Extraction of basic roadway information for non-state roads in Florida.
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Extraction of basic roadway information for non-state roads in Florida.

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  • Abstract:
    The Florida Department of Transportation (FDOT) has continued to maintain a linear-referenced “All-Roads” map

    that includes both state and non-state local roads. The state portion of the map could be populated with select data

    from FDOT’s Roadway Characteristics Inventory (RCI). However, the RCI data are available for only a small

    portion of the local roads in the All-Roads map, leaving a majority of the local roads in the map without the same

    data. Given the large number of local roads in the map, it is clearly not cost feasible to collect the data in the field.

    Methods that make use of existing data as alternatives to field data collection are thus needed.

    One potential source of existing data is police crash reports. For every reported crash in Florida, the law enforcement

    officers record information on more than 300 variables to describe the site and time of the crash, the geometric

    conditions, the traffic control, and drivers’ and pedestrian’s characteristics. Accordingly, this project aims to develop

    methods to extract roadway data recorded in crash reports as a means to both acquiring and continually updating the

    All-Roads map for local roads in Florida. To the extent possible, the project attempted to extract data for the

    following four variables that are included in Florida’s crash reports: number of through lanes, posted speed limit,

    shoulder type, and median type.

    The data extraction process to acquire data for the four variables in this project includes three steps. The first step

    involves the extraction of data from crash records for as many road segments as possible. The second step covers the

    case in which a road segment does not have any crashes. In this step, the values are derived from their adjacent

    segments based on the assumption that roadway features are likely to be continuous. Finally, the third step focuses

    on the remaining segments for which data could not be extracted or derived in the first two steps. In this step, the

    missing data are manually collected using a web-based data collection application that is designed specially to

    facilitate the process of observing and recording information from satellite images in Google Maps.

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