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Analysis of Recent Public Transit Ridership Trends (2019)

Chapter: Chapter 3: National Ridership Trends

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Suggested Citation:"Chapter 3: National Ridership Trends." National Academies of Sciences, Engineering, and Medicine. 2019. Analysis of Recent Public Transit Ridership Trends. Washington, DC: The National Academies Press. doi: 10.17226/25635.
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Suggested Citation:"Chapter 3: National Ridership Trends." National Academies of Sciences, Engineering, and Medicine. 2019. Analysis of Recent Public Transit Ridership Trends. Washington, DC: The National Academies Press. doi: 10.17226/25635.
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Suggested Citation:"Chapter 3: National Ridership Trends." National Academies of Sciences, Engineering, and Medicine. 2019. Analysis of Recent Public Transit Ridership Trends. Washington, DC: The National Academies Press. doi: 10.17226/25635.
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Suggested Citation:"Chapter 3: National Ridership Trends." National Academies of Sciences, Engineering, and Medicine. 2019. Analysis of Recent Public Transit Ridership Trends. Washington, DC: The National Academies Press. doi: 10.17226/25635.
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Suggested Citation:"Chapter 3: National Ridership Trends." National Academies of Sciences, Engineering, and Medicine. 2019. Analysis of Recent Public Transit Ridership Trends. Washington, DC: The National Academies Press. doi: 10.17226/25635.
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Suggested Citation:"Chapter 3: National Ridership Trends." National Academies of Sciences, Engineering, and Medicine. 2019. Analysis of Recent Public Transit Ridership Trends. Washington, DC: The National Academies Press. doi: 10.17226/25635.
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Suggested Citation:"Chapter 3: National Ridership Trends." National Academies of Sciences, Engineering, and Medicine. 2019. Analysis of Recent Public Transit Ridership Trends. Washington, DC: The National Academies Press. doi: 10.17226/25635.
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Suggested Citation:"Chapter 3: National Ridership Trends." National Academies of Sciences, Engineering, and Medicine. 2019. Analysis of Recent Public Transit Ridership Trends. Washington, DC: The National Academies Press. doi: 10.17226/25635.
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Suggested Citation:"Chapter 3: National Ridership Trends." National Academies of Sciences, Engineering, and Medicine. 2019. Analysis of Recent Public Transit Ridership Trends. Washington, DC: The National Academies Press. doi: 10.17226/25635.
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Suggested Citation:"Chapter 3: National Ridership Trends." National Academies of Sciences, Engineering, and Medicine. 2019. Analysis of Recent Public Transit Ridership Trends. Washington, DC: The National Academies Press. doi: 10.17226/25635.
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Suggested Citation:"Chapter 3: National Ridership Trends." National Academies of Sciences, Engineering, and Medicine. 2019. Analysis of Recent Public Transit Ridership Trends. Washington, DC: The National Academies Press. doi: 10.17226/25635.
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Suggested Citation:"Chapter 3: National Ridership Trends." National Academies of Sciences, Engineering, and Medicine. 2019. Analysis of Recent Public Transit Ridership Trends. Washington, DC: The National Academies Press. doi: 10.17226/25635.
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Suggested Citation:"Chapter 3: National Ridership Trends." National Academies of Sciences, Engineering, and Medicine. 2019. Analysis of Recent Public Transit Ridership Trends. Washington, DC: The National Academies Press. doi: 10.17226/25635.
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Suggested Citation:"Chapter 3: National Ridership Trends." National Academies of Sciences, Engineering, and Medicine. 2019. Analysis of Recent Public Transit Ridership Trends. Washington, DC: The National Academies Press. doi: 10.17226/25635.
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Suggested Citation:"Chapter 3: National Ridership Trends." National Academies of Sciences, Engineering, and Medicine. 2019. Analysis of Recent Public Transit Ridership Trends. Washington, DC: The National Academies Press. doi: 10.17226/25635.
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Suggested Citation:"Chapter 3: National Ridership Trends." National Academies of Sciences, Engineering, and Medicine. 2019. Analysis of Recent Public Transit Ridership Trends. Washington, DC: The National Academies Press. doi: 10.17226/25635.
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Suggested Citation:"Chapter 3: National Ridership Trends." National Academies of Sciences, Engineering, and Medicine. 2019. Analysis of Recent Public Transit Ridership Trends. Washington, DC: The National Academies Press. doi: 10.17226/25635.
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Suggested Citation:"Chapter 3: National Ridership Trends." National Academies of Sciences, Engineering, and Medicine. 2019. Analysis of Recent Public Transit Ridership Trends. Washington, DC: The National Academies Press. doi: 10.17226/25635.
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Suggested Citation:"Chapter 3: National Ridership Trends." National Academies of Sciences, Engineering, and Medicine. 2019. Analysis of Recent Public Transit Ridership Trends. Washington, DC: The National Academies Press. doi: 10.17226/25635.
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Suggested Citation:"Chapter 3: National Ridership Trends." National Academies of Sciences, Engineering, and Medicine. 2019. Analysis of Recent Public Transit Ridership Trends. Washington, DC: The National Academies Press. doi: 10.17226/25635.
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17 Chapter 3: National Ridership Trends Based on the literature and industry knowledge, the major factors traditionally influencing transit ridership are changes in service levels, population, and transit-dependent population. For each mixed traffic and dedicated right-of-way cluster, a trend analysis was performed to examine the relationship between transit ridership and these three factors. In all cases, transit ridership was defined by unlinked passenger trips (UPT). Service levels are represented by transit vehicle revenue miles (VRM). Population is represented by one-year American Community Survey (ACS) estimates. Transit-dependent population is represented by zero-vehicle households from the ACS. Population, zero vehicle households, and transit service levels were plotted against transit ridership to determine whether they were similar in magnitude and direction. In each case, the relationship between transit ridership and its determinants is first evaluated using only 2012 data. This point-in-time analysis helps understand the steady-state effects each factor has on transit ridership after decades of interaction. Of course, the causal relationship goes both ways as transit ridership could directly or indirectly affect population size, share of zero-vehicle households, and transit service levels. In the second part of the analysis, the percentage change in transit ridership is compared to the percentage change in each explanatory factor between 2012 and 2016. This analysis helps understand their relationship in the short term. Exploring how factors change together within a four year period can provide important insights on the potential causal relationships that have been driving ridership down. In all of the figures, regions are abbreviated with three letter codes, shown in Appendix C. Ridership Trends Analysis for Mixed Traffic Modes Using the Clusters 1 through 5 as explained in the methodology section, trends in transit ridership versus population, zero-vehicle households, and service levels at a point in time as well as trends in change in transit ridership versus changes in these factors are described in the following section for the mixed traffic modes. For more information about which regions are in each cluster, see Appendix C.

18 Population – Mixed Traffic Modes While transit ridership is declining nationally, the population of urban areas overall is at its highest point in history. Although suburbs have been growing at a faster pace in recent years, urban cores have increased in population every year since 2006 (Frey 2018). These trends make the recent decline in transit ridership even more alarming because they indicate that ridership per capita has been falling at an even faster rate than ridership. One potential explanation for the transit ridership decline is that population growth has been concentrated in sprawling metropolitan areas, particularly in the Sun Belt, while denser cities, particularly in the Rust Belt, have lost population. According to a study by Driscoll, et al (2017), urban migration away from cities with strong transit markets may be a leading cause of the ridership decline. The relationship between population and transit ridership in each cluster is first evaluated in 2012 as shown in Figure . Clusters 1 and 5, which contain transit supportive regions, population explains a large part of the variation in ridership among regions. In Cluster 1, which contains mid-sized MSAs, only San Figure  6:  2012  Transit Ridership  vs  Population  for Mixed  Traffic  Modes 

19 Juan, Detroit, MI, and Honolulu, HI, deviate from the linear relationship between population and ridership. In Cluster 5, which contains large MSAs, the relationship is also clear, although heavily influenced by Los Angeles and Chicago. Clusters 2, 3, and 4, which are all car-oriented MSAs have diverging trends. While Cluster 2, which contains mid-sized MSAs, shows a clear positive relationship between population and ridership, Cluster 4, which contains larger MSA, does not. Cluster 3 (small towns) seems to be showing a trend, but it is more difficult to identify because a large subset of the cluster is highly homogeneous. The 62% of regions that have less than a million in population and less than 5 million unlinked transit passenger trips seem to be uniformly distributed. However, the ridership of satellite regions orbiting around this core group tends to increase with population. Overall, the trend between population and transit ridership is quite strong in every cluster except for Cluster 4, sprawling metropolises. Figure  6  (continued):  2012  Transit  Ridership  vs  Population  for  Mixed Traffic Modes 

20 Population Change – Mixed Traffic Modes Figure 7: Change in Transit Ridership vs Change in Population for  Mixed Traffic Modes  In order to understand how the change in ridership between 2012 and 2016 relates to population, Figure shows ridership change against population change in percentage. Despite the strong trends in Figure , the short term trends in Figure are mixed. In some cases, metro regions with dense urban cores lost the most ridership and gained the least population. Areas in Cluster 1 (mid-sized transit-oriented) lost the most population with 20 out of 47 metro regions experiencing a net population loss. Metro regions in Cluster 5 (dense metro) all grew by less than 7%. In both clusters, however, transit ridership change seems unaffected by the decline in population. Clusters 2 and 3 (mid- sized auto-oriented and sprawling small towns) have no clear trend either. Metro regions are scattered around a negative change in ridership that is not affected by changes in population. In both clusters, the trend line is flat, which indicates that the change in transit ridership is uncorrelated with change in population. Despite population growing by more than 4% for every MSA in Cluster 4 (sprawling metro), there is also no relationship.

21 Figure  7  (continued):  Change  in  Transit  Ridership  vs  Change  in  Population for Mixed Traffic Modes  Overall, there is a strong relationship between transit ridership and population but almost none between ridership change and population change. In every cluster except for Cluster 4, population explains a large portion of the variation in ridership. It is interesting to note that Clusters 2, 3, and 4 have gained the most population perhaps at the expense of Clusters 1, and 5. However, if population change was a significant factor of transit ridership for mixed traffic modes, then these trends would be reflected within each cluster in Figure 5, particularly Clusters 1 and 5. We therefore conclude that although population is an indication of transit ridership, other factors than recent population changes are having an overwhelming effect on transit ridership change. It may be that change within a population, such as demographics, are having a larger impact than overall population change.

22 Zero Vehicle Households – Mixed Traffic Modes The proportion of households with zero vehicles is an important indicator of transit ridership because it reflects the medium to long term propensity of individuals to ride public transportation. Zero vehicle households can be delineated into two groups :  car-free households, where residents choose to live without a car, and  car-less households, where residents lack access to a vehicle for physical or financial reasons. A recent study based on 2012 California Household Travel Survey found that 79% of zero vehicle households are car-less (Brown 2017). In general, the total lack of cars in a household, is more closely associated with constraint than choice. These households, therefore constitute a population sometimes refered to as captive transit riders, because they have no other accessible means of transportation. Larger households can also be one-vehicle households, thus necessitating travel by other means for most members of the household, however readily available data to quantify these households in every region is more difficult to obtain. Therefore, zero- vehicle households is used as a surrogate for those without automobile access. Figure 8: 2012 Transit Ridership vs 2012 Percent of Zero Vehicle  Households 

23 The proportion of zero-car households should also be understood in the context of density. Density determines whether people can live without a car and still have access to alternative transportation options. While Clusters 1 and 2 have the same proportion of population living in transit supportive density, the proportion of households without cars is almost twice as great in Cluster 1. The same can be said about Cluster 4 and 5, which have similar densities but very different proportions of zero- car households. These numbers suggest that regions in clusters 1 and 5 have large proportions of transit- dependent households. Figure shows the percent of zero vehicle households against unlinked passsenger trips in each cluster. In Clusters 1, 2, and 3 (lower population MSAs), the regions with the greatest transit ridership have medium and in some cases low proportions of zero vehicle households. These regions include Honolulu, HI, and Baltimore, MD, in Figure 8 (continued): 2012 Transit Ridership vs 2012 Percent of Zero  Vehicle Households  Cluster 1, Orlando, FL, and Austin, TX, in Cluster 2, and St. Louis, MO, and Durham, NC, in Cluster 3 which all have far greater levels of ridership than the average in their cluster, yet have relatively low zero-vehicle households compared to other cities. Besides these outliers, there is a slight positive trend in all three clusters. However, it is apparent that the proportion of zero vehicle households accounts for a small share of variation in transit ridership. There is also not a strong relationship between transit ridership and the proportion of zero vehicle households in Clusters 4 and 5 (higher population MSAs), although the more car-oriented large metropolitan areas in Cluster 4 have a slightly stronger relationship than the more transit-oriented large metropolitan areas in Cluster 5.

24 Change in Zero Vehicle Households – Mixed Traffic Modes Figure  9:  Change  in  Transit Ridership  vs  Change  in  Zero Vehicle  Households for Mixed Traffic Modes  While the proportion of zero vehicle households at a point in time reflects the steady-state of economic, land-use, and transportation forces, the increase in car ownership has been identified as a major cause of transit ridership decline. A study from the Southern California Association of Governments suggested that the decrease in zero-vehicle households was the primary cause of transit ridership decline in the Los Angeles greater area (Manville et al. 2018). To evaluate this trend at the national level, Figure shows the change in transit ridership against the absolute change in percentage of households with zero vehicles between 2012 and 2016. Unlike the 2012 analysis, there is a relationship between the change in transit ridership and the change in zero vehicle households in Cluster 4 (sprawling metros) and Cluster 5 (dense metros), but not in Clusters 1, 2, and 3 (lower population MSAs). The relationship between the change in transit ridership and zero vehicle households is rather flat in Clusters 1, 2, and 3, with regions spread widely and almost symetrically around.

25 Figure 9 (continued): Change in Transit Ridership vs Change in Zero  Vehicle Households for Mixed Traffic Modes  In the large metro areas, Clusters 4, and 5, however, the change in zero vehicle households is associated with change in transit ridership. In Cluster 4, only Las Vegas, NV, increased in proportion of zero-vehicle households, and in Cluster 5, Seattle, WA, was the only region not to decline in zero vehicle households except for Boston, MA, which did not substantially change. Overall, the decline in transit ridership is therefore connected with the decline in proportion of zero-vehicle households in large metro areas (Clusters 4 and 5) but not in smaller ones (Clusters 1 to 3).

26 Transit Service – Mixed Traffic Modes The amount of service provided is one of the few levers available for transit agencies to affect ridership. It is therefore important to evaluate the relationship between ridership and service levels both at a point in time and as a change over time. In order to better understand the base-case relationship between transit ridership and service levels, Figure shows 2012 transit ridership against 2012 transit service levels in each cluster. In every cluster, the relationship between transit ridership and transit service levels is both clear and strong. In clusters 1 and 2, transit service levels explain almost all of the variation in transit ridership. In both clusters, transit service levels span a wide spectrum with the largest regions having 50 times more transit service than the smallest ones. The relationship between transit ridership and transit service levels holds true at all levels of transit service levels. It is interesting to note that the trend line has slope equal to 3 in Cluster 1 and slope equal to 2 in Cluster 2. These results suggest that each marginal vehicle revenue mile is associated with more transit ridership in mid-sized transit-oriented Rust Belt regions than in car-oriented, and for the most part Sun Belt, regions of similar sizes and densities. Figure 10: 2012 Transit Ridership vs 2012 Transit Service Levels for  Mixed Traffic Modes 

27 In cluster 3, the trend is also clear, but there is not as much spread in transit service levels as in Clusters 1 and 2. There are 9 regions with much more transit vehicle revenue miles and transit ridership than the rest of the cluster. These are the same regions that had overwhelming transit ridership for their proportion of zero vehicle households in the last analysis. All other regions within the cluster are compact between zero and three million transit vehicle revenue miles. The group of compact regions shows a relationship between transit ridership and transit service levels, which extends to outlying regions with far greater transit service levels, thereby confirming the trend. Although there is also a clear relationship between transit ridership and transit service levels in Clusters 4 and 5, it does not explain as much of the variation in transit ridership. This is especially true in Cluster 4 with Las Vegas, NV, and Dallas, TX, for which transit service levels do not explain transit ridership well. Figure  10  (continued):  2012  Transit  Ridership  vs  2012  Transit  Service Levels for Mixed Traffic Modes  Besides Boston, MA, all regions in Cluster 5 provided more transit service in 2012 than even the largest region in Cluster 4, which was Houston. It is also worth noting that the slope of the relationship between transit ridership and transit service levels is three times greater for Cluster 5 than for Cluster 4. These results indicate that transit service levels contribute far more ridership in the large transit-oriented metropolitan areas of Cluster 5 than in more car-oriented sun-belt regions of Cluster 4. Figure shows that there is a strong relationship between transit ridership and service levels at a point in time. These results suggest that transit service levels may be a strong influencer of transit ridership, but it is important to also evaluate the influence of changes in transit service levels in the next section to explain the ridership decline since 2012.

28 Change in Transit Service Levels – Mixed Traffic Modes Figure 11: Change in Transit Ridership vs Change in Transit Service  Levels for Mixed Traffic Mode  As shown in Figure 11, there is a definite relationship between change in transit ridership and change in transit service levels in Clusters 1 and 2, and to a lesser extent, Cluster 3.  In Cluster 1, the spread around the trend line is wide, but consistent. Every region where ridership has grown has increased transit service levels.  In Cluster 2, the relationship explains a large part of the variation in transit ridership. Regions with the greatest ridership growth have increased transit service levels the most and regions with the greatest ridership fall have reduced transit service levels the most.  In Cluster 3, several regions where both transit ridership and transit service levels increased dramatically between 2012 and 2016 drive the relationship. There are, however, outliers such as Chico, CA, Huntsville, AL, and Elizabethtown, KY, where ridership increased despite slight reductions in transit service levels and regions such as Fayetteville, MO, Baton Rouge, LA, and Port St. Lucie, FL, where vehicle revenue miles increased by more than 75% but transit ridership did not substantially increase.

29 Figure 11  (continued): Change  in  Transit Ridership  vs Change  in  Transit Service Levels for Mixed Traffic Mode  Besides the strength of the relationship, the intercepts are also interesting. The intercept of the trend lines is the amount of ridership change that should be expected if transit service levels had not changed. The intercept is -11% for Cluster 1, - 9% for Cluster 2, and -8% in Cluster 3. These results indicate that small to mid-sized regions that didn’t change transit service levels between 2012 and 2016 should expect 8-10% loss in ridership. Although there is a definite relationship between the change in transit ridership and the change in transit service levels, there is also a systematic effect driving transit ridership down in transit agencies across clusters irrespective of service levels. In Clusters 4 and 5, there is no discernable relationship between the change in transit ridership and the change in transit service levels. If anything, the trend in Cluster 4 is pointing downward with San Bernardino, CA, which increased vehicle revenue miles by 28%, still losing 11% of ridership. Regions in cluster 5 exhibit no relationship between change in transit ridership and transit service levels. Boston cut service by 3% and increased ridership by 5.5%, while Washington D.C. increased service by 7.9% and lost 5% of ridership. As for small and medium-sized regions, large metro areas lost transit ridership systematically with an average drop of 1% in Cluster 4 and 4% in Cluster 5. However, unlike small and mid-sized regions, the change in transit service levels was not a significant factor of transit ridership change between 2012 and 2016 in large metro areas. Overall, Figure 11 shows that transit service levels were not responsible for the decline in ridership in mixed traffic modes between 2012 and 2016. In every cluster, an overwhelming majority of regions both increased transit service and lost ridership. The relationship between transit ridership and transit service levels for small transit agencies means that small transit agencies were able to minimize the

30 decline in ridership and in some cases even yield modest increases, but at the cost of increases in transit service. In larger regions, changes in transit ridership seem completely uncorrelated with increasing transit service levels.

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.

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Analysis of Recent Public Transit Ridership Trends Get This Book
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Transit ridership across the United States has declined for six straight years. Bus ridership, which has declined more than other transit services, is now at the lowest point since 1965. Rail ridership, with the exception of commuter rail, has also declined, and commuter rail ridership has recently leveled off.

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. Nearly every transit agency investigated in the case studies had 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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