Application of Big Data Approaches for Traffic Incident Management (2023) / Chapter Skim
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8 The existence of data alone does not ensure that accurate analyses can be performed or that the data can guide effective decisions. Quality data are essential to clearly identify operational behaviors and to make effective operational decisions.
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Datasets and Data Quality 9 • How are the data distributed geographically? • How are the data distributed across time?
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10 Application of Big Data Approaches for Traffic Incident Management Questions to ask when assessing the data: • Do any data values comply with the specified formats? • If so, do all the data values comply with those formats?
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Datasets and Data Quality 11 2. Traffic data.
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12 Application of Big Data Approaches for Traffic Incident Management flow and safety through improved traffic incident detection and clearance, traveler information, and so on. Operators at a TMC and field personnel can also input information on incidents, maintenance, and work zones, among others, into the ATMS.
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Datasets and Data Quality 13 Challenges/ limitations • Human data collection contributes to quality issues (e.g., missing data, erroneous data, commas within cells, free-text fields, proper nouns)
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Source: © 2021 Mapbox; © OpenStreetMap. Figure 3.
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Datasets and Data Quality 15 3.2.1.3 CAD Data Assessment Overview of the CAD Data Collected and Assessed State and local law enforcement agencies, fire departments, emergency medical services (EMS) agencies, and 911 centers rely on computer-aided dispatch (CAD)
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16 Application of Big Data Approaches for Traffic Incident Management pre-established codes, keywords, or free text. Some SSP programs also request a response from the drivers/vehicles assisted in the form of a postcard survey or a request to complete an online survey with structured and unstructured data.
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Datasets and Data Quality 17 1 NIEM is a common vocabulary that enables information exchange across diverse public and private organizations. See https://www.niem.gov/.
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Challenges/ limitations • Traditional, less rigorous data management (e.g., spreadsheet files, stored in shared network folders, managed manually) may lead to difficulty ingesting and analyzing content.
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Datasets and Data Quality 19 The team was able to obtain access to real-time navigation app data feeds for California, Massachusetts, Minnesota, and Utah to support the selected use cases. In addition, the team had access to a historical nationwide navigation app dataset ranging from August 1, 2012, to February 14, 2017, as well as access to U.S.
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20 Application of Big Data Approaches for Traffic Incident Management sensor ID, roadway ID, direction, annual average daily traffic (AADT) , truck AADT, volumes (vehicles per minute)
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Datasets and Data Quality 21 Challenges/ limitations • The density of sensor locations (across state and along routes) impacts the amount of data available.
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22 Application of Big Data Approaches for Traffic Incident Management Source: University of Maryland Center for Advanced Transportation Technology (2023)
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Datasets and Data Quality 23 3.2.3 Location Reference Data The team needed access to reference data to facilitate the integration of incident, traffic, and weather data. There are many diverse types of reference data.
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24 Application of Big Data Approaches for Traffic Incident Management Roadway Inventory Data Assessment -- Summary A summary of the data assessment, including challenges, limitations, and recommendations, is provided in Table 8. Detailed findings of the roadway inventory data assessment can be found in Appendix H posted with this report.
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Datasets and Data Quality 25 Figure 10. Geographic representation of routes in MnDOT's LRS data.
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26 Application of Big Data Approaches for Traffic Incident Management Third-Party Road Network API Assessment -- Summary A summary of the data assessment, including challenges, limitations, and recommendations, is provided in Table 10. Detailed findings of the third-party road network API assessment can be found in Appendix J posted with this report.
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Datasets and Data Quality 27 3.2.5 ARNOLD Data Assessment 3.2.5.1 Overview of the ARNOLD Data Collected and Assessed On August 7, 2012, FHWA expanded the requirement for state DOTs to include all public roads in their LRSs as part of the HPMS. As previously mentioned, this requirement is referred to as the All Road Network of Linear Referenced Data (ARNOLD)
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28 Application of Big Data Approaches for Traffic Incident Management Source: © OpenSteetMap -- Basemap, used under the Creative Commons AttributionShareAlike 2.0 License (CC BY-SA 2.0) , https://creativecommons.org/licenses/by-sa/2.0/ legalcode (no changes made)
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Datasets and Data Quality 29 Challenges/ limitations • The geometric accuracy of ARNOLD is not ideal (i.e., simplified geometries and missing segments)
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30 Application of Big Data Approaches for Traffic Incident Management Following the addition of traceability data to the MADIS data, the unified observations undergo a series of static and dynamic quality checks, which are also added as flags to each MADIS observation to indicate the quality from a variety of perspectives (e.g., temporal consistency, spatial consistency)
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Datasets and Data Quality 31 Source: NOAA (2021)
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32 Application of Big Data Approaches for Traffic Incident Management • Collects data in real time from fixed ESS and mobile sources; • Computes value-added enhancements to these data, such as by computing quality-check values for observed data and computing inferred weather parameters from CV data (e.g., inferring precipitation based on windshield wiper activation) ; • Archives both collected and computed data; and • Supports subscriptions for access to near-real-time data generated by individual weatherrelated CV projects.
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Datasets and Data Quality 33 Figure 16 shows the third-party weather records obtained to enrich crash data from Arizona, Florida, Maine, Ohio, Tennessee, Utah, and Wyoming. Third-Party Weather API Assessment -- Summary The assessment of data from the weather API had positive results.
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34 Application of Big Data Approaches for Traffic Incident Management geolocation of the vehicle, vehicle speed, and vehicle heading. Both datasets contain timestamp information based on Universal Coordinated Time (UTC)
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Datasets and Data Quality 35 Source: © OpenSteetMap -- Basemap, used under the Creative Commons Attribution-ShareAlike 2.0 License (CC BY-SA 2.0) , https://creativecommons.org/ licenses/by-sa/2.0/legalcode (no changes made)
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36 Application of Big Data Approaches for Traffic Incident Management 0 – 0 kph 0 – 4.6 kph 4.6 – 19.6 kph 19.9 – 43.8 kph 43.8 – 107.1 kph Crash Source: © OpenSteetMap -- Basemap, used under the Creative Commons AttributionShareAlike 2.0 License (CC BY-SA 2.0) , https://creativecommons.org/licenses/by-sa/2.0/ legalcode (no changes made)
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