The Ohio Department of Transportation (ODOT) currently has a vast amount of data that is being used within each division/office. This data has traditionally been housed separately within those divisions/offices and limited data sharing has been performed. This restricts innovative improvements to processes from being imagined and developed, due to the lack of knowledge of available data. As a result, some divisions/offices may be performing burdensome and extensive manual processing of data unnecessarily.
The goal of this research is to identify methods in which automation can be utilized to improve and make existing ODOT processes more efficient.
Other objectives of this research include the following:
• Identify ways in which data automation processes may be used/developed to support the functions of each division/office within ODOT.
• Define use case requirements and workflows including identifying problem statements, inputs, processing method, outputs and quality measures and validations.
• Develop statements of work to define requirements, data gaps and needs for future application development.
• Identify available third-party data sets that can be used to support transportation use cases.