Incorporating Multimodal Network Connectivity Measures into Planning Processes

April 4, 2018

Photo Credit: Nancy Pierce, 2017

Why does multimodal network connectivity matter?

In the transportation planning and design industry, we celebrate the opening of a new bike lane, sidewalk, or freeway over-crossing. However, it’s important to take a step back and think about what the construction of that facility means in practical terms for daily users. One compelling way to understand the impact of new facilities is through its impact to network connectivity.

Connectivity of our bicycle and pedestrian networks is vital for improved safety, health, and access for users of all ages and abilities. A short segment of protected bike lanes is a great step toward improving the bicycling environment in a city, but the utility of this facility is further realized when it is one element of a complete network leading residents from their homes to their jobs and other destinations without major barriers.

A focus on connectivity helps answer the question: “Can I get to where I want to go safely in whatever way I choose?”

How we understand connectivity is likely to vary by location, development context, data availability, and network vision. Therefore, quality of connectivity should be defined by the following criteria:

  • Network completeness
  • Network density
  • Route directness
  • Access to destinations
  • Network quality

Credit: FHWA Measuring Multimodal Network Connectivity guidebook.

How is multimodal network connectivity measured?

The Federal Highway Administration (FHWA) released the Measuring Multimodal Network Connectivity guidebook to help planners and analysts better understand, define, and measure network connectivity for active modes. This guidebook builds on the 2016 Guidebook for Developing Pedestrian and Bicycle Performance Measures and details the application of connectivity measures to assess network condition, identify gaps, prioritize investments, and measure network performance over time.

Alta consulted on the technical analysis methods, peer city interviews, and guide layout, and synthesizing and presenting the full range of options available for measuring network connectivity and tracking change over time. By addressing both bicycle and pedestrian network connectivity, the guide serves as a source of inspiration to help practitioners conduct meaningful analysis and provide thoughtful and relatable reports from that analysis for use by the public and decision makers.

Credit: FHWA Measuring Multimodal Network Connectivity guidebook.

The guide walks planners and analysts through the a five-step process of selecting a connectivity analysis method, running the analysis, and presenting the results, the guide serves as a connectivity analysis ‘how-to’ guide. Following that, the guide provides a toolbox of resources, including methods and emerging techniques. In this section, peer examples and considerations for application are also highlighted.

Credit: FHWA Measuring Multimodal Network Connectivity guidebook.

The five-step process detailed in the guide walks planners through the process of selecting measures that are both relevant and informative for their study area. Step one leads practitioners through a series of questions to identify the purpose of the analysis, the scale, and other items that may inform the specific method. Step two outlines the types of measures available and helps to connect the answers of step one with an analysis method. Steps three and four focus on the acquisition of data and completing the analysis, while step five speaks to the visualization and summary of the results.

These guidelines are applicable across geographic scale and level of data available. Locations that are large in scale or have limited data can still measure connectivity, while more developed areas with detailed data provide opportunities for digging deeper or overlaying results of complementary analyses. Many of the resources included in the the guide include considerations for applying the measure in small town or rural locations.

Beyond the five-step analysis process, the guide features fact sheets on the connectivity analysis methods and a series of emerging measures. Connectivity analysis methods refer to category of methods, such as network completeness, network density, or route completeness. Connectivity measures reference the specific analysis technique employed, such as bicycle level of traffic stress, bicycle route quality index, or pedestrian index of the environment. The guide also features real world data analysis projects to better understand the application of network connectivity measures to real world practice.

Credit: FHWA Measuring Multimodal Network Connectivity guidebook.

What is the impact on the future of transportation planning?

The results generated by connectivity analyses enhance accountability by helping decision makers weigh the potential outcomes of planned multimodal infrastructure investments. By using multimodal measurements to inform the iterative, comprehensive process of planning and implementation, transportation agencies can more successfully create and improve mobility options and systems. Connectivity measures can lead to filling gaps and addressing barriers in the transportation network, to increase safety for all users and improve access to jobs, schools, economic centers, and other destinations.

For example, connectivity measures have been utilized in Oakland, CA to frame the discussion around how the City’s Bike Plan will evaluate roadways currently available for bicycling vs. roadways that people would like to bike on.

When approaching connectivity analysis, be sure to articulate a clear goal for what you intend to measure before you select your tools and select methods and measures that are appropriate for the study area. Be prepared to conduct a data validation exercise and test measures before committing. Overlay the data with multiple sources of information to understand the question from many perspectives (e.g., health, safety and accessibility). By following these best practices, you can achieve a successful and context-sensitive analysis.

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