In past decades, transportation research has been driven by mathematical equations and has relied on scarce data. With increasing amounts of data being collected from intelligent transportation system sensors, data-driven or data-based research is expected to expand soon. Most online systems are designed to handle one type of data, such as from freeway or arterial sensors. Even if transportation data are ubiquitous, data usability is difficult to improve. A framework is proposed for a regionwide web-based transportation decision system that adopts digital roadway maps as the base and provides data layers for integrating multiple data sources (e.g., traffic sensor, incident, accident, and travel time). This system, called the Digital Roadway Interactive Visualization and Evaluation Network (DRIVE Net), provides a practical method for facilitating data retrieval and integration and enhances data usability. Moreover, DRIVE Net offers a platform for optimizing transportation decisions that also serves as an ideal tool for visualizing historical observations spatially and temporally. Not only can DRIVE Net be used as a practical tool for various transportation analyses, with the use of its online computation engine, DRIVE Net can also help evaluate the benefit of a specific transportation solution. In its current implementation, DRIVE Net demonstrates potential to be used soon as a standard tool to incorporate more data sets from different fields (e.g., health and household data) and offer a platform for real-time decision making.
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