National Academies Press: OpenBook

Analysis of Recent Public Transit Ridership Trends (2020)

Chapter: Chapter 5 - Case Studies

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Suggested Citation:"Chapter 5 - Case Studies." 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 5 - Case Studies." 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 5 - Case Studies." 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 5 - Case Studies." 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 5 - Case Studies." 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 5 - Case Studies." 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 5 - Case Studies." 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 5 - Case Studies." 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 5 - Case Studies." 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 5 - Case Studies." 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 5 - Case Studies." 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 5 - Case Studies." 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 5 - Case Studies." 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 5 - Case Studies." 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 5 - Case Studies." 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 5 - Case Studies." 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 5 - Case Studies." 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 5 - Case Studies." 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 5 - Case Studies." 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 5 - Case Studies." 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 5 - Case Studies." 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 5 - Case Studies." 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 5 - Case Studies." 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 5 - Case Studies." 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 5 - Case Studies." 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 5 - Case Studies." 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 5 - Case Studies." 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 5 - Case Studies." 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 5 - Case Studies." 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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Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

38 Chapter 4: Transit Agency Strategies 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 complexities. 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, reliability, 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 ridership 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. However, 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 right-of-way 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 (Cluster 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, especially 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. Bus Network Restructuring Recent efforts to increase transit ridership have consisted in restructuring bus networks to prioritize service concentration over coverage. Bus network redesigns in locations such as

39 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 proportion of residents. However, the Houston press reported that low-income neighborhoods lost 12 routes whereas non-low-income neighborhoods gained three (Flynn, 2015). Figure 18: Houston Metro Before and After Frequent Network Redesign Map  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 Transit 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’ 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 several years. Metropolitan Atlanta Rapid Transit Authority (MARTA) commissioned a Comprehensive Operations Analysis study, which also recommended concentrating bus service on core corridors (Parson Brinckerhoff, 2016). In

40 reducing their coverage, however, MARTA has faced stiff resistance from residents who rely on bus service as their only mode of transportation (Abubey, 2017). 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 bus rapid transit lines were added, the bus networks were readjusted accordingly. The objective was to facilitate connections 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). 2015 Green Line ridership 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

41 a 7% increase in bus vehicle revenue hours between 2016 and 2017, bus ridership fell by nearly 9%. Dedicated Right-of-way & 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 ridership. Although the negotiations are often considerable, they can often be completed at little capital cost compared to the resulting benefits to transit riders. 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. 180 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 of 85%, 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 AM on weekdays. The results were a decrease in travel time by 20 to 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, 2018).

42 Transportation Network Companies (TNCs) & Bike, Scooter and Car Sharing Partnerships There is currently much discussion on the role of 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, and recently expanded and added a Lyft partnership (PSTA, 2016). PSTA provides subsidies to Uber, Lyft, and taxi rides to designated bus stops, expanding 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, 2018). 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 bikeshare 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 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. 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.

43 Demand Response & 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 implemented 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 Transit District has been providing dynamic rides with their own vehicles and operators since 2000 (Becker et al. 2013). Kansas City, the Bay Area, and Austin 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 in 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 eleven transit agencies were involved in the program for fiscal year 2016, with some pilot programs extending to bike sharing partnerships and advanced trip planner technology 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 Transit 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, 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

44 that they drove less often because of the service, a similar number indicated using the bus less often (Shaheen et al., 2016). Despite promising technology 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 US.  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 AM and 8:30 PM (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 fare, 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 (A. W., 2017). Fare Media & Integration Fares are a vital component in transit policy, as it is a delicate balance between transit ridership 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 technology 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

45 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, TriMet recently began a transition to a comprehensive, permanent pass. The transit agency currently relies on paper tickets to collect and validate fares, often resulting 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. 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, 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 US, 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 transit 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 2016 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 regions without. However, timely data on the impacts of such services on ridership are just emerging and more research is needed.

46 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 concerns, homelessness, and unavailable or unreliable transit information have caused former riders to stop using transit. Overall, these incremental improvements such as real-time information, partnerships 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 implementation 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 effectiveness 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 partnerships with ride- hailing companies and piloted microtransit programs. Dedicated bus right of way has shown the potential for drastic improvements in operational efficiency, which could translate into increased transit ridership.

47 Chapter 5: Case Studies To further explore strategies transit agencies are using along with the relationships between ridership and operations, ten case studies were chosen for further analysis. The ten case studies conducted represent a variety of conditions in terms of ridership change, other performance trends, and strategies attempted to encourage transit ridership and combat potential declines. The ten transit agencies include:  Connect Transit in Bloomington-Normal, IL  Greater Portland Transit District in Portland, ME  IndyGo in Indianapolis, IN  King County Metro in Seattle, WA  Maryland Transit Administration in Baltimore, MD  Massachusetts Bay Transportation Authority in Boston, MA  Metro Transit in Minneapolis-St. Paul, MN  Metropolitan Transit Authority of Harris County in Houston, TX  Pinellas Suncoast Transit Authority in St. Petersburg, FL  Spokane Transit Authority in Spokane, WA For each transit agency, an interview was conducted to obtain background information on ridership trends and strategies from the transit agency’s perspective. Data on unlinked passenger trips, vehicle revenue miles, vehicle speeds, and in some cases on-time performance were obtained from each transit agency and trends in each of these operating characteristics were graphed along with their relationship to transit ridership.

48 Case Study 1 – Connect Transit, Bloomington-Normal, IL Background Connect Transit operates fixed route bus service in the Bloomington-Normal, IL metro area providing around 8,600 trips per weekday. Connect Transit operates 15 fixed routes that converge on two transit centers. Illinois State University represents a sizable portion of both the region’s population and the transit agency’s ridership. The typical Connect Transit passenger is transit dependent and between 18-24 years old. Bloomington-Normal Public Transit System was established as a joint effort between the City of Bloomington and the Town of Normal in 1972. After rebranding as Connect Transit in 2012 and refocusing efforts on customer service, employee development, and technology, the fixed route bus system saw significant growth in transit ridership in 2012, 2013, and 2014. In 2015, Connect Transit received the American Public Transportation Association (APTA) Award for Outstanding Public Transportation System for transit agencies in North America providing fewer than 4 million passenger trips annually. In 2015, the transit agency switched from a flag system, where passengers could flag down a bus at any safe street corner, to a fixed system, where passengers can only be picked up and dropped off at predetermined bus stop locations and transfer centers. The Connect Transit fixed-network was comprehensively redesigned in 2016 with long and circuitous routes replaced with new route alignments on major corridors. The network redesign consisted in increasing frequency, adding Sunday service to the system, and providing customers with a real-time mobile app. Since the redesign, minor adjustments to the transit system were made, included cutting a route in 2017 and extending service hours for select routes in 2018. Key Performance Trends Key trends for bus service from 2012 to 2018 are shown in Figure 19 which displays a 12- month rolling average normalized to January 2012 of the unlinked passenger trips (UPT), vehicle revenue miles (VRM), and average speed. Bus ridership since 2012 has followed a remarkable trend, peaking in 2015 at over 35% above 2012 levels, and recently settling near 15% above. This all came with almost no change in service levels and recently declining average speeds. The increasing ridership happened at the same time new technologies were rolled out under the new General Manager who joined the transit agency in 2011. A redesigned website, mobile bus tracking, a rebranding to Connect Transit, and better customer service all took place in the last several years. The increase in ridership may be partially due to a change in the method for estimating ridership, manual counts to automated passenger control counts (APC). However, new technology could also account for improvements in passenger information, which may also partially explain the ridership increase. Although the network was redesigned in 2016 to increase fixed route ridership, the number of passenger trips and average speed continued to drop until mid-2017, as seen in Figure 19. This

49 initial decrease of riders may be due to the public confusion of the new routes with reused names. The decrease in average speed may be explained by new route alignments on major corridors with congestion. After an initial adjustment period post-launch of the restructured system, fixed route ridership began to increase. An extra hour of service on four of Connect Transit’s main routes was added in late 2018. Figure 19: Connect Transit Bus Systemwide Trends  Ridership data for Figure 20 and Figure 21 was calculated as monthly average weekday boardings averaged over the fall period (September, October, November, and December) of 2013 and 2017. Both passenger counts and route frequencies were provided from the transit agency. While the route alignments and schedules changed between 2013 and 2017, their comparison provides insights on the effect of the network redesign. From 2013 to 2017, all bus routes increased frequency but did not increase in passenger boardings per trip as seen in Figure 20. Connect Transit saw a peak in ridership in 2015 and overall average weekday ridership has decreased between the end of 2013 and the end of 2018. On time performance increased overall from 2013 and 2018 after the introduction of fixed routes and restructured system as seen in Figure 21. Following the system restructure in 2016, overall on-time performance improved. This may be due to the new alignment on major corridors, which allowed buses to operate shorter routes on better-maintained streets. Dwell times were also added in 2016 to the schedule on all routes to allow room for error or delay. Future Plans to Encourage Ridership Connect Transit was recently awarded a grant for battery electric replacement buses. Connect Transit hopes the new-technology buses will allow improvements in on-time performance while decreasing operating expenses. There is additional discussion of improving bus stop infrastructure, increasing fares, and the discontinuation of low-performing routes.

50 Figure 20: Connect Transit Frequency and Ridership and Trends in 2013 and 2018  Figure 21: Connect Transit On‐time Performance and Ridership Trends in 2013 and 2018 

51 Case Study 2 – Greater Portland METRO, Portland, ME Background The Greater Portland Transit District (Greater Portland METRO) is Maine’s largest public transit agency and provides more than 1.8 million boardings per year. METRO operates 11 fixed route bus service in southern Maine including Brunswick, Falmouth, Freeport, Gorham, Portland, South Portland, Westbrook, and Yarmouth. In the past half-decade, METRO bus ridership has increased after implementing high school student transit passes and a commuter service. Founded in 1966, METRO went through several decades of declines in bus service area and ridership. In 2004, the transit agency began expanding again, and improvements have come quickly since then. After a 2013 bus priority study of recommended improvements to a street to increase speed of buses, two signals were modified to accommodate transit and an in-line bus stop was added by 2017. In 2015, free rides for high school students began, and Sunday service was increased. An express bus service, METRO Breez, was added in 2016 and expanded in 2017. A university program with University of Southern Maine (USM), started providing free transit for students, staff, and faculty in 2018. The Husky Line, a distinctively-branded bus route featuring more frequent connections for students and professionals, was introduced in 2018 as well. Key Performance Trends Key trends for bus service from 2012 to 2018 are shown in Figure 22 which displays a 12- month rolling average normalized to January 2012 of the unlinked passenger trips (UPT), vehicle revenue miles (VRM), and average speed. Bus ridership since mid-2015 shows a remarkable trend of nearly 30% growth. A sizeable portion of this ridership may be attributed to incoming high schoolers following the elimination of yellow bus service in 2015, indicated on Figure 22. Fixed route ridership continues to grow in 2016 to 2018, and service levels and average speed have steadily grown as well. METRO Breez express bus service began operating in August 2016 connecting the downtowns of Portland, Freeport, and in mid-2017 Brunswick.

52 Figure 22: METRO Bus Systemwide Trends  Ridership data for Figure 23 was calculated on each route as average monthly ridership over a year from 2013 to 2018. Historic route frequencies were not available. METRO bus ridership has remained steady or increased on all routes. Unfortunately, on-time performance (OTP) data before February 2018 was not available. METRO defines a bus to be “on-time” if it is operating less than six minutes late at a timepoint. On-time data is calculated and tracked through an automatic vehicle location (AVL) system. Available on-time performance data, February 2018 to February 2019, is displayed in Figure 24. Low OTP on express bus may be due the longer route. Figure 23: METRO Bus Ridership Trends by Route 

53 Figure 24: METRO Bus On‐time Performance Trends by Route  Future Plans to Encourage Ridership Looking towards the future, transit officials of greater Portland have begun a study of the region’s bus, rail and ferry services to guide transportation planning for the next three decades. METRO will deploy a new fare structure and payment system in 2019 to modernize the system. Although mobile app and plastic card technology will be introduced, a cash box will remain. A fare increase has been proposed, from $1.50 to $2.00, and the current reduced fare for riders older than 65 will extend to riders between 6 and 18. METRO is planning to add zero- emissions vehicles to its fleet in 2020. The city of Portland is also undergoing a series of progressive enhancements, such as changes to zoning code that allows developers to pay a fee in lieu of meeting minimum parking requirements.

54 Case Study 3 – IndyGo, Indianapolis, IN Background The Indianapolis Public Transportation Corporation, branded as IndyGo, is the largest public transportation operator in Indiana. IndyGo provides and operates bus and paratransit services around the Indianapolis region with 31 fixed bus routes providing nearly 10 million passenger trips a year. IndyGo is improving resources and operations over the next five years to expand service frequency and hours of operation for its fixed route local network. The transit agency is also constructing three new rapid transit lines, and changing the orientation of their network from a hub-and-spoke network to a grid system. Fixed route transit ridership generally declined since the public agency took over operations in 1975. IndyGo has recently undertaken a series of active steps to reverse the trend. Free circulator routes and university-focused routes became popular in the mid-2000s with transit ridership peaking in 2003 at more than 10.9 million unlinked annual passenger trips. These routes fell out of use and were discontinued in 2009 with system transit ridership falling to 8 million annual riders. On-board surveys conducted by IndyGo in 2009 and 2016 indicate that the typical rider profile, a low-income adult traveling between home and work, has not changed significantly over the years. A similar distribution of activities is seen between 2016 and 2009 responses, but there are slightly fewer passenger activities per vehicle trip in 2016. Today, the typical IndyGo passenger is transit dependent and frequently uses services to a wide variety of destinations. Key Performance Trends Key performance trends of IndyGo from 2012 to 2018 are shown in Figure 25 which displays a 12-month rolling average normalized to January 2012 of the unlinked passenger trips (UPT), vehicle revenue miles (VRM), and average speed. The system saw a leap in transit ridership between 2012 and 2015, followed by steady ridership declines despite a new downtown transit center opening in 2016. Improved frequency, extended hours, and additional stop amenities were implemented on existing fixed routes in mid-2013. Fixed route-level frequency has not dramatically changed since mid-2013, and route-level transit ridership has decreased from late 2013 to late 2017, as seen in Figure 26. From 2013 to 2017, routes with typically high ridership lost the most ridership proportionally. Transit ridership data for Figure 26 and Figure 27 is from monthly farebox data averaged over the period and stop and frequency data is from the transit agency’s General Transit Feed Specification (GTFS).

55 Figure 25: IndyGo Bus Systemwide Trends from 2012 to 2018 Figure 26: IndyGo Frequency and Ridership Trends in 2013 and 2017 

56 Figure 27: IndyGo On‐time Performance and Ridership Trends in 2013 and 2017  IndyGo has defined on-time performance as one minute early to 5 minutes late from scheduled arrival since 2009. IndyGo measures on-time performance with on-board Automatic Vehicle Location systems and is in the process of transitioning to a new platform. IndyGo tracks every Professional Coach Operator’s on-time performance each month and frequently recognizes drivers who meet or exceeds the goal of a 90% on-time monthly average. Although the average speed has dropped nearly 10% since 2012, the on-time performance (OTP) for each route has improved on all routes between 2013 and 2017 as seen in Figure 27. Improving OTP during this period has not resulted in ridership increase. OTP data for the winter periods in Figure 27 is calculated by averaging IndyGo’s self-reported data averaged over the months of November, December, and January. Winter storms in 2013 may partially account for low on-time performance. However, the long term trend of improved reliability is shown in Figure 28. Figure 28: IndyGo Systemwide On‐Time Performance 

57 Future Plans to Encourage Ridership To prepare for upcoming capital improvements, the strategic planning division at IndyGo has performed exploratory analysis of ridership trends. Changes in IndyGo ridership has generally mirrored national changes with a slight lag. At the local level, the IndyGo team has examined geospatial transit ridership trends as seen in Figure 29. While examining area stop-level boardings, they found a decrease of boardings on specific streets which were affected by street closures and resulting delay. Figure 29: IndyGo Area Year over Year Ridership Gains (Losses)  Past efforts to improve on-time performance and frequency have not resulted in ridership improvements. Looking to the future, IndyGo is hoping to combat decreasing ridership by:  Adding BRT lines,  Utilizing geospatial analysis tools,  Updating rolling stock,  Converting one-way streets to two-ways for more accessibility, and  Improving transit shelters downtown. Three bus rapid transit (BRT) lines will replace some of IndyGo’s most popular routes and include improved station infrastructure, dedicated lanes, transit-signal priority, level boarding, and off-board fare collection infrastructure starting in 2019, 2021, and 2022. Downtown transit shelters will be converted to “Super Stops” which include near-level boarding, real-time arrival information, and upgraded lighting and covered seating.

58 Case Study 4 – King County Metro, Seattle, WA Background King County Metro is the primary operator of bus service, vanpools, paratransit services, and community shuttles in the Seattle region. The transit agency also operates two streetcar lines, Seattle’s light rail, and commuter rail services. As the eighth-largest bus agency in the US, King County Metro operates 237 fixed route bus services and provides over 120 million passenger trips each year. Seattle has recently been featured in the press for its dramatic shift from driving to transit. Light rail openings have boosted these effects, but King County Metro has also managed to continually increase bus ridership over the past several years. Founded in 1973, King County Metro has played an increasingly important role in reducing congestion, protecting the environment, and getting people where they need to go in the Seattle area. King County Metro operated in the downtown Seattle fare-free zone for almost 40 years until the ride free area was eliminated in 2012. A network of high-frequency limited-stop bus routes, known as RapidRide, was introduced in 2010 and expanded in 2011, 2012, and 2014. RapidRide operates on six corridors and accounted for approximately 17% of bus ridership in 2017. After briefly reducing service in 2014, fixed route bus service has restructured and expanded fixed route bus hours and frequency service in 2015, 2016, 2017, and 2018. Over the past three years, King County Metro has significantly increased ridership, launched a reduced-fare program for lower income passengers, improved passenger and operator safety, and transitioned towards zero- emission bus fleets. “Transit GO Ticket” mobile app was launched at the end 2016 and allows riders to buy and redeem transit tickets for King County Metro buses, King County Water Taxi, Seattle Streetcar, Sound Transit’s Link light rail, and Sounder trains, on their mobile devices. Future large technology projects include bus lanes, signal priority, and re-timing, often on a corridor level, to help improve bus route performance. King County Metro’s also implements constant small spot improvements like adding parking restrictions to help buses access stops. Key Performance Trends Key performance trends for fixed route bus and streetcar service are shown in Figure 30 and Figure 31 which display a 12-month rolling average normalized to January 2012 of the unlinked passenger trips (UPT), vehicle revenue miles (VRM), average speed, and on-time performance (OTP). As seen in Figure 30, despite a consistent decrease in average speed, fixed route bus ridership has followed an upward trend since 2012, and has remained roughly constant since mid- 2016. The decrease in bus average speed may be due to an increase in traffic within Seattle causing bus average speed to gradually slow. Increased ridership may also explain a portion of the decrease in average speed; increased ridership is associated with higher dwell times. King County Metro officials indicated during interviews that service frequency was increased in an attempt to address

59 this passenger crowding. A decrease in average fixed route bus speed may explain decreased on- time performance on certain routes as seen in Figure 33. Streetcar ridership trends, placed on a different scale due to dramatic increases following the opening of the First Hill Line, is seen in Figure 31. The First Hill Line nearly tripled the system’s length in 2016. Figure 30: King County Bus Systemwide Trends from 2012 to 2018  Figure 31: King County Streetcar Systemwide Trends from 2012 to 2018  Fixed route bus ridership data for Figure 32 and Figure 33 is from adjusted average weekday automated passenger counter (APC) data averaged monthly over the fall period (September, October, November, December, January, February, and March). The fall 2015 service period extends from September 2015 to March 2016 and the fall 2017 service period extends from September 2017 to March 2018. Frequency data is provided from the transit agency. Express bus service and “One-Way Peak-Only” routes are not displayed in Figure 32 and Figure 33. Service frequencies have generally increased between 2015 and 2017 but ridership trends have not increased on every route.

60 Figure 32: King County Bus Frequency and Ridership Trends in 2015 and 2017  King County defines on-time performance as an arrival time between 1.5 minutes ahead of to 5.5 minutes behind the posted schedule. On-time performance metric for each route is calculated as the number of on-time arrivals divided by the total number of arrivals at time stops. The average weekday on-time performance metric during the fall service period is displayed per route in Figure 33. Figure 33: King County Bus On‐time Performance and Ridership Trends in 2015 and 2017 

61 Future Plans to Encourage Ridership King County Metro continues to monitor ridership and system performance, and analyze crowding and reliability to encourage ridership. King County Metro has a large budget each year to address crowding, reliability and service expansion needs. Recent and future key projects include the following:  Third Avenue is largely considered the key transit spine in Seattle. Beneath it lies the transit tunnel, which serves light rail and bus vehicles in dedicated lanes. On the avenue itself, transit priority has been added for additional downtown capacity, and recent improvements include restricting left turns and extending transit priority hours, both taking place throughout 2018.  State Highway 99 is a downtown freeway in Seattle, which the group mentions receiving transit upgrades around 2016. The highway is also the focus of a major construction project, and due to anticipated traffic impacts, King County Metro has provided additional service along parallel routes to provide alternative transportation options. These projects are both ongoing, and therefore do not show up in the figures. Additionally, as planners mentioned, their goal is primarily to make incremental improvements along small segments of routes across several years.  Four new RapidRoute lines will be added by 2024 to create a grid of frequent bus lines connecting the major population centers in King County. There are additional plans to add seven new RapidRoute lines between 2025 and 2040.  After the successful test of three battery-electric buses and an in-depth feasibility analysis, King County Metro will purchase only zero-emission buses starting in 2020.

62 Case Study 5 – Maryland Transit Administration, Baltimore, MD Background The Maryland Transit Administration (MTA) provides bus, light rail, heavy rail, and commuter rail service in the Baltimore, MD region. Commuter trains also serve the Washington, DC region. MTA operates 80 fixed route bus lines, three light rail lines, three commuter rail lines, and one heavy rail line, providing around 300,000 trips per weekday. The MTA took over bus operations from the private Baltimore Transit Company in 1970. The fixed route bus network prior to BaltimoreLink had many routes that served outdated job locations and were too long to manage reliably; buses that served downtown Baltimore frequently compounded congestion. In June 2017, the fixed route bus network was redesigned. The transit agency spread out the routes within the downtown core and created a grid of high-frequency routes with the goal to be a more efficient and reliable bus network. BaltimoreLink is a complete overhaul and rebranding of 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. In the future, MTA is pursuing the addition of a new rail line and a new northbound corridor with bus rapid transit (BRT) treatments. The Metro Subway heavy rail line opened in 1983, serving northwest suburbs and downtown Baltimore. The commuter rail, known as Maryland Area Regional Commuter (MARC), began operation in 1984 between Baltimore and Washington DC. An unconnected light rail line opened in 1992, serving north suburbs, downtown, and the Baltimore airport. Most of MTA’s light rail operates on a dedicated right-of-way and as of 2007, the mixed traffic downtown portion of the route operates with a transit signal priority system. Key Performance Trends Key performance trends for fixed route bus, light rail, and streetcar service are shown in Figure 34, Figure 35, and Figure 36 which display a 12-month rolling average normalized to January 2012 of the unlinked passenger trips (UPT), vehicle revenue miles (VRM), average speed, and on-time performance (OTP). MTA’s fixed route bus ridership trend grew from 2013 to 2015 as seen in Figure 34. However, ridership has begun to plummet, falling nearly 15% from its peak in 2015. VRM, average speed, and OTP have all remained steady or improved over the same period for both bus and rail modes. Unfortunately, OTP data is available only on a fiscal year basis, and only reliably until 2016. Rail ridership followed a similar downward trend following 2015 as seen in Figure 35. Commuter rail, MARC, ridership has increased from 2012 to 2014 and since remained fairly constant as seen in Figure 36.

63 Figure 34: MTA Bus Systemwide Trends from 2012 to 2018  Figure 35: MTA Light Rail and Heavy Rail Systemwide Trends from 2012 to 2018  Figure 36: MTA Commuter Rail Systemwide Trends from 2012 to 2018  Fixed route bus ridership data for Figure 37 is from adjusted average weekday automated passenger counter (APC) data averaged monthly over the fall period (Sept to Dec). Frequency data

64 is provided from GTFS (General Transit Feed Specification) archives. Because of the network redesign and complete overhaul of the fixed bus system, 2014 and 2017 data are not connected in the figure. It is not possible to relate 2014 routes to 2017 routes due to the substantial changes in the network. The new BaltimoreLink network includes new route alignments, frequencies, and spans on most routes. Route level OTP data is not available because of a recent shift from using Automatic Vehicle Location (AVL) to an Automated Passenger Counter (APC) system. MTA’s fixed bus routes have seen a decrease in passenger boardings per trip as seen in Figure 37. Service frequencies have generally increased between 2014 and 2017 but ridership trends have not increased on every route. Figure 37: MTA Frequency and Ridership Trends in 2014 and 2017  Future Plans to Encourage Ridership Although MTA’s fixed bus ridership did not increase after the launch of BaltimoreLink, the network redesign process has left MTA in a better position for future transit improvements:  The Purple Line will be a 16-mile light rail line in suburban Washington, DC that will extend from Bethesda, MD to New Carrollton, MD. It will provide a direct connection to the Metrorail Red, Green and Orange Lines, as well as MARC Train, Amtrak and local bus services. The line will mainly operate in dedicated lanes with twenty-one planned stations. Purple Line service is anticipated to begin in 2022.  MTA is in the process of designing dedicated bus lanes, transit signal priority, Light RailLink and Metro SubwayLink stations enhancements, bus stop improvements, streetscaping, and roadway repaving on a five-mile stretch of North Avenue in Baltimore with completion by the end of 2021.

65 Case Study 6 – Massachusetts Bay Transportation Authority, Boston, MA Background The Massachusetts Bay Transportation Authority (MBTA) operates bus, light rail, heavy rail, and commuter rail in the Boston metro area. The MBTA operates some of the oldest rail lines in the country, including the first subway in the U.S. The MBTA system revolves around three heavy rail lines and one branched light rail main line that meet in downtown Boston. There are 177 bus routes, five bus rapid transit (BRT) routes, and thirteen commuter rail routes filling out the rest of the system. A history of strong transit ridership in the Boston metro area is the result of a connected and comprehensive system. To address existing service issues including unreliable and slow service and overcrowding, MBTA is working on modernizing their fixed route bus system with the Better Bus Project. The MBTA was formed in 1964 as a replacement for Metropolitan Transit Authority. Cuts in service and track mileage occurred in the latter half of the 20th century, as routes lost ridership and were abandoned. The Silver Line BRT was opened in 2002, followed by a series of extensions and expansions of that system until the present day. Recent projects to improve fixed route bus ridership primarily focus on speeding up buses on select routes. 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, in May 2018. The temporary lane was originally set with orange cones blocking off a single inbound lane to cars between 5-9 AM on weekdays, allowing only buses and bikes to travel in the lane. The results were a decrease in travel time by 20 to 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. Key Performance Trends Key performance trends for fixed route bus, heavy rail and light rail, and commuter rail are shown in Figure 38, Figure 39, and Figure 40 which display a 12-month rolling average of unlinked passenger trips (UPT), vehicle revenue miles (VRM), and average speed normalized to January 2012. Prior to 2014, passenger trip counts were collected and processed from farebox data. Automated passenger counters (APC) were implemented in most buses after 2014 but possible counting software errors made ridership data unreliable in 2015. The MBTA reports highly detailed OTP data, aggregated by individual day and mode. Daily OTP data became public in 2016. Bus OTP data only goes back to 2015, and rail OTP data only became available in March 2016, and it is therefore excluded from the figures. Bus data includes the Silver Line BRT. Trends in fixed bus ridership include increased bus ridership in mid-2015 followed by steady declines as seen in Figure 38. The increases may be due to inconsistencies in passenger trip reporting; starting in 2014, MBTA switched from fare box data to APC data for ridership data. An increase in bus ridership may also be due to a steady increase in bus use as more people are moving

66 to bus accessible areas. MBTA fixed route buses did not experience the national trend of ridership declines until about 2015 possibly a benefit of a larger, more robust system. VRM and speed remained somewhat constant over the period for both bus and rail, indicating that any route-level bus lane or reliability pilots may be holding off general declines in system-wide ridership seen with other transit agencies. Heavy rail and light rail ridership has remained fairly constant from 2012 to 2018 as seen in Figure 39. The temporary closing of Government Center Station in March 2014 to Jun 2016 may explain a drop in light rail ridership. Commuter rail ridership has decreased since 2015 despite the opening of two new stations, as seen in Figure 40. MBTA believes the drop in commuter rail ridership is due to service interruptions in Winter of 2015. Figure 38: MBTA Bus Systemwide Trends  Figure 39: MBTA Heavy Rail and Light Rail Systemwide Trends 

Next: Chapter 6 - Conclusions and Next Steps »
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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