National Academies Press: OpenBook

Analysis of Recent Public Transit Ridership Trends (2020)

Chapter: Chapter 4 - Transit Agency Strategies

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Suggested Citation:"Chapter 4 - Transit Agency Strategies." National Academies of Sciences, Engineering, and Medicine. 2020. Analysis of Recent Public Transit Ridership Trends. Washington, DC: The National Academies Press. doi: 10.17226/25635.
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Suggested Citation:"Chapter 4 - Transit Agency Strategies." National Academies of Sciences, Engineering, and Medicine. 2020. Analysis of Recent Public Transit Ridership Trends. Washington, DC: The National Academies Press. doi: 10.17226/25635.
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Suggested Citation:"Chapter 4 - Transit Agency Strategies." National Academies of Sciences, Engineering, and Medicine. 2020. Analysis of Recent Public Transit Ridership Trends. Washington, DC: The National Academies Press. doi: 10.17226/25635.
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Suggested Citation:"Chapter 4 - Transit Agency Strategies." National Academies of Sciences, Engineering, and Medicine. 2020. Analysis of Recent Public Transit Ridership Trends. Washington, DC: The National Academies Press. doi: 10.17226/25635.
×
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Suggested Citation:"Chapter 4 - Transit Agency Strategies." National Academies of Sciences, Engineering, and Medicine. 2020. Analysis of Recent Public Transit Ridership Trends. Washington, DC: The National Academies Press. doi: 10.17226/25635.
×
Page 40
Page 41
Suggested Citation:"Chapter 4 - Transit Agency Strategies." National Academies of Sciences, Engineering, and Medicine. 2020. Analysis of Recent Public Transit Ridership Trends. Washington, DC: The National Academies Press. doi: 10.17226/25635.
×
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Page 42
Suggested Citation:"Chapter 4 - Transit Agency Strategies." National Academies of Sciences, Engineering, and Medicine. 2020. Analysis of Recent Public Transit Ridership Trends. Washington, DC: The National Academies Press. doi: 10.17226/25635.
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Page 42

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31 Ridership Trends Analysis for Dedicated Right-of-way Modes In this section, the analysis of population, zero-vehicle households, and service levels in 2012 and the changes in each from 2012 to 2016 is repeated for dedicated right-of-way transit modes. A critical difference with mixed traffic modes is that regions operating transit in its own lane are typically much larger and there are a limited number of regions operating dedicated right-of-way. Due to the limited number of regions, these graphics are presented as one for all clusters, although clusters are shown using differing symbols. Population – Dedicated Right-Of-Way Modes Figure 12 shows 2012 transit ridership against 2012 population. The point in time scatter plot of transit ridership and population has only a moderately strong relationship overall. Cluster C (mid-size dense), Cluster D (mid-size auto-oriented) and Cluster E (sprawling metro) are compact in the lower left quadrant of the graph. There would be a slight upward trend if not for the three most populated MSAs, Miami, FL, Houston, TX, and Dallas, TX, having only modest transit ridership, and in Houston’s case below average ridership. Regions in Cluster B (dense metro) have much greater ridership for their population, although there too, no clear relationship can be established. Los Angeles, the lone region in Cluster A(Los Angeles) has the most population by far but lower transit ridership than any metro region in Cluster B (dense metro). Figure 13 shows the percentage change in transit ridership against the percentage change in population between 2012 and 2016. The relationship between the change in transit ridership and change in population is also moderately strong. Except for Minneapolis, MN, Seattle, WA, and Houston, TX, which have expanded their rail systems, ridership and population change seems to have a linear and positive relationship across clusters. Cluster C (mid-size dense) regions, which are all Rust Belt regions with the exception of San Juan, Puerto Rico, have lost the most population overall and experienced the greatest transit ridership decline. Cluster D (mid-size auto-oriented) regions gained the most population overall and experienced gains in ridership except for Sacramento, CA, and Portland, OR. Cluster A (Los Angeles), Cluster B (dense metro), and Cluster E (sprawling metro) are spread out along and around the trend line. As for mixed traffic modes, but to a lesser extent, the trend line intercept is clearly negative, meaning for a region with no population growth, ridership would be down.

32 Figure 12: 2012 Transit Ridership vs 2012 Population for Dedicated Right‐of‐way Modes  Figure 13: Percentage change in Ridership vs Percentage Change in Population between 2012 and  2016 for Dedicated Right‐of‐way Modes 

33 Zero-Vehicle Households – Dedicated Right-Of-Way Modes Figure 14 shows 2012 transit ridership against 2012 percentage of zero vehicle households and Figure 15 shows the percentage change in transit ridership against the percentage share of zero vehicle households between 2012 and 2016. Cluster D (mid-size auto-oriented) and Cluster E (sprawling metro) are compact in the lower left quadrant of Figure 14 with low levels of zero- vehicle households and low ridership. Cluster C (mid-size dense) regions have much higher shares of zero-vehicle households, from Pittsburgh, PA, at 11% and San Juan, Puerto Rico, at 16%, but transit ridership is within the ranges of Clusters D (mid-size auto-oriented) and Cluster E (sprawling metro). Cluster B (dense metro) also has high shares of zero-vehicle households, between Washington DC at 10% and Philadelphia, PA, at 13%. All the transit agencies with the greatest ridership are from Cluster B (dense metro). Los Angeles is in between with 8% zero- vehicle households and transit ridership between Cluster B and all the others. As shown in Figure 15, there also is no clear relationship between transit ridership change and zero-vehicle households change between 2012 and 2016. Figure 14: 2012 Transit Ridership vs % Zero Vehicle Households for Dedicated Right‐of‐way Modes 

34 Figure 15: Percent Change in Transit Ridership vs Percent change in Share of Zero Vehicle Households  between 2012 and 2016 for Dedicated Right‐of‐way Modes  Vehicle Revenue Miles – Dedicated Right-Of-Way Modes Transit agencies have relied on dedicated right-of-way modes in recent years to increase transit ridership. Between 2012 and 2016, total transit vehicle revenue miles in the United States have increased by 7.5% for dedicated right-of-way modes. As shown in Boisjoly et al. 2018, the increase in rail service has often come at the expense of bus service. The effort to prioritize rail has allowed dedicated right-of-way modes to keep increasing in ridership between 2012 and 2015, when bus ridership was declining. However, rail ridership has dropped in 2016 and again in 2017. It is therefore important to evaluate the relationship between transit ridership and service levels. Figure 16 shows 2012 transit ridership against 2012 transit service levels and Figure 17 shows the percentage change in transit ridership against the percentage in transit service levels between 2012 and 2016 for dedicated right-of-way modes. Figure 16 shows there is a clear relationship between the two variables at a point a time, which holds true across the spectrum of service levels. The vast majority of regions are grouped in the lower left corner of the figure. These regions are distributed closely around the trend line. Note that while the labels are spread out, the actual points follow the line closely. Ridership in regions with greater dedicated right-of-way transit service is distributed along the same line. The only outliers are Boston, MA, which has slightly more transit ridership than would be expected for its service level and San Francisco, CA, which has slightly less transit ridership than would be

35 expected for its service level. This trend is consistent with mixed traffic modes, where transit ridership is also closely related to transit service levels. Figure 17a would show strong relationship if it were not for a few outliers such as Houston, TX. While Houston, TX, did increase ridership by 62%, these gains are modest in comparison to the 265% increase in transit service. Seattle, WA and Minneapolis, MN had much greater gains in ridership for more modest increases in service. By zooming in without these three cities in Figure b, the relationship between change in transit ridership and change in transit service levels is easier to see as far stronger for dedicated right-of-way modes than for mixed traffic modes. Figure 16: 2012 Transit Ridership vs 2012 Transit Service Levels for Dedicated Right‐of‐way Modes 

36 Figure 17a: Percent Change in Transit Ridership vs Percent Change in Transit Service between 2012 and  2016 for Dedicated Right‐of‐way Modes  Figure 17b:  Zoomed  in version of Percent Change  in Transit Ridership vs Percent Change  in Transit  Service between 2012 and 2016 for Dedicated Right‐of‐way Modes 

37 Summary Through this analysis of trends, we were able to evaluate the relationship between transit ridership and three of its main determinants both at a point in time and as a change over time (2012 to 2016). Our analysis confirmed that population and service levels, which are typically associated with transit ridership, explain a large portion of the variation in transit ridership among peer transit agencies. We found a close relationship between the change in the factors and the change in transit ridership for dedicated right-of-way modes. However, this correlation does not hold for all factors for mixed traffic modes. We found differing trends for population and ridership for dedicated versus mixed traffic modes. There is a clear relationship between 2012 ridership and population for mixed traffic modes, but it is more moderate for dedicated right-of-way modes. Conversely, there is a clearer relationship between transit ridership change and population change between 2012 and 2016 for dedicated right-of-way modes, but not for mixed traffic modes. While both dedicated and mixed traffic modes had a strong relationship between ridership and service levels in 2012, the change in transit ridership was much more closely associated with change in transit service levels for dedicated right-of-way than for mixed traffic modes, especially in larger metropolitan areas. Population affects transit ridership both directly and through the quantity of transit service available, both in terms of operations and infrastructure. Our results suggest that the direct effect (population on ridership) is stronger on dedicated right-of-way modes and that the indirect effect (service levels on ridership) is stronger on mixed traffic modes. The point-in-time transit ridership is more sensitive to population for mixed traffic modes because these modes can be expanded incrementally. Dedicated right-of-way modes, however, are the product of deliberate, often regional policy decisions that require long-term planning and therefore may not necessarily follow population linearly. Conversely, the change in transit ridership has a stronger relationship with the change in population for dedicated right-of-way modes because these modes provide a fast and reliable service, which is more competitive with private vehicles in congested cities. While there is a clear relationship between transit ridership and transit service levels at a point in time for both mixed traffic and dedicated right-of-way modes, the change in ridership is much more closely associated with change in service levels for dedicated right-of-way modes than for mixed traffic modes. With superior travel speed and reliability, dedicated right-of-way modes can attract patrons who also have access to private vehicles. Therefore, ridership on these modes may be more sensitive to service levels. A significant departure from previous findings in the literature is that neither population nor transit service levels explain the change in transit ridership between 2012 and 2016 for mixed traffic modes. Transit regions overall gained both population and service levels while still losing transit ridership. Clearly, new factors are at work influencing transit ridership beyond the traditional factors of population, zero-vehicle households, and service levels.

Next: Chapter 5 - Case Studies »
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Transit ridership is down across all modes except commuter rail and demand response. Bus ridership is down the most in mid-size cities (populations of 200,000 – 500,000), and, after six years of consecutive decline, it is at its lowest point overall since the 1970s.

The TRB Transit Cooperative Research Program's TCRP Research Report 209: Analysis of Recent Public Transit Ridership Trends presents a current snapshot of public transit ridership trends in the U.S. on bus and rail services in urban and suburban areas, focusing on what has changed in the past several years. It also explores and presents strategies that transit agencies are considering and using for all transit modes in response to changes in ridership.

Ten case studies are included to better understand individual strategies transit agencies are using to mitigate ridership losses and increase ridership overall. Seven of the 10 transit agencies investigated in the case studies followed the trend, with ridership increases between 2012 and 2015 followed by steady decreases in ridership. Generally, on-time performance has been improving, although it is not causing transit ridership to increase.

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