Bentley OpenPaths Patterns

No! Bentley OpenPaths Patterns runs in your web browser with a growing set of native mobility analytics tools and provides exportable data which can be ingested into other modeling or analysis frameworks.

Bentley OpenPaths Patterns provides origin-destination (O-D) movements, traffic volumes, and travel routes throughout the United States, as well as tools to query and filter the data to understand daily travel patterns. Data is available for 2019, 2022, and 2023, with planned ongoing data releases.

Bentley OpenPaths Patterns supports modeling workflows by providing data about the magnitude, purpose, time of day, and location of travel to support model calibration, validation and related workflows. The data is also a good source of external movements, including internal-external, external-internal, and external-external travel. 

Bentley OpenPaths Patterns provides data to support the following model workflows*: 

• Validate existing models using Bentley OpenPaths Patterns traffic volumes 

• Use Bentley OpenPaths Patterns O-D data as base-year traffic demand matrices 

• Assign base-year O-D traffic demand 

• Calibrate models using O-D traffic demand and volumes** 
• Use O-D traffic demand matrices for matrix adjustment/estimation (ODME) procedures 

* All of the above capabilities are possible with Bentley OpenPaths or suitable third-party multimodal transport planning software. 
**Automated model calibration capabilities are available with Bentley OpenPaths Ultimate.

Origin-destination (O-D) mobility data tracks the movement of people or goods from a starting point (the "origin") to an ending point (the "destination"). It creates a picture of travel patterns, showing where journeys begin and end, and the paths taken between the two points. 

This data is vital for a wide range of applications. For transportation planning, it’s used to identify high-demand corridors, understand commute patterns, and plan for new roads or public transit services. 

In urban planning, it’s used to make informed decisions about zoning, where to locate new housing and services, and how to improve city connectivity. 

O-D data is typically collected from sources like anonymized mobile phone signals, GPS devices, public transit usage, and traditional travel surveys. By analyzing these patterns, planners can make smarter, data-driven decisions to create more efficient and accessible systems. 

Mobility data provides transportation planners with a real-world understanding of how, when, and where people travel. This allows them to make data-driven decisions to improve transportation networks. 

Key uses include: 

• Improving traffic flow: identifying sources of congestion to better manage traffic. 

• Optimizing public transit: redesigning routes and schedules to match actual ridership. 

• Making smart investments: pinpointing the best locations for new roads, bike lanes, or transit services. 

• Increasing safety: analyzing travel patterns to identify high-risk areas and prioritize safety projects. 

• Ensuring equitable access: evaluating if the transportation system serves all communities fairly. 

A corridor analysis is a study that focuses on a single transportation route, like a specific highway or a major city street. It's a "deep dive" to understand the unique travel patterns, congestion points, and safety issues within that specific area. 

This focus allows planners to identify targeted solutions, such as optimizing traffic signals, redesigning intersections, or adding a dedicated lane for buses or bikes to improve flow and safety along that route.