Leveraging Big Data to Improve Traffic Incident Management (2019) / Chapter Skim
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Pages 55-93
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From page 55... ...
55 TIM professionals are at the cusp of harnessing the potential of data to strengthen understanding of program operations and performance. Big Data has the potential to enhance exponentially both the breadth and depth of understanding of policies, strategies, and practices leading to more efficient, effective, and institutionalized programs.
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56 Leveraging Big Data to Improve Traffic Incident Management • The National Association of State Emergency Medical Services Officials (NASEMSO) , for EMS data; • The NHTSA, for crash data, including fatal data from the nationwide Fatality Analysis Reporting System (FARS)
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From page 57... ...
Assessment of Data Sources for TIM 57 the data openness, data challenges, and data costs categories warrant more explanation and are discussed further in the text that follows Table 5-1. 5.1.1.1 Data Openness The openness of data typically is assessed on three factors: availability and access, re-use and redistribution, and universal participation.
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58 Leveraging Big Data to Improve Traffic Incident Management The openness of data is important for several reasons, For example, data openness allows for: • The interoperability of datasets: Without interoperability, merging disparate datasets is very challenging, and without the ability to merge disparate datasets, it is impossible to discover relationships (correlations) between them, which is one of the primary goals of -- and is essential to -- Big Data analytics.
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Assessment of Data Sources for TIM 59 contained in public records. A common restriction is that data about a person is not normally available to others.
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60 Leveraging Big Data to Improve Traffic Incident Management 5.1.1.3 Data Costs The costs associated with obtaining, preparing, and using data can be divided into five cost categories; specifically, the cost of: 1. Acquiring the data, 2.
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Assessment of Data Sources for TIM 61 Chicago Data Maturity Framework offers a more-involved assessment of data maturity, called data readiness, based on multiple criteria and multiple maturity levels, with no single qualitative or quantitative output. The use of both models provides a more comprehensive look at the maturity of each data source.
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62 Leveraging Big Data to Improve Traffic Incident Management The questionnaire and scorecards were developed to help non-profits, government agencies, and other groups evaluate their data maturity and identify what they need to do to move forward with a successful data-driven project (Haynes 2015)
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Source: University of Chicago (2017) Figure 5-3.
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64 Leveraging Big Data to Improve Traffic Incident Management Data Readiness Lagging Basic Advanced Leading Accessibility Storage Integration Relevance and Sufficiency Quality Collection Frequency Granularity History Privacy Documentation Source: University of Chicago (n.d.) Table 5-2.
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Assessment of Data Sources for TIM 65 – Vehicle weight, and – Vehicle identification number (VIN) , and – Name of vehicle owner.
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66 Leveraging Big Data to Improve Traffic Incident Management state departments of motor vehicles or their equivalents. Large trucks and other commercial motor vehicles are an important subset of licensing and registration systems.
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Assessment of Data Sources for TIM 67 • Disparities in the formats and names for data elements and attributes sometimes make it difficult for officials in one jurisdiction to interpret data elements that appear on the vehicle registration documents of another jurisdiction. • Challenges in accessing the data in bulk or raw format may limit the usefulness for Big Data analytics.
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68 Leveraging Big Data to Improve Traffic Incident Management Data Readiness Lagging Basic Advanced Leading Accessibility Storage Integration Relevance and Sufficiency Quality Collection Frequency Granularity History Privacy Documentation Table 5-3. Crash data readiness.
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Assessment of Data Sources for TIM 69 Data Readiness Lagging Basic Advanced Leading Accessibility Storage Integration Relevance and Sufficiency Quality Collection Frequency Granularity History Privacy Documentation Table 5-6. Roadway data readiness.
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70 Leveraging Big Data to Improve Traffic Incident Management 5.2.2 Transportation Data 5.2.2.1 Description of Sources Within the transportation data domain, the following six data sources were assessed: • Traffic sensor data: A suite of in-roadway or over-roadway sensors provides the mainstay for transportation agencies to plan for and operate the road network. Sensors include inductive loop detectors, magnetic sensors and detectors, video image processors, microwave radar sensors, laser radars, passive infrared and passive acoustic array sensors, and ultrasonic sensors, plus combinations of these sensor technologies.
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Assessment of Data Sources for TIM 71 converge in TMCs, where software systems like advanced traffic management systems (ATMSs) combine the data and store it in relational databases.
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72 Leveraging Big Data to Improve Traffic Incident Management • TMCs are currently challenged with assimilating data from a variety of sources and deriving measures of traffic management performance. Big Data makes more data available to calculate meaningful measures, but the proliferation of Big Data also increases the demand for detailed reporting, thus increasing the challenges (Gettman et al.
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Assessment of Data Sources for TIM 73 through the associated systems, such as NOAA's Meteorological Assimilation Data Ingest System (MADIS) and the FHWA Weather Data Environment (WxDE)
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74 Leveraging Big Data to Improve Traffic Incident Management Data Readiness Lagging Basic Advanced Leading Accessibility Storage Integration Relevance and Sufficiency Quality Collection Frequency Granularity History Privacy Documentation Table 5-11. SSP/IRP data readiness.
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Assessment of Data Sources for TIM 75 5.2.3 Public Safety Data 5.2.3.1 Description of Sources As the primary point of contact for the public via the 911 calling system, and with their recognizable role as "first responders," public safety agencies are a critical part of TIM and generate valuable data. Public safety agencies generally are recognized to be law enforcement, fire and rescue, and emergency medical services (EMS)
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76 Leveraging Big Data to Improve Traffic Incident Management • Towing and recovery data: Towing and recovery data includes a catalog of calls for service and various timestamps associated with the response. 5.2.3.2 Summary of Findings Data from public safety agencies represents information collected by and from a significant number of incident responders -- particularly for incidents that require an official report or documentation by statute, for insurance company purposes, or in case of potential litigation.
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Assessment of Data Sources for TIM 77 This organization can be ideal for data collection, but it can complicate data extraction and analysis because the data typically sought after may be distributed across more than one record (time on scene, number of responders on the scene)
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78 Leveraging Big Data to Improve Traffic Incident Management Data Readiness Lagging Basic Advanced Leading Accessibility Storage Integration Relevance and Sufficiency Quality Collection Frequency Granularity History Privacy Documentation Table 5-16. ECC/911 call center/PSAP data readiness.
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Assessment of Data Sources for TIM 79 • Third-party vehicle probe data providers (e.g., HERE Technologies, INRIX) in which anonymous GPS-based data is automatically collected from vehicle fleets, consumer smart phones, and others including road sensors and toll tags.
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80 Leveraging Big Data to Improve Traffic Incident Management Open questions remain about the use of crowdsourced and social media data. Some of these questions, posed in the 2015 BDE and ERTICO-ITS Europe workshop report on Smart, Green, and Integrated Transport, can be paraphrased as follows: • How can data users best decide which crowdsourced and social media data is reliable and to what degree?
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Assessment of Data Sources for TIM 81 5.2.5 Advanced Vehicle Systems Data 5.2.5.1 Description of Sources Advanced vehicle systems are the norm in modern automobile manufacturing. These systems record, share, and ingest information in a variety of ways, for a variety of purposes.
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82 Leveraging Big Data to Improve Traffic Incident Management record speed, engine throttle, braking, ignition cycle, whether the driver was using a safety belt, airbag deployment, and the physics of crash events, including crash speed, change in forward crash speed, maximum change in forward crash speed, time from beginning of the crash event at which the maximum change in forward crash speed occurs, the number of crash events, the time between crash events, and whether the device completed recording. Unlike EDRs, which collect and store a few seconds of data immediately before and after a crash event, telematics systems continuously record all types of second-by-second data about vehicles and driver behavior, sometimes for years at a time.
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Assessment of Data Sources for TIM 83 over long periods of time and can be communicated in real time. In addition, as the cost of enabling mobile broadband communications has fallen, more automakers have been embedding telematics in vehicles.
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84 Leveraging Big Data to Improve Traffic Incident Management data, either partially or fully, without having to collect it one vehicle at a time. Similarly, telematics system user agreements may allow for the data to be reused or sold to entities other than the telematics system owner and/or the driver.
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Assessment of Data Sources for TIM 85 5.2.6 Aggregated Datasets 5.2.6.1 Description of Sources Aggregated datasets are created when a source collects (aggregates) data that has originated from other sources for the purposes of adding value to the data.
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86 Leveraging Big Data to Improve Traffic Incident Management speed, wind direction, cloud cover, visibility index, humidity, and other weather details, as well as ancillary data elements such as nearby storms, moon phase, sunrise, and sunset derived from multiple national and international meteorological data sources. • National Fire Incident Reporting System (NFIRS)
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Assessment of Data Sources for TIM 87 This section summarizes the research team's assessment of the various aggregated datasets that have been described. For ease of reading, the summaries have been grouped as follows: • RITIS and NPMRDS datasets, • Weather datasets, • Standardized public safety datasets, • MCMIS dataset, and • Private data aggregator datasets.
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88 Leveraging Big Data to Improve Traffic Incident Management Most notable is NOAA, which operates various weather databases (e.g., MADIS and the MADIS Integrated Mesonet)
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Assessment of Data Sources for TIM 89 leveraging these datasets for Big Data analytics for TIM remain. These challenges and limitations include the following: • The NFIRS distributed dataset is not a complete dataset.
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90 Leveraging Big Data to Improve Traffic Incident Management • The NEMSIS location data at the national level is limited to the zip code level, which could greatly limit data analytics, as this level of resolution would be too low for meaningful analysis. Data would need to be drawn from the local level, which significantly increases the effort needed to use the data for Big Data analyses of TIM.
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Assessment of Data Sources for TIM 91 or investigations conducted on motor carriers and other entities (e.g., U.S. DOT number, review date, review type, and safety rating)
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92 Leveraging Big Data to Improve Traffic Incident Management 5.3 Summary This chapter has presented the research team's assessment of 31 data sources in six data domains. The data sources were assessed on several criteria and against two data maturity models.
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Assessment of Data Sources for TIM 93 Big Data analytics because these sources and datasets lack openness, completeness, quality, collection frequency, and/or granularity, or because they are inaccessible due to legal, privacy, and proprietary issues. More immediate applications for TIM may be feasible through the integration of state traffic records data at the state and national level; use and integration of nationwide probe data (e.g., data from systems like the NPMRDS, if made available, or purchased from third-party providers like HERE Technologies or INRIX, Inc.)
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