurement 33 The survey respondents were commenting on the NPRM, where the requirements related to the reporting of metrics were not yet clear. The final rule and subsequent guidance from FHWA clarifies that states do not need to complete conflation of their networks, which simplifies the requirement significantly. Rather, states must enter the travel time metrics into HPMS, and the combination of these metrics with volumes occurs at the FHWA level. However, a state may still want to analyze data to prepare for target-setting analysis, which would require preparing and combining speeds and volumes, which would then require conflation. Segmentation Nineteen states and eight MPOs have issues regarding segmentation. Ten states indicated that they anticipate issues with HPMS as a data source for VMT. Five out of 13 MPOs have issues. The issues cited include data segmentation not matching, gaps in data, lack of consistency among states, lack of reliable occupancy data, HPMS data not robust enough for operations, HPMS not available until June of the following year and coverage of HPMS. Note â segmentation refers to the method states and MPOs use to divide the roadway into sections for analysis. One state indicated, âThe data accuracy and segmentation to do the conflation are serious issues to be considered for this part of the measure.â Segmentation issues include the following: â¢ Reconciling traffic segments â Traffic Message Channel (TMC) and Linear Reference System (LRS) segmentation is differentâone TMC can equal multiple LRS segments and vice versa, and creating a crosswalk table will be challenging. â Frequency of segmentation would change continuously, which would affect the perfor- mance measures to be reported. â Segments not aligning could cause significant work. â¢ Differences between data sources â Private-sector segmentation does not align with public-sector data. â NPMRDS network may not match the networks being used for other performance measure- ment efforts. â¢ Geospatial processes may be difficult â Agencies have to account for regional boundaries, and some segments are too long on urban NHS. â One state reported: âIt isnât clear what to do with segments that cross urbanized area boundaries. There is also a wide range of segment lengths and the very short segments tend to have data quality issues in NPMRDS.â â¢ Network issues â There could be mismatches that need manual processes. â There can be misrepresentations of the changes in the network. DOT Mobility MPO Mobility Figure 4-1. DOTs and MPOs anticipating issues with conflation.
34 Analyzing Data for Measuring Transportation Performance by State DOTs and MPOs â One state reported âCurrent guidance is one mile lengths, but people do not evaluate travel time reliability over such short segments. A more logical process like using NHS intersect- ing roads as termini would make more sense.â â¢ PSL â PSL is available in the HPMS Sample Sections only. â One state reported that âThe required number of samples for HPMS are derived by formula from the normal dispersion characteristic of AADT values within a framework of preselected AADT groups.â In summary, a large percentage of states and MPOs reported technical issues associated with conflation and segmentation that will result in extra effort on their part. However, as previously noted, the need for conflation was removed from the final rule. Gaps in Safety Data DOTs and MPOs reported very few challenges with the collection of data to support safety measures. A few commented that some law enforcement agencies might not submit crash reports in a timely manner. Another reported that local law enforcement agencies are not required to provide details, such as locations and number of fatalities. Others suggested that there is a delay in FARS data that prevents them from reporting in a timely manner. For example, âthere is a learning curve for applying new categories to data, timeliness and coverage, timeliness in get- ting the annual data, timeliness of FARS data, and geographic coverage (data is at county level, but need at MPO level).â Another state said, âSerious injury data lags behind 2 years due to late submission by various entities.â Other comments pertaining to collection needs were related to data quality management for serious injury data, spatial data, and funding for future MIRE data collection and updates. Gaps in Data Analysis and Tools Figure 4-2 shows DOTs reporting unmet needs with respect to tools and technology. Across the board, less than half of State DOTs responding need tools and technology for ana- lyzing performance data. The highest numbers of needs are in pavement, followed by mobility, then bridge, and finally the safety performance area. Figure 4-3 shows DOT needs for reporting tools. In all cases, there appears to be slightly less need for reporting tools compared to analysis tools. The highest need for reporting tools is in the mobility area. The lowest needs are in pavement and safety. Gaps in Bridge Data Analysis and Tools Eighteen states have unmet needs with respect to tools and technology. They include the need for accurate deterioration models and forecasting; BrM development to support developing Figure 4-2. DOTs with unmet needs with respect to tools/technology.
Gaps in Data and Tools to Support Performance Measurement 35 prioritization and planning aspects; more automated analysis tools; enhanced degradation curves representing population of bridges within stateâs inventory; and migration from Pontis to BrM, conflating BrM data with climatic data, geologic data, socioeconomic data, and natural disaster data. Fifteen states need improved tools for reporting, including better statistical tracking of assets, dashboards for bridge condition, and better visualization tools that are user-friendly. Some states also suggested enhanced reporting with respect to condition of deck, superstructure and substructure condition, and record of changes over time. At least two states responded that there should be a standardized report in BrM, so that all states are doing the same thing. Gaps in Pavement Data Analysis and Tools Thirty-five DOTs are prepared to process the data and produce the required reports. Those that cannot do so cited database and software issues and cracking data as impediments. Nineteen states and one-half of the MPOs reporting have unmet needs with respect to tools and tech- nology. Eighteen states indicated they need better reporting tools. Tools and technology needs include automated cracking detection (four states), more automation of tools and processes (four states), ability to forecast (three states), and integration of tools. In answering the survey, DOTs might have been considering the requirement to collect the pavement data in both direc- tions; however, the final rule only requires the data in one direction. DOTs made the following comments about needs regarding tools: â¢ HPMS was mentioned several times in terms of it being an issue for reporting pavement measures (i.e., states were not sure how the HPMS process needs to be adjusted to account for entry of the measures.) â¢ Automated, consistent tools and methods are desired. â¢ An AASHTOWare product for taking a stateâs HPMS data set and reproducing the pavement measure calculations would be welcome. â¢ Additional ideas for using âinfographicsâ to better communicate this information to stake- holders are wanted. â¢ Formalized reporting tools to fulfill Transportation Asset Management Plan (TAMP) and performance reporting requirements are wanted. MPO needs include the ability to forecast future conditions and learning new tools. Gaps in Mobility Data Analysis and Tools Eighteen DOTs need better analysis tools. Seventeen DOTs would like better reporting tools. Tools and technology needs include powerful in-house analytic and computing tools, automated platforms to produce results, more resources, and improved tools for implementing the congestion management process (CMP). Figure 4-3. DOTs with a need for better reporting tools.
36 Analyzing Data for Measuring Transportation Performance by State DOTs and MPOs Comments follow: â¢ âIt would appear that a single program could be built for the states using NPMRDS to produce the mobility measures. Downloading the data and running it has been very time consuming.â (The final rule and subsequent guidance from FHWA clarifies that states do not need to complete conflation of their networks, which simplifies the requirement significantly.) â¢ âAlthough the tools are adequate for New York State, we would be interested in developing partnerships with other States to share and build on the capabilities of the AVAIL dashboard tool.â â¢ âAnticipating that data storage and processing power will be a limitation. Also have a need for application examples and accessible truck-related data.â â¢ âWould be great for FHWA to provide universal analysis tools for the data like they provided the universal data.â â¢ âWisconsin DOT does not have the skillset or tools to do work needed for these measures, that is why it is contracted to the TOPS Lab. TOPS Lab has the staff, for now, to analyze the data. If their staffing changes, we may face significant delays and costs in delivering the measures.â The main comment was the need for a national tool provided and approved by FHWA. Gaps in Safety Data Analysis and Tools Fifteen states and five MPOs indicated that they have unmet needs with respect to tools and technology. Regarding better tools, one-third of states and one-third of MPOs responded they would like them. Unmet tools and technology needs include the following: â¢ An HSP online tool that automatically updates the FARS database; â¢ More robust tools for analyzing trends and forecasts; â¢ Expansion of SafetyAnalyst and updating of the safety database to cover local roads; â¢ Electronic Transfer of Crash Reports to get crash reports efficiently; â¢ Data to support use of SafetyAnalyst; â¢ Deployment/acceptance/promotion of existing tools; â¢ Tools for visual presentations of data for transparency ease of use; and â¢ Tools for conflating crash data with other sources (e.g., volume, travel times, speed, pavement, road geometry, weather, and work zone). Specifics related to reporting tools are as follows: â¢ Predictive tools for deployment to regions and troopers; â¢ Visual reporting and graphics for community engagement and public meetings; â¢ Geospatial, interactive mapping; â¢ Forecasting the future of crashes; and â¢ Dashboard tools, interactive data modules for local agencies, and other web-based tools for safety analysis. Gaps in Target Setting As shown in Figure 4-4, DOTs have the most data gaps pertaining to setting targets for mobility measures and the least related to bridge condition. Data gaps for setting targets in the pavement and safety areas are reported by approximately one-third of all states.
Gaps in Data and Tools to Support Performance Measurement 37 Bridge Target-Setting Gaps Only seven states reported a need for improved target setting for bridge condition measures. Some of the gaps reported in terms of target setting include completion of a model to predict the growth of structurally deficient (SD) deck area, gaps in influencing factors, and the fact that there are many other bridge owners, which causes challenges. Pavement Target-Setting Gaps Fifteen of the DOTs cited that they have data gaps for setting targets. Gaps include no history of good, fair, and poor measures; no accurate cracking and faulting data; no history other than IRI (PSR and percent cracking are new); hidden costs of data; local NHS trend is missing; and changed technology for collection results in a discontinuity. In answering the survey, DOTs may have been considering the requirement to collect the pavement data in both directions; however, the final rule only requires the data in one direction. Mobility Target-Setting Gaps Twenty-five states and four MPOs indicated that they have major data gaps in terms of set- ting targets for mobility measures. Gaps include lack of data, no predictive model, data gaps in NPMRDS data, segment matching, and historic data. Safety Target-Setting Gaps One-third of the states and one-quarter of the MPOs reporting indicated they have major data gaps for setting targets. Gaps include difficulty obtaining current injury data, major injuries data entry is not up to date in the database, quality serious injury values and training with safety analysis tools, integration of data sources, obtaining VMT on local roads, and knowledge of external variables (e.g., age of vehicle fleet). Figure 4-4. DOTs with major data gaps for setting targets for measures.