Leveraging Big Data to Improve Traffic Incident Management (2019) / Chapter Skim
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Pages 16-38
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16 By 1996, digital storage became a more cost-effective option for storing data than paper (SB 2016)
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State of the Practice of Big Data 17 Big Data datasets often are characterized using five attributes, referred to as the "five Vs": volume, variety, velocity, veracity, and value. 3.1.1 Volume Volume characterizes the main aspect of a Big Data dataset.
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18 Leveraging Big Data to Improve Traffic Incident Management Emergency Medical Services Information System (NEMSIS) -- consisting of 30.2 million records from 15,000 EMS agencies -- makes up about 40 gigabytes (GB)
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State of the Practice of Big Data 19 rapidly changing Big Data dataset that needs to be processed quickly to catch suspicious transactions and deny payments. 3.1.4 Veracity Veracity refers to the trustworthiness of the data in Big Data datasets.
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20 Leveraging Big Data to Improve Traffic Incident Management corrective or augmentative action(s)
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State of the Practice of Big Data 21 3. The tool had to be capable of handling component failure (e.g., failure of the CPU, or memory, or of the network)
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22 Leveraging Big Data to Improve Traffic Incident Management programming model. They can scale up from single servers to thousands of machines, each offering local computation and storage.
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State of the Practice of Big Data 23 work with random access memory (RAM) , solid-state drives, or disk drives.
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24 Leveraging Big Data to Improve Traffic Incident Management to deliver high availability and performance to NoSQL databases without sacrificing the robust consistency requirements and transaction capabilities found in relational databases. NewSQL databases also support the standard relational database language, SQL, to access and modify their data.
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State of the Practice of Big Data 25 has already been organized into an easily manageable format that facilitates sorting, merging, aggregating, and calculating. It should be noted that a traditional data warehouse -- which is a complex analytical system composed of one or more relational databases -- is not the same as a Big Data store.
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26 Leveraging Big Data to Improve Traffic Incident Management is composed of a distributed storage system where data is archived indefinitely. A server cluster processes the stored data using various Big Data frameworks and databases to allow users to explore and query the data, create visualizations and dashboards, classify data, identify patterns or trends, and create rules or predictive models.
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State of the Practice of Big Data 27 and use of Big Data analytics arise from the absence of theory to drive the analytics and critical judgment in interpreting the analytics. These shortcomings are of particular concern for evolving social systems.
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28 Leveraging Big Data to Improve Traffic Incident Management of clusters in which each observation belongs to the cluster with the nearest mean. Examples of software programs capable of performing clustering analysis include Apache Mahout, Apache Spark, and Revolution R Enterprise.
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State of the Practice of Big Data 29 be information about the text (e.g., its author, title, date, edition) and/or information that has been extracted using the text mining libraries (e.g., all names or locations mentioned in the text)
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30 Leveraging Big Data to Improve Traffic Incident Management a face)
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State of the Practice of Big Data 31 enhance brand awareness and adoption. Figure 3-8 shows an example of the results of a graph analysis called betweenness centrality.
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32 Leveraging Big Data to Improve Traffic Incident Management Example Application: Graph analysis often is used in fraud detection. In 2016, the International Consortium of Investigative Journalists (ICIJ)
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State of the Practice of Big Data 33 One benefit Big Data holds for transportation planners is the ability to track movements of vehicles and people on a scale never before imagined. Recent advances in crowd modeling systems have led to more focus on modeling complex locations; however, accurate data collection is one of the biggest limitations that crowd specialists face today (Alvarez 2015)
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34 Leveraging Big Data to Improve Traffic Incident Management 3.3.4 Public Transportation Many city administrations recognize the value of using Big Data for public transportation, particularly for improving the management of bus fleets and optimizing maintenance and operations. In Sao Paulo, Brazil, Big Data collected in real time provides a more accurate picture of how many people ride the buses, which routes are on time, how drivers respond to changing conditions, and many other factors.
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State of the Practice of Big Data 35 • Develop data standards, especially if transportation agencies are not collecting and managing the data themselves; • Consider approaches to reduce the volume of connected vehicle and traveler data so that it is more manageable while ensuring that all valuable data is collected; • Utilize specific technologies and techniques like crowdsourcing, cloud computing, and federated database systems that have come to characterize the state-of-the-practice in Big Data and which will facilitate transportation operators or private sector data service providers in extracting value from connected-vehicle and traveler data; • Develop connected vehicle Big Data use cases that incorporate Big Data analytics approaches and the operational strategies that could derive from the knowledge gained through those approaches; and • Further investigate the potential cost and other resource implications of adopting Big Data approaches based on the outcome of the use-case investigation. Shi and Abdel-Aty (2015)
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36 Leveraging Big Data to Improve Traffic Incident Management tunnel operations, chain stations, maintenance, and GIS shape files) in a cloud-based data lake.
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State of the Practice of Big Data 37 • Big Data in transport will lead to improved multi-source traffic and travel data availability and processing as well as to tools that improve multi-source traffic and travel data fusion. Combining big, open, and linked data will foster innovation and economic benefits.
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38 Leveraging Big Data to Improve Traffic Incident Management data, researchers are developing the TIMELI system (Traffic Incident Management Enabled by Large-data Innovations) , which will make use of emerging large-scale data analytics to reduce the number of incidents and improve incident detection.
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