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                        Exploration of the Application of Collective Information to Transportation Data for Safety White Paper


            
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        Safety Data Analysis & Tools
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                BACKGROUND
The need for roadway data to support safety decisions is increasing due to the development of a new generation of safety analysis tools and methods. These include the 2010 Highway Safety Manual (HSM) *(1)*, the Interactive Highway Safety Design Model (IHSDM) *(2)*, SafetyAnalyst *(3)*, and the American Association of State Highway and Transportation Officials (AASHTO) National Cooperative Highway Research Program (NCHRP) Series 500 Data and Analysis Guide *(4)*, which all require crash, roadway, and traffic data to achieve the most accurate results. State and local Departments of Transportation (DOTs) need more detailed roadway and traffic data to support data driven decision-making that will have the greatest impact in safety on our nation's roadways. Transportation agencies responsible for the collection, analysis, management, and interoperability of highway safety data have expressed that one of the largest hurdles to having a comprehensive data management information system for highway safety is the lack of roadway inventory data, particularly for local roads. More specifically, agencies identified a lack of resources (both personnel and monetary) to collect roadway data for local roads, or to collect detailed roadway data elements for specific types of locations such as intersections, ramps, curves, or pedestrian facilities. Similarly, the data are also difficult to maintain or update as agencies make changes to these facilities.

One potential solution is collective information. Collective information is the process of assembling data or collecting information on a subject using a large, disperse, and potentially uncontrolled group of people. Collective data is an emerging concept for highway safety data, although there have been some very powerful examples of success in other transportation fields. Agencies have the potential to significantly advance their data collection efforts with minimal investments by using applications that leverage data-enabled phones or a standard web browser for the collection, identification, or classification of data by the public.

The process of collecting the data could be described as crowdsourcing, but the results would be classified as collective information. The popular website *Wikipedia* is an excellent example of collective information. *Wikipedia* is a web-based, collaborative encyclopedia. Users of the website collectively populate the information in the online encyclopedia. This is an organic process as the users independently elect to populate the information, guided by some loose principles and forms of oversight. Most of the production of the information is done anonymously and essentially independent of one another *(5)*. However, collectively the information presents a nearly complete description of many topics.

Collective information is starting to emerge as a method to collect data in the transportation world. Currently, two of the largest and most well-known examples of collective data applications in transportation are the City of Boston's Citizens Connect and Street Bump applications. The Citizens Connect application (described in more detail in Appendix A) allows residents and visitors in Boston to collect information on potholes, faulty street lights, and other public works issues. Citizens report these issues to the City using their smart phones and populate a database that the City uses to prioritize public works efforts. A more detailed application by the City of Boston is the Street Bump application which uses a mobile device's accelerometer, or motion sensor, and global positioning system (GPS) to detect a distinct jolt when a motorist drives over a pothole or sunken manhole cover and pinpoint its location. The users of this application are collectively developing a pavements condition database for the City through their daily travel. While these applications utilize the general public, transportation agencies could rely on anyone from contractors, maintenance crews, volunteer organizations, to the general public for information.

Many of the other notable applications of this concept in transportation involve more planning level data to include applications that track travel behaviors for use in these models. Chapter 26 of *The On-line Travel Survey Manual* identifies five such examples, the most notable of which is San Francisco County's CycleTracks application *(6)*. CycleTracks, a tool developed by the San Francisco County Transportation Authority (SFCTA), uses smart phone's GPS support to record citizen's bicycle trips for use in a route choice model.

Why are agencies using these collective data applications? One of the primary reasons is the applications present a cost-effective way to collect transportation data that agencies need for decision-making. The examples to date are only a handful; however, these pioneers provide a glimpse of the potential of the application this technology. The prevalence of smart phones and other mobile devices makes this type of data collection feasible. Distributed data collection applications may provide a tremendous opportunity for transportation agencies to cost- effectively collect the data needed to improve the transportation system or to develop a database of existing infrastructure or conditions.
      
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                Book:Exploration of the Application of Collective Information to Transportation Data for Safety White Paper
              BACKGROUND
                OBJECTIVE
                DISCUSSION
                REFERENCES
                APPENDIX A: AGENCY INTERVIEWS
                APPENDIX B: NON-TRANSPORTATION EXAMPLES OF CROWD SOURCING
                ACRONYMS U.S. DEPARTMENT OF TRANSPORTATION

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