Connecting Sacramento: A Trip-Making and Accessibility Study

Accessibility metrics tend to be reported as cumulative metrics, which are straightforward to calculate and communicate. However, more meaningful metrics assign different weights (utility) to different destinations based on travel time, instead of assigning arbitrary importance to a specific threshold.

Key takeaway:

  • Accessibility metrics tend to be reported as cumulative metrics (i.e., jobs within 30 minutes), which are straightforward to calculate and communicate. However, more meaningful metrics assign different weights (utility) to different destinations based on travel time, instead of assigning arbitrary importance to a specific threshold (i.e., 30 minutes).



  • Connecting Sacramento measures accessibility in terms of travel time and travel time-based utility, which can represent access to various destinations, such as stores, restaurants, parks, schools, etc. It additionally measures overall transit accessibility in terms of access to jobs by transit during the morning peak period, which includes the time needed to walk to/from transit stations. This is a departure from conventional approaches to measure accessibility based on vehicle speeds and road capacity, which ignore fundamental questions about where people are going and how easy it is for them to get there, thereby making it difficult to gather information about people who don’t drive and walk, bike, and/or take public transit instead. Accessibility metrics enable transportation planners to prioritize last-mile connections and to quantify the impacts of accessibility improvements. They are important in conducting equity analyses to ensure that transportation-related decisions benefit populations that could benefit most, such as low-income households, people of color, children, the elderly, and people with disabilities.
  • Transportation planners can leverage technological improvements and big data to promote active, sustainable transportation. To avoid being overwhelmed, the Connecting Sacramento researchers recommend:
  1. Familiarizing oneself with and considering a range of data sources. Big data increases the available options, replacing some and complementing others.
  2. Asking a specific question to retrieve and interpret relevant data for a specific challenge or problem.
  3. Enlisting the right people with the appropriate knowledge and skillsets (i.e., GIS, data analysis, etc.) to help interpret the data. Data is useful to the extent that it makes sense and is interpretable.
  4. Aggregating big data appropriately to leverage its full potential. Reliability of findings increases with sample size, so it’s important to be aware of limitations based on sample sizes.



  • Accessibility metrics can inform decision-making by engaging multiple stakeholders, creating community-oriented transportation plans, increasing and improving public transit, encouraging and enabling more multi-modal transit, promoting transportation equity, and shaping private development. However, just like any performance metrics, accessibility metrics must be formally integrated into decision-making processes to be effective. This underscores the role of governance in enabling and supporting transit accessibility.



  • This groundbreaking Connecting Sacramento study explores the utility of new accessibility metrics and trip-making data and employs them to: (1) better understand how Sacramento’s light rail transit is being used and (2) identify “first- and last-mile connections” to improve and increase transit ridership.   
  • The researchers used the Sugar Access package by Citilabs for new accessibility metrics based on travel time, which enabled them to ask questions like: How accessible are transit stations by walking? How many jobs are reasonably accessible by transit? How well does transit enable people to access their daily needs?
  • To identifying the most critical missing last-mile connections, Connecting Sacramento researchers calculated:
  1. Station utility based on actual walking distance to the nearest station.
  2. Potential utility improvement by determining the most feasible direct connection to the nearest station.
  3. An impact score that accounts for both the potential utility improvement and the number of households and jobs that it would affect.
  • The researchers incorporated two trip-making data sources:
    • Cell phone data from Teralytics that provides information on light rail trips, combined with transit routes and schedules from General Transit Feed Specification data.
    • GPS data from StreetLight Data that provides information on vehicle trips, bicycle trips, and pedestrian trips.
    • The researchers combined accessibility metrics and trip-making data, i.e., vehicle trips, light rail trips and mode share, pedestrian trips to/from light rail stations, etc., and explored their practical applications.


McCahill, C. and Sundquist, E. (2017). Connecting Sacramento: A Trip-Making and Accessibility Study. State Smart Transportation Initiative with the Lincoln Institute of Land Policy.

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