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Experimental Implementation of Big Data Analytics for Traffic Incident Management

Description:

There is much talk about Big Data these days within the field of transportation; however, many groups and organizations do not fully understand or appreciate the scale of the data, the concepts, and the paradigm shift that is necessary to move from traditional data collection, storage, and analytics to the implementation of Big Data. This shift is not simply a linear one; rather, it requires completely new approaches to data collection, storage and management, and procurement of IT services, as well as skill sets that most agencies do not have and which are difficult to acquire. Furthermore, there are few ready to use Big Data data sets that can be used to demonstrate the benefits of this approach, particularly for Traffic Incident Management.

The recently completed project NCHRP 17-75, Leveraging Big Data to Improve Traffic Incident Management (TIM), was developed to begin to address these issues. Products from this project include a list of Big Data Opportunities, Big Data guidelines for transportation agencies and TIM programs, and Outreach Materials. One of the most significant findings of NCHRP 17-75 is that in order to gain the most benefits from Big Data approaches and analytics, a large-scale, multi-state implementation is essential. In addition, three barriers to implementation were identified: organizational culture, organizational capabilities, and access to large amounts of varied data. These significant technical and non-technical barriers would be difficult for any agency to overcome and should be addressed through continued national-level research into implementation.

This proposed project is a critical next step to document issues and demonstrate the feasibility and value of the Big Data approach; NCHRP is the ideal multi-state environment for this work. Products from this project will be used by state DOTs to enhance their TIM programs specifically and to enhance general operations. The NCHRP 17-75 panel originally submitted this as a project for NCHRP implementation funds. NCHRP 20-44 panel endorsed this proposed project but stated it should be solicited as a continuation project rather than an implementation project.

Objective:

The objective of this project is to demonstrate the feasibility and value of the Big Data approach to improve TIM. The project will demonstrate the scale and variety of the data needed, the data sources that can be leveraged, the Big Data concepts (e.g., cloud data storage, open data, data management), and the Big Data analytics techniques through real-world data, examples, and case studies.

Benefits:

With the completion of NCHRP 17-75, now is the time to conduct this proposed project, when the research is relevant and up to date.

(a) Value: the product will be valuable to DOTs; the findings of NCHRP 17-75 showed that due to the paradigm shift needed for data sources, storage, and use, a large- scale pilot is needed to facilitate this shift. DOTs and other response agencies will benefit from detailed guidance and use cases on how to leverage these new data sources to realize the benefits.

(b) Successful achievement: building on 17-75, proposed project is likely to be successful

(c) Likelihood of implementation-ready products: proposed project is a pilot implementation with supporting documents so likelihood is high

(d) Likelihood of implementation by state DOTs: state DOTs will be significant participants in the pilot so likelihood high

Related Research:

The recently completed project NCHRP 17-75, Leveraging Big Data to Improve Traffic Incident Management (TIM), was developed to begin to address these issues. Products from this project include a list of Big Data Opportunities, Big Data guidelines for transportation agencies and TIM programs, and Outreach Materials. One of the most significant findings of NCHRP 17-75 is that in order to gain the most benefits from Big Data approaches and analytics, a large-scale, multi-state implementation is essential.

Tasks:

1. Establish a data environment in which data can be stored and analyzed a. Integrate multiple, diverse datasets into a data analytics environment.

b. Document openness of data, as well as challenges with gaining access to the data.

c. Provide a description of the data environment.

d. Establish the costs of the data environment.

e. Describe the data management (storage, data structure, accessibility, security, etc.).

  1. Develop use cases for improving TIM – based on the data collected, explore the data and identify possible analyses that would help to improve TIM.

  2. Apply Big Data analytics techniques to produce real-world examples.

  3. Document and share outcomes– share trends, models, visualizations, and outliers.

  4. Develop lessons learned and case studies– document lessons learned throughout the process and develop case studies to enhance (i.e., give a practical basis for) the NCHRP 17-75 guidelines to further support and improve adoption and implementation. The case studies will look at using local, regional, state, and national data sets to develop the proof of concept. In addition, the case studies will aim to show how the use of the Big Data approach can support tracking national TIM performance measures: roadway clearance time, incident clearance time, and number of secondary crashes, as well as other measures. Through more detailed information about the TIM timeline, agencies should be able to better pinpoint causes of delay during response.

Implementation:

The project will involve the documentation of lessons learned and use cases, as noted in task 5 above. Implementation support could include peer exchanges, workshops, and presentations to support state DOT implementation.

Relevance:

This proposed project is a critical next step to document issues and demonstrate the feasibility and value of the Big Data approach; NCHRP is the ideal multi-state environment for this work. Products from this project will be used by state DOTs to enhance their TIM programs specifically and to enhance general operations.

Sponsoring Committee:ACP10, Regional Transportation Systems Management and Operations
Research Period:24 - 36 months
Research Priority:High
RNS Developer:Eileen Singleton Principal Transportation Engineer Baltimore Metropolitan Council esingleton@baltometro.org 410-732-0500 x 1033
Source Info:TRB Regional Transportation Systems Management & Operations; AASHTO Committee on Transportation Systems Operations
Date Posted:01/10/2019
Date Modified:05/01/2019
Index Terms:Data analysis, Data collection, Data storage, Traffic incidents, Incident management, Implementation,
Cosponsoring Committees: 
Subjects    
Highways
Data and Information Technology
Operations and Traffic Management
Safety and Human Factors

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