Origin-Destination (O-D) trip matrices have traditionally been prepared through a combination of roadside interviews and the application of both trip-end and gravity models, followed by matrix prediction models. This approach also incorporates evidence from supplementary traffic counts. Recently, the transportation planning community has begun to leverage mobile device GPS data to develop trip matrices as an alternative to roadside interview data and synthetic methods. Each of these approaches has a number of advantages and disadvantages. However, it has often been debated whether any of these approaches results in a matrix that performs better (or worse) overall. So, in order to understand how trip matrices best apply to your use cases, understanding O-D trip matrix output is crucial.
Origins And Destinations In A Trip Matrix?
Origin-destination (O-D) data, as the name implies, represents movement through geographic spaces, beginning at a point of origin (O) and heading to a destination (D). O-D datasets, also known as flow data, contain information about numbers of trips between two geographic points, more commonly known as zones. Most O-D datasets use data fields with "ID" headings that contain character strings like "zone1" and “zone2” to refer to start and end locations.
Non-geographical attributes also are commonly found in O-D datasets. These include the number of trips made from the origin to the destination over a given time period (i.e., a typical work day) and period of time (i.e., 8 AM to 9 AM). Additional attributes can include home locations of trip makers, disaggregation of the trips based on the modes of transportation used. Often times, all trips are combined, regardless of mode, and transportation planners essentially dissect this information to estimate the mode split among the trips. In the end, this O-D data provides the baseline data that transportation planners need to conduct their work when seeking to understand how transportation networks are used.
The Significance Of O-D Data
O-D datasets are an important part of today's planning world. The datasets serve as the foundation for analyses and models that influence the future of transportation systems. Traditionally, these models and the O-D datasets that power them were used to plan for car-centric cities. However, over the past few decades, the industry has shifted more towards planning for multi-modal facilities - a truly complete transportation network. Thus, understanding trip origins and destinations, regardless of mode, should be used as part of the solution and way forward.
The goal of this blog is to encourage greater use of Origin-Destination trip matrix output, as it is a critical element of many transportation planning models and efforts. We hope to steer your team in the direction of using O-D data to help support development of sustainable transportation plans. This can help advance opportunities to spur a modal shift away from cars and toward biking, walking, and public transportation.
Conclusion
If you are looking to learn more about and benefit from use of the Origin-Destination (O-D) trip matrix data, visit AirSage today!
