Transportation Deployment Casebook/2025/Bike sharing in China

Qualitative

Traditional transport technologies cannot effectively meet the needs of people without a private vehicle and those residing far from public transit.

The population can be segmented into residents and tourists. For residents, two scenarios can negatively impact travel experience/demand and reduce potential economic activities. (1) Residents without private vehicles relying on public transit often walk long distances to access public transit. (2) Currently, areas lacking public transit or without public transit to desired destinations must either walk extensively or rely on taxis/ride-hailing services, increasing travel time and costs. In both cases, residents tend to avoid travel, reducing potential economic activities.

For tourists. (1) Tourists without convenient access to public transit must either walk or use ride-hailing to reach destinations/transit stations. Walking consumes a large amount of time and potentially cause tourists to miss optimal sightseeing opportunities, negatively affecting their mood and tourism experience. Conversely, ride-hailing services consume travel budgets, discouraging travel and reducing their contributions to the local economy. (2) If tourist attractions are bike-friendly, tourists face inconvenience in searching for a rental bike, complicating their travel experience. Both scenarios discourage travel and thus reduce potential economic activities.

Technology

As both software and hardware technologies have matured, bike sharing had been introduced as a green sharing economy rental model to address this market.

Hardware innovation was key to the success of bike-sharing. Each bike is equipped with a smart lock containing GPS and IoT chips, allowing users to locate bikes and parking spots in app. Operators can track and manage assets through these smart locks. Some bike sharing companies, such as Mobike, utilize solar charging technology to reduce maintenance costs .

On the software side, users can perform various functions within the app, including locating nearest bikes, unlocking bikes, paying deposits and trip fees, locating parking spots, and locking the bikes. Operators can analyze user data to improve bike deployment and use credit scores to reduce improper parking .

Context

Before bike-sharing, people primarily used private cars and public transit. Private cars offer door-to-door convenience, but high ownership costs, fuel expenses, parking fees, traffic congestion make them inaccessible to most people. Public transit have their own fixed stops, so accessibility varies by location, the distances between public transit stop and origin/destination significantly impact travel time and convenience. Bike-sharing effectively overcomes these limitations. Its low-cost and convenient rental feature provide a flexible alternative, solving the last-mile problem .

Invention

Before modern bike sharing, several pivotal historical events marked key turning points:

• In 1965, the Provo group introduced the White Bicycle Plan in Amsterdam, they painted bicycles white and allowed people to use them freely . However, without designated stops or proper management, the bikes were improper parked and frequently stolen.
• Similarly, inspired by Amsterdam's free-bike concept, Keating and Tom O'Keefe launched the America's first community free-bike initiative — Portland's Yellow Bike Project (1994) , but it also suffered from theft and improper parking.
• With advancing technology, Portsmouth Bikeabout System (UK) was the first bike-sharing project to adopt "smart cards" . But its technology limitation and high hardware costs eventually led to its shutdown.

The White Bicycle Plan and Portland's Yellow Bike Project implied that insufficient management can result in the public assets loss, while Portsmouth Bikeabout validated the feasibility of smart card, positively influencing developments. With technological innovation, limitations in bike management and operating costs have been effectively addressed. Specifically, advances in following three areas had greatly contributed to its success.

GPS Technology

By 2016, as low-cost GPS modules became widespread, Mobike and Ofo began integrating GPS chips into their smart locks. Coupled with A‑GPS, it enabled comprehensive tracking of bikes. Previously, bike-sharing systems relied on fixed docks, which required large government investment for construction and maintenance costs, thereby limiting the bike sharing scalability. The advance in GPS technology expanded coverage and made dockless bike sharing possible . This innovation made parking more flexible, and improve user's efficiency of locating nearby idle bikes and enable operators to dynamically rebalance those bikes based on real-time data.

Internet of Things

Advance in IoT technology help improve operational efficiency. Huawei's NB-IoT was integrated into Mobike's smart locks, significantly enhancing data transmission. According to the Huawei Technical Manual , NB-IoT was standardized by 3GPP, expands the coverage of IoT devices while reducing power consumption and improving system capacity and efficiency. It created the following benefits: (1) With its extensive coverage, operators can remotely monitor vehicle status and control locks to prevent theft/damage. (2) Operator can assess the bikes' status in real-time and mitigate potential issues. (3) Broader coverage and more stable communication allow for real-time data collection, enriching the user behavior data and ensuring more accurate billing.

Credit System  

By integrating with Alipay and WeChat Pay, bike sharing not only streamlines the user process but also efficiently manages the bikes, as the credit system was able to fairly assign responsibility . Traditionally, users had to pay a deposit of 99 or 199 RMB . However, with the credit system enabled, the registration can to be completed within 30 seconds, greatly enhancing user experience. On the operator side, the credit scoring model effectively managed bikes . If misconduct occurs, the system deducts the user's credit points and negatively impact the user's financial credit evaluations.

Early Market Development

The commercial viability was tested in different scenarios, including in the established market (Hangzhou West Lake scenic area) and in new market (Peking University), the results indicated that bike sharing could effectively meet short-distance travel needs and a dockless system is more favorable than a dock-based system.

Case 1: Hangzhou Public Bicycle (2008)

The Hangzhou municipal government, partnered with the public transport, leveraged existing infrastructure to launch a dock-based bike-sharing system near the West Lake scenic area, targeting the tourism market. Users could register bikes using cards, and the bikes had to be returned to designated spots. To cultivate user habits, the pricing was set extremely low, it is free for the first hour, and 1 RMB for each additional hour . According to a report by the Hangzhou Transportation Bureau, by the end of October 2016, there were 3626 docks and 84100 bikes in Hangzhou, with a total of 716 million uses , indicating the significant market demand for bike sharing short-distance travel. However, the dock-based model's inflexibility, reliance on government-subsidized low pricing, and improper rebalance management constraint its scalability.

Case 2: OFO at Peking University (2015)

Because dormitories are so distant from classrooms in Peking University, OFO's founders deployed 2,000 dockless bikes to meet this demand. According to OFO operational report, there were 20,000 users, maximum daily use of 3700 times, cumulative use of 66,000 times . This not only confirmed the big market potential for dockless bike sharing but also highlighted the high-frequency, essential bike sharing demand among users.

The Role of Policy

The Chinese government actively promoted bike-sharing. First, learnt from European models. Second, it showed tolerance and implicit approval for practices that facilitated bike-sharing introduction, even when they violated non-motorized vehicles regulations. But the government also introduced policies to curb illegal activities associated with the market development.

Beyond the aforementioned pilot project near West Lake, in 2012, the government attempted to replicate the European public-private partnership model for bike-sharing in Wuhan. The government provided free land for docking stations, while private companies were responsible for operations. Users were required to pay a deposit before renting bikes, but these funds were misappropriated by managers. Additionally, the government's subsidy structure was based on the number of docking stations rather than operational performance, led to the project's failure. Both the Wuhan case and the West Lake case highlighted the capital-intensive nature of dock-based systems and underscored how China's institutional environment differed from Europe, making it unsuitable for a direct replication of Western countries' models.

In 2015, the Beijing municipal government allowed OFO to pilot dockless bikes on the Peking University campus, relaxing regulations despite its conflict with non-motorized vehicle management regulations. Rapidly increasing market penetration demonstrated the importance of regulatory relaxation in fostering the emerging technology growth.

During the early stage of bike-sharing market, government policies played three key roles: (1) by permitting operators to bypass conventional approval protocols, authorities encouraged dockless system innovation and enable the scalability of dockless bike sharing. (2) to address market saturation from highly competition, the deployment ban in 2017 was introduced, but failed to curb expansion, companies continued competing market share with lower price, further compressing profit margins. (3) after deposit misappropriation, polices restricting deposit forced operators to shift revenue drivers to ride fees. Companies were trapped in a paradox, unable to raise or lower prices to increase profit or market share.

Growth

During this stage, companies engaged in aggressive expansion through capital leverage, prioritizing market capture over profitability. According to China's economic financing reports, Mobike expanded to 200 cities within three years, while OFO raised over $8.66 billion in funding . Governments attempted to curb expansion through policies, but with limited effectiveness. For example, Beijing municipal mandated that companies allocate more staff to manage bikes, but still failed to stop such aggressive growth. As of September 2018, the weekly active penetration rate of OFO and Mobike dropped by about 75% .

Although the growth led to resource waste and inefficient competition, it greatly encouraged the adoption of bike-sharing. Penetration rates increase rapidly as demand surged. The penetration rate of shared bicycles increased by nearly eight times in 2017, and the high-frequency user index increased from 187 in January 2017 to 1229 in September 2017, a similar pattern was also seen in the distance and duration indices of shared bikes.

Maturity

During the maturity phase, companies adopted new technologies to grow revenue. (1) HelloBike leveraged AI-driven real-time deployment, more efficiently rebalance bikes and thus improve operation performance. (2) Meituan introduced dynamic pricing, adjusting fares based on different time of the day, for example, increasing prices during peak hours. (3) The government introduced Mobility-as-a-Service integration, as seen in Shanghai's Suishenxing app, which consolidated services from three major bike-sharing platforms.

Despite these developments, the market still faces growth bottlenecks. Users have long been costed 1 RMB per hour, making them resistant to higher price. To increase profit, some operators changed the pricing from 0.5 yuan per 30-minute ride to 1.5 yuan for the first 15 minutes and 1 yuan for every additional 15 minutes . According to the survey on the price increase, the number of people who said they would not continue to share bicycles accounted for 39.7% .

While demand for bike-sharing persists in the maturity stage, it primarily targets short-distance commuters. However, companies also face competition from substitutes such as electric-assist bicycles and shared e-scooters, among price-sensitive segment, bike-sharing competes with traditional bicycles, and among efficiency-focused segment, it competes with electric-assist bikes. Despite facing intense competition, AI-driven cost-saving measures can potentially improve operational efficiency, helping to stabilize profit .

Quantitative

The bike-sharing market size data from 2017 to 2023 is used to measure industry development. Since the logistic model can effectively fit the market lifecycle curve, it is assumed here that the market development of shared bicycles follows the logistic pattern. The corresponding model parameters are estimated to predict the market size.

The value of Smax is set as the largest historical value, 351.2 in 2023.

The equation $Y=\ln \left({\frac {S}{S_{\max }-S}}\right)$is used to calculate Y for different market sizes. Then, Y and X (year) are used in a linear regression to obtain the coefficients b and ti.

• S(t) is the market size (Billion Yuan)
• t is the number of year,
• ti is the inflection time = 1/2 Smax
• Smax is saturation status level, the largest historical value is used in this analysis.
• b is a coefficient to be estimated.  

Year	Market size (S)	Y = ln(S/(351.2 - S))
2017	130.3	-0.527870626
2018	180	0.050124387
2019	250	0.904362161
2020	320	2.327902901
2021	290	1.555733733
2022	304	1.862633809

The estimated coefficients, b = 0.511 and ti = 2017.49, are then input into the three-parameter logistic function $S(t)={\frac {S_{\max }}{1+e^{-b(t-t_{i})}}}$. Since ti represents the inflection time (the year in which S = 1/2 Smax), when ti = 2017.49, S(2017.49) = 175.6, which aligns with the expected value.

The model $S(t)={\frac {351.2}{1+e^{-0.511(t-2017.49)}}}$ is used to calculate the market size for different years.

Actual and predicted market size (Billion Yuan)

Year	Market Size (Billion Yuan)	Predicted Market Size (Billion Yuan)	Difference
2017	130.3	135.2	-4.9
2018	180	179.1	0.9
2019	250	238.4	11.6
2020	320	293.3	26.7
2021	290	318.6	-28.6
2022	304	332.4	-28.4
2023	351.2	339.7	11.5

R² = 0.882, indicating that the model explains 88.2% of the variations in market size, while the remaining 11.8% may be attributed to external factors such as policy interventions, the COVID-19 pandemic, or stochastic chaos. Under the assumption that no external factors influence the market, this model should effectively capture the overall trend.

Figure: Actual and predicted bike sharing market size (Billion Yuan) from 2017 to 2023.

The model can capture market development without the influence of external factors. The gap between the actual and predicted market size of bike sharing from 2020 to 2022 was larger, possibly due to several external factors such as regulations, COVID-19, and substitutes. (1) To constrain expansion and price competition, as discussed in the qualitative section, government policies are expected to impact pricing. Since market size = average price * number of users, even if the number of users increases, a decline in the average price will slow down market growth. (2) During COVID-19, lockdown policies restricted people from interacting with each other. Labor shortages may have caused a decline in operational efficiency, thus lowering rebalance efficiency compared to pre-COVID levels. (3) Lockdown policies reduced total travel demand, but bike sharing was considered a "contactless travel" option. As a result, market growth slowed but remained positive.

Bike sharing lifecycle

Years	Stage	Characteristics
2008-2017	Emerging	China government tried to introduce bike sharing in 2008 and 2012, OFO founder start dockless system in 2016. And the ti = 2017.4 also indicates that this market start rapidly increasing around mid of 2017, so the stage before 2017 should be classified as Emerging phase.
2017-2020	Growth	The market start rapidly growing between 2017 and 2018, the Figure also support this point, as the slope between 2017 and 2020 is steeper.
2020- present	Mature	In this stage, the market size grew slower than Growth phase, it should be classified as Mature stage.