National Academies Press: OpenBook

Analysis of Recent Public Transit Ridership Trends (2020)

Chapter:Chapter 4 - Transit Agency Strategies

« Previous: Chapter 3 - National Ridership Trends
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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.
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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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36 A national trend of falling transit ridership has had many wondering what can be done. Many transit agencies across the country have undertaken campaigns to win back riders. From simple boosts in service to complex partnerships, these transit agencies and the cities they serve are hoping to avoid the national trend. No one solution can work as a catch-all because operating conditions between transit agencies can vary widely and ridership has many com- plexities. However, lessons learned from the various strategies attempted can be important for other transit agencies to understand how and if to implement a strategy in their area. Service Levels Transit agencies have long known that ridership is sensitive to the levels of service, reli- ability, and fares. Recently, levels of transit service have been identified as the main reason for the national decline in ridership both in the literature and in the news. Many experts have pointed to transit agencies that have increased service and gained the most ridership as examples. Seattle stands out with a 1.3% increase in bus ridership and a 74% increase in light rail rider ship between 2014 and 2016 based on an analysis of National Transit Database (NTD) data. Service additions likely played a considerable role in this growth, with bus and light rail vehicle revenue hours increasing 9% and 42% over the same period, respectively. How- ever, the Seattle region’s ridership growth cannot be entirely attributed to added service. According to Curbed, Seattle saw a nearly 9% drop in single-occupancy vehicle commuting from 2005 to 2015, the highest drop among major U.S. cities (Keeley, 2016). A dedicated transit mall, strategic small projects to speed up buses, and quick political maneuvering to come up with funding before shortfalls have all helped Seattle stay on top of ridership changes (Small, 2017). As shown in Chapter 3, change in transit service levels, in terms of vehicle revenue miles, only explains a portion of the changes in transit ridership levels and the portion it explains is dependent on mixed traffic versus dedicated ROW and the size and density of the region. While transit service levels explain some of the decline in mixed traffic transit ridership in smaller regions (Clusters 1, 2, and 3), they are not correlated with ridership change in larger regions (Clusters 4 and 5). These trends indicate that the decline in transit ridership, espe- cially in large transit agencies, is caused by some other factors that are occurring at a more disaggregate level. It is therefore important to analyze the other factors and strategies that may be affecting the ridership impact of service provided by transit agencies. The remainder of this chapter describes the initiatives by transit agencies to increase ridership independently of service levels. C H A P T E R 4 Transit Agency Strategies

Transit Agency Strategies 37 Bus Network Restructuring Recent efforts to increase transit ridership have consisted in restructuring bus networks to pri- oritize service concentration over coverage. Bus network redesigns in locations such as Houston, TX, have prioritized frequency of service in core corridors over long and circuitous routes with lower frequencies. The theory behind these efforts is that there is an inherent trade-off between service coverage and frequency of service (Walker, 2012). Therefore these network redesigns reflect a shift in policy goals from spreading service to reach the few and concentrating it to attract the many. In August 2015, Houston’s Metropolitan Transit Authority (MTA) of Harris County redesigned their bus network, increasing high-frequency bus routes, while cutting lower-frequency routes. The system was redesigned for the first time since the 1980s, with some routes unchanged since the 1920s (Lewis, 2015). Figure 18 shows the bus network before and after the redesign. The MTA’s goal was to simplify bus routes and improve frequency to reach a higher propor- tion of residents. However, the Houston press reported that low-income neighborhoods lost 12 routes whereas non-low-income neighborhoods gained three (Flynn, 2015). Called the “hottest trend in transit” by Governing Mag at the end of 2017, bus network restructuring is being considered by transit agencies across the nation. The Los Angeles Metro announced in May 2017 the start of a three-year process to restructure the bus network in response to a 20% drop in ridership over three years (Hymon, 2017). The Dallas Area Rapid Transit (Schmitt, 2017), the Southeastern Pennsylvania Transportation Authority (Laughlin, 2017), and the Washington Metro Area Transit Authority (Powers, 2017) are exploring similar bus network redesigns. Omaha Metro Area Transit, Austin’s Capital Metro, and Columbus’s Central Ohio Transit Authority (COTA) have followed suit with their own network redesigns. Seattle’s King County Metro went through a similar process, albeit over the course of several years. Metropolitan Atlanta Rapid Transit Authority (MARTA) commissioned a Comprehen- sive Operations Analysis study, which also recommended concentrating bus service on core corridors (Parsons Brinckerhoff, 2016). In reducing their coverage, however, MARTA has faced stiff resistance from residents who rely on bus service as their only mode of transportation (Abubey, 2017). Figure 18. Houston Metro before and after frequent network redesign map.

38 Analysis of Recent Public Transit Ridership Trends One potential contributing factor not yet addressed in the literature or in the press is that these bus network redesigns were accompanied by net increases in bus operating budgets, likely to add substantial service. There is a need for research to parse the contributing factors of transit ridership and evaluate the singular impact of prioritizing concentration over coverage. Mode Integration In recent years, transit agencies have started changing their bus networks to improve the connectivity among modes. This trend is analogous to network redesigns described above but distinct because they do not necessarily prioritize service concentration over coverage. Mode integration is the reorienting of transit service to improve links among modes of transit, such as rail and bus. It is usually done in preparation for service expansion of new high-capacity transit lines. In Minneapolis–St. Paul, MN, and in Baltimore, MD, where new light rail and BRT lines were added, the bus networks were readjusted accordingly. The objective was to facilitate con- nections among modes. In Minneapolis, parts of the bus network were restructured to serve a new light rail line. In preparation for the opening of the Metro Green Line in June 2014, surrounding bus routes were routed and timed to transfer seamlessly (Metro Transit, 2012). Metro’s predictions were that around 40% of Green Line riders would connect to the bus system, and the network needed realignment to best facilitate these connections. The process took around two years to plan and implement. In addition, a new rapid bus service was planned and opened in 2016 with a direct connection to the Green Line (Shieferdecker, 2017). Green Line ridership in 2015 was 37,400, nearing Metro’s goal of 41,000 yearly rides by 2030. Central Corridor ridership, including Green Line and surrounding bus routes, nearly doubled between 2013 and 2015 (Metro Transit, 2016). Overall, light rail ridership has increased 126% while vehicle revenue hours have increased 162% between 2013 and 2016. Bus ridership over the same period has fallen by 16% despite a 2% increase in vehicle revenue hours based on an analysis of NTD data. Similar efforts took place between 2015 and 2017 by the Maryland Transit Administration (MTA) in Baltimore, as several routes were rebranded and the system reworked to provide BRT- ready color-coded lines with 24-hour service and high frequencies radiating from the city center. Additionally, connecting local buses were planned to form rings around the city to bridge gaps in service, and peak-period express buses would create fast links to downtown. The MTA’s stated goals were to provide better and more frequent service city-wide and to strengthen connections between bus and rail (Maryland Transit Administration, 2017). The system went into effect in June of 2017 to much fanfare and high expectations (Dovak, 2017). In an analysis of NTD data, despite a 7% increase in bus vehicle revenue hours between 2016 and 2017, bus ridership fell by nearly 9%. Dedicated Right-of-Way and Bus Rapid Transit Increased congestion in growing cities, due in part to increased single-occupancy vehicle and TNC trips, has slowed bus speeds in cities (Schaller, 2018). As these services both slow down transit and potentially pull riders away, many transit agencies and their cities are giving transit dedicated lanes to move vehicles faster through congested streets. Dedicated lanes also allow for tighter headways and keep buses from frequent bunching. These partnerships between transit agencies and local jurisdictions display a dedication to improving transit experiences and rider- ship. Although the negotiations are often considerable, they can often be completed at little capital cost compared to the resulting benefits to transit riders.

Transit Agency Strategies 39 Two cities’ pilots proved wildly successful at both speeding up vehicles and attracting riders at little cost. In Toronto, the city’s busiest streetcar route on King Street was plagued with delays and inconsistent service as the vehicles sat in traffic with cars. In November 2017, a one-year pilot was announced to help speed up the streetcars by restricting private cars’ access to the street. One hundred eighty parking spots were removed to make way and private vehicles were forbidden to drive more than one block without turning right or left (Spurr, 2018). Deliveries, local access, and emergency access were not affected, and car travel times throughout the city experienced little change. The streetcar, however, saw increases in on-time performance to 85% on time, as vehicles were more consistently arriving within four minutes of their scheduled time. The pilot has also seen small decreases in travel time and increases in transit ridership of 13% all day and up to 19% for the afternoon peak between October 2017 and March 2018 (City of Toronto, 2018). In Boston, the city’s transportation department tested pilot bus lanes as part of their 2030 plan (City of Boston, May 2018). Bus ridership for the Massachusetts Bay Transportation Authority (MBTA) has fallen over 9% since 2012, corresponding to an 8% reduction in vehicle revenue hours based on NTD data. In a partnership between the city and the MBTA, a temporary bus lane was created in the Roslindale neighborhood along Washington St., one of the city’s busiest routes. The temporary lane was originally set with orange cones blocking off a single inbound lane to cars between 5–9 A.M. on weekdays. The results were a decrease in travel time by 20–25% during rush periods. In response to overwhelming support from bike and transit riders, the city made the bus lane permanent after the end of the four-week implementation period. Similarly, a peak-hour bus lane that replaced a mile of on-street parking along Broadway in Everett has cut trip times by 20–30% (City of Boston, June 2018). Transportation Network Companies and Bike, Scooter, and Car Sharing Partnerships There is currently much discussion on the role of transportation network companies (TNCs), such as Uber and Lyft, in recent transit ridership declines. Though a thorough analysis has yet to be completed, there is evidence that these services may be helping to increase ridership in some cases and decrease ridership in others (Hall et al., 2018). Regardless, TNCs have the potential to decrease auto ownership, and many transit agencies have partnered with these services to allow connectivity to areas near stops and stations that encourages transit use for a portion of each trip. A prime example is the Pinellas Suncoast Transit Authority (PSTA), whose pilot partnership with Uber was the first of its kind, who recently expanded and added a Lyft partnership (PSTA, 2016). PSTA provides subsidies to Uber, Lyft, and taxi rides to designated bus stops, expand- ing their service area outside of walking distance from bus lines. NTD data shows that demand response ridership increased over 5% between 2015 and 2017, with reported vehicle revenue hour increases of 91%. Bus ridership, however, fell nearly 20% over the same period, while bus vehicle revenue hours fell 3%. Since PSTA’s pilot, 13 other transit agencies, including some of the country’s largest, have begun exploring subsidized rides in their service areas (APTA, 2018a). These programs range from paratransit-specific trips to full service area TNC subsidies. A potential benefit to some of these programs is the elimination of select inefficient and underutilized bus routes so as to send more resources to routes that need them. Ridership effects are still unknown, and a variety of factors including wait time, fares, accessibility, and service area are at play. Additional partnerships between transit agencies and shared mobility services such as bike- share and scooters have the potential to allow more car-free trips. These technologies allow first-and-last-mile connectivity from transit stops and stations without transit or private

40 Analysis of Recent Public Transit Ridership Trends vehicles. The FTA sandbox program, detailed in the next section and primarily focused on demand response, has provided funding for a bike sharing partnership in Chicago that looks to include bike sharing in its trip planning and fare payment app (Spielman, 2017). A 2015 survey of over 80 transit agencies and transportation stakeholders by Iacobucci et al. (2017) found that only transit agencies in Boston and Seattle had data sharing partnerships with TNCs and that many officials were skeptical of partnerships with TNC and car sharing companies. Others were concerned with their transit agencies’ and local jurisdictions’ ability to keep up with rapidly changing technology but insisted that access to data is key for the future success of these partnerships. Since the study, transit agencies such as Miami-Dade Transit (Zipcar, 2017) and the Maryland Transit Administration (Zipcar, 2018) have added dedicated car sharing spaces at rail stations as an added form of flexibility for transit riders to complete trips and run errands. Demand Response and Flex Routes To provide greater transit access in low-density neighborhoods, a re-emerging strategy consists in using demand-responsive transit. Research using simulation has shown that in low- density areas, demand-responsive transit can service short trips faster (Qiu et al., 2015) and at a lower cost than fixed routes (Edwards and Watkins, 2013). Several transit agencies have imple- mented demand-responsive service either to reach the first-and-last-mile or to connect origins and destinations directly. There are two main approaches used in practice to provide demand-responsive transit. The first approach consists in using third-party software to dispatch transit agency operators. The Denver Regional Transportation District has been providing dynamic rides with their own vehicles and operators since 2000 (Becker et al., 2013). Kansas City (Kansas and Missouri), the Bay Area in California, and Austin, Texas, all experimented with demand-responsive programs operated by their own staff, with varying degrees of success, detailed below. Chicago’s suburban Pace recently announced a microtransit pilot to supplement its fixed-route network and provide more streamlined service (DemandTrans, 2018). The second approach consists of employing independent drivers who use their own vehicles to pick up customers at their door, similar to the TNC partnerships described above. The Los Angeles Metro is planning a similar program in partnership with the technology company, Via. The advantage of going through independent drivers is that the transit agency can take advantage of economies of scale from existing networks of ride-hailing drivers. There still lacks, however, quantitative research to assess the service and ridership implications of the programs. One primary source of funding and inspiration for recent demand response programs comes from the Federal Transit Administration’s Mobility on Demand Sandbox Program. The $8 million program, announced in October 2016, is interested in assisting transit agencies and departments of transportation in introducing mobility tools like demand response and vanpool programs. A total of 11 transit agencies were involved in the program for fiscal year 2016, with some pilot programs extending to bike sharing partnerships and advanced trip planner tech- nology in addition to demand response and paratransit pilots (FTA, 2017). Outside of these Sandbox programs, transit agencies in Kansas City, the Bay Area, and Austin have been experimenting with unique approaches to demand response microtransit. • In 2016, the Kansas City Area Transportation Authority (KCATA) announced a one-year microtransit pilot with Ford and microtransit provider Bridj. The goal of the project was to extend KCATA’s reach to new communities by placing 10 roving vans throughout the service area,

Transit Agency Strategies 41 and when riders would enter their origin and destination from a set of specific pickup and dropoff points, rides would be paired and chained together with Bridj’s algorithm (Marshall, 2016). During the pilot, a series of surveys were conducted on those participating, with over half indicating they chose to use the service because it was cheaper than alternatives. While 25% of respondents indicated that they drove less often because of the service, a similar number indicated using the bus less often (Shaheen et al., 2016). Despite promising tech- nology and survey results, a pilot attracted only 1,480 rides. Bridj later went out of business. Officials in Kansas City saw the pilot as a learning process, and they were optimistic that with better marketing and more data, a similar type of service could be successful in the U.S. • The Bay Area’s Santa Clara Valley Transportation Authority (VTA) experimented with a similar microtransit pilot for six months in 2016. Called “FLEX,” the service launched in January 2016 to test the viability of an on-demand service and its associated software in the region. Within a six square mile service area, riders could use an app to request a shared ride between 5:30 A.M. and 8:30 P.M. (VTA, 2016). The high costs and lack of ridership of the pilot caused it to be severed after six months. A Curbed article argues that its primary issues were a restrictive service area, lack of connection to existing transit options like light rail, and lack of utility to most potential users (Sisson, 2018). • Despite the lack of ridership in other cities, Austin’s microtransit pilot saw much greater success. In June of 2017, Capital Metro partnered with Via to provide free on-demand rides for a year within a specified service zone. The service was available through an app, and a vehicle was guaranteed to arrive within 15 minutes (Capital Metro, 2017). Within two months, the service reached its six-month ridership goals, and after a year, the vehicles had served more than 20,000 rides (Bliss, 2017). Austin’s pilot may have been unique due to lack of fares and the city’s uneasy history with TNC providers. In May 2016, Uber and Lyft were effectively forced out of Austin by a referendum requiring drivers to be held to similar scrutiny as taxi drivers. After a year, the services were allowed to resume normal service (Liptak, 2017). Fare Media and Integration Fares are a vital component in transit policy, as it is a delicate balance between transit rider- ship and revenue. Transit fare media and fare policies can determine the ridership experience and ultimately affect transit ridership. Outdated fare technology can slow down vehicles and damage a transit agency’s perception as being outdated or left behind, and new fare technol- ogy can help modernize and speed up service. A 2015 study in Los Angeles showed a 2 second decrease in dwell time per passenger using a smart card over a traditional ticket (Shockley et al., 2015). Transit agencies have recently implemented account-based and open-loop fare payment systems to reduce the time and effort required to purchase a transit fare. Account-based systems integrate these modes into a single user account, which can then be anonymously tied to trips for better origin-destination data. Slow and inefficient payment systems serve to keep buses and trains waiting longer for passengers to board. Open-loop payments allow riders to use their own bank accounts and smartphones to pay without purchasing passes or tickets from the transit agency. Several transit agencies have undertaken these technologies to simplify methods of payment and combine services into a single platform. • In Portland, OR, TriMet recently began a transition to a comprehensive, permanent pass. The transit agency currently relies on paper tickets to collect and validate fares, often result- ing in slow boarding processes (TriMet, 2018). TriMet’s new Hop Fastpass allows seamless connection between bus, rail, streetcar, and commuter rail modes with built-in transfers.

42 Analysis of Recent Public Transit Ridership Trends TriMet also accepts phone payments via mobile wallets and NFC readers (Altstadt, 2018). An added feature of the Hop Fastpass is its fare-capping capabilities. Riders taking multiple trips will never be charged more than the cost of a day pass in a single day, nor will they be charged more than a monthly pass in a single month, regardless of how many trips they take (Hop Fastpass, 2018). This policy can provide peace of mind to riders concerned with paying multiple fares and may encourage extra trips. • In Chicago, IL, a magnetic-swipe system was slowing down buses and costing the CTA close to $5 million per year in handling expenses (O’Neil, 2013). Chicago’s Ventra system, set up in July 2014, was one of the first smart card technologies of its size in the U.S., combining bus and rail swipe into a faster tap system. Later, Metra commuter rail, Pace suburban bus, and real-time tracking were combined with CTA services into one app that allows purchasing and using fares without a physical card. This system allows any Chicago transit rider to use a single account and payment system, simplifying transit use across the region (Ventra, 2018). Additional Strategies In addition to the methods detailed above, there are a variety of efforts that cities and tran- sit agencies have gradually adopted that may be helping to boost ridership. The availability of real-time information in transit service has grown substantially over the past several decades. These generally app-based services have allowed transit riders to have confidence in the arrival of their next bus or train and potentially decrease wait time at stops and stations. A 2015 study by Brakewood et al. demonstrated that the arrival of real-time information to buses in New York City brought with it a 2–3% increase in ridership. The arrival of alternatives to auto ownership in recent years may also help transit agencies sustain or grow ridership. A 2018 study of 25 North American regions by Boisjoly et al. showed that the presence of a bike sharing service and Uber both correlated with higher ridership than in regions without. However, timely data on the impacts of such services on ridership are just emerging and more research is needed. Similarly, many agencies are turning to customer experience issues as part of an effort to improve ridership. Through surveys, agencies such as LA Metro have found that security con- cerns, homelessness, and unavailable or unreliable transit information have caused former riders to stop using transit. Overall, incremental improvements such as real-time information, partner- ships with other mobility services, and improvement to customer service have the potential to retain riders and help curb auto-dependency within regions. Summary Transit agencies across the country have adopted a wide variety of tactics to combat recent ridership declines. While research must still be done on the effectiveness of the implemen- tation of all of the pilots and programs above, there are some key takeaways to be had from projects over the last several years. While transit service levels remain a key determinant of transit ridership, transit agencies have implemented new strategies to maximize the effective- ness of scarce operating funds. One of the most significant trends of the last several years has been network restructuring and integration. Transit agencies have also implemented partner- ships with ride-hailing companies and piloted microtransit programs. Dedicated bus ROW has shown the potential for drastic improvements in operational efficiency, which could translate into increased transit ridership.

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Analysis of Recent Public Transit Ridership Trends Get This Book
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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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