Working Paper No. 15-04 Bike-Share Systems: Accessibility and Availability Ashish Kabra INSEAD Elena Belavina University of Chicago Booth School of Business Karan Girotra INSEAD All rights reserved. Short sections of text, not to exceed two paragraphs. May be quoted without Explicit permission, provided that full credit including notice is given to the source. This paper also can be downloaded without charge from the Social Science Research Network Electronic Paper Collection. Electronic copy available at: http://ssrn.com/abstract=2555671 BIKE-SHARE SYSTEMS: ACCESSIBILITY AND AVAILABILITY Abstract. The cities of Paris, London, Chicago, and New York (among others) have recently launched large-scale bike-share systems to facilitate the use of bicycles for urban commuting. This paper estimates the relationship between aspects of bike-share system design and ridership. Specif- ically, we estimate the effects on ridership of station accessibility (how far the commuter must walk to reach a station) and of bike-availability (the likelihood of finding a bike at the station). Our anal- ysis is based on a structural demand model that considers the random-utility maximizing choices of spatially distributed commuters, and it is estimated using high-frequency system-use data from the bike-share system in Paris. The role of station accessibility is identified using cross-sectional variation in station location and high -frequency changes in commuter choice sets; bike-availability effects are identified using longitudinal variation. Because the scale of our data, (in particular the high-frequency changes in choice sets) render traditional numerical estimation techniques in- feasible, we develop a novel transformation of our estimation problem: from the time domain to the “station stockout state” domain. We find that a 10% reduction in distance traveled to access bike-share stations (about 13 meters) can increase system-use by 6.7% and that a 10% increase in bike-availability can increase system-use by nearly 12%. Finally, we use our estimates to develop a calibrated counterfactual simulation demonstrating that the bike-share system in central Paris would have 29.41% more ridership if its station network design had incorporated our estimates of commuter preferences—with no additional spending on bikes or docking points. 1. Introduction Urban agglomerations across Asia, Europe, and the Americas are faced with unprecedented traffic congestion and poor air quality that threatens their attractiveness to citizens and businesses. Only three of the 74 Chinese cities monitored by the central government managed to meet official minimum standards for air quality in 2013 [Wong, 2014]. In March 2014, levels of suspended particulate matter in the air above Paris reached twice the permissible level, which led to driving restrictions [Rubin, 2014]. Likewise, many large US cities are in a state of “non-attainment” with respect to their clean air requirements.1 Passenger vehicles are the major culprit in each case: 45% of air pollution in European cities can be directly attributed to private passenger vehicles, and that figure reaches 80% for some Asian cities.2 Traffic congestion worsens air quality and is a scourge in its own right. An average US commuter loses 34 hours and $750 annually to traffic congestion; commuters in Washington DC, Los Angeles, San Francisco, and Boston lose twice as much time and money.3 An average resident of Paris loses €2,883 each year because of traffic congestion, costing the French economy some €17 billion 1“Green Book”, US Environmental Protection Agency, 2 July 2014, http://bit.ly/EPAGreen 2“Changing Gears: Green Transport for Cities”, World Bank and Asian Development Bank Report, 2012. 3“Urban Mobility Report”, Texas A&M Transportation Report, 2012. 1 Electronic copy available at: http://ssrn.com/abstract=2555671 2 BIKE-SHARE SYSTEMS: ACCESSIBILITY AND AVAILABILITY annually [Negroni, 2014]. Emerging market mega-cities (Bangkok, Manila, Kuala Lumpur, Delhi, Mumbai, and Ho-Chi Minh City, inter alia) routinely break traffic congestion records, and Asian economies lose from 2% to 5% of their annual GDP to traffic congestion. The use of bicycles helps to alleviate both traffic congestion and poor air quality. Bicycles can substitute for polluting vehicles on short trips, and they facilitate the use of environmentally efficient public transport for long trips by providing effective “last mile” connectivity. The use of bicycles also reduces road congestion: compared with a typical passenger vehicle, which occupies 115 m3 of road space, a bicycle makes do with only about 6 m3 [Rosenthal, 2011]. However, the adoption of bicycles by commuters remains low in most major cities. The main barriers are the lack of safe parking spaces for bikes in urban dwellings and at public transit hubs, vandalism and theft of bikes, and the inconvenience and cost of owning and maintaining a bike. Bike-share systems address each of these concerns.4 A typical bike-sharing system includes a communal stock of sturdy, low-maintenance bikes dis- tributed over a network of parking stations. Each station provides 10–100 automated parking spots, or docking points, and a networked controller interface. A registered commuter can “check out” any available bike from a station and, at the end of her commute, can return the bike to any station in the network. Registration often requires the commuter to pay a security deposit. Usually the first half hour of use is free of charge and subsequent intervals are progressively more expensive. From an individual commuter’s point of view, bike-share systems eliminate the inconvenience of bike ownership, the need to find parking places, and the fear of theft and vandalism. Moreover, being able to take a bike from one station and drop it off at another facilitates one-way trips and the use of different modes on round trips. A crucial system feature is that bicycles can be used as an effective last-mile feeder system to other public transit systems, such as metro rail or bus systems. While bike-sharing systems have existed since the 1950s, there has been renewed interest since the successful implementations in France in 2006. As of April 2013, these systems had spread across Europe, the United States, and Asia—there were more than 530 bike-sharing systems in operation around the world with a total fleet of about 517,000 bikes. Paris, Barcelona, London, Wuhan, Hangzhou, Shanghai, New York City, and Chicago have all implemented large-scale systems.5 Although bike-sharing systems have garnered considerable attention, their promise is far from being fully realized. Despite widespread enthusiasm among citizens, ridership in some systems has fallen short of projections and there is increasing pressure on operator finances. More importantly, 4In the words of Chicago transportation commissioner Gabe Klein: “There is no holy grail, but a public bike share is pretty close.” http://bit.ly/1pTaagl 5Janet Larsen, “Bike-Sharing Programs Hit the Streets in Over 500 Cities Worldwide”, Earth Policy Institute, 25 April 2013; and Wikipedia entry on the “bicycle sharing system”. Electronic copy available at: http://ssrn.com/abstract=2555671 BIKE-SHARE SYSTEMS: ACCESSIBILITY AND AVAILABILITY 3 current ridership levels are well short of meeting the challenge of transforming urban transportation. A key reason for the lacking ridership is that while providers and operators have focused on bike- design and technology aspects, there is almost no rigorous analysis of operational aspects such as station location, system-reliability, nor are the commuter responses to such aspects understood [Tangel, 2014]. The aim of this paper is to identify relationships between ridership and design aspects of a bike-share system and, to illustrate the use of these relationships in designing systems that achieve higher ridership. In particular, we estimate the impact on ridership of two factors: station accessibility, or how far a commuter must walk to reach a station; and bike-availability, or the likelihood of finding a bike at the station. There are, in turn, two aspects of bike-availability. The immediate one is that commuters must walk longer (or use other means of transport) if nearby stations don’t have bikes. The more subtle, long-term aspect of availability is that—to the extent that the system is less reliable in this regard—commuters are less likely to incorporate bike-sharing into their daily commute or to make long-term commitments (e.g., forgoing their cars, choosing to live in an urban area). We conceptualize commuter behavior in bike-share systems as a choice between differentiated products. Thus, each commuter is viewed as a consumer, each station is a different product, the distance to a station and its historic bike-availability are product characteristics, and the set of stations with available bikes is the consumer’s choice set. Our parameters of interest are commuter preferences for distance and for historic bike-availability. Note here that the first product characteristic (distance to the station) is a characteristic that is both station and commuter specific. A reduced-form, station-level model would need to consider a representative commuter, and in such a model the effect of distance is confounded with the effect of the station’s “catchment” area; that is, stations far away from other stations require that commuters walk farther but also have a larger catchment area. An alternate approach would be to build a model at the commuter–station level, but that would require observing the start point of every commuter—data that are available neither to us nor to any bike-share operator. To avoid the pitfalls of either approach, we develop a structural demand model based on a random utility maximization framework (as in [Berry et al., 1995], “BLP” henceforth) that uses only station-level data yet recovers the effect of distance on commuter choice. Our model considers a population of potential commuters distributed randomly across the oper- ation area with a parametric spatial density.