Transportation Deployment Casebook/2025/Bike sharing in China

Qualitative

Technology

To meet the demands for short-distance and low-cost travel, bike sharing had been introduced as a green sharing economy rental model .

1. The use process is as follows:
2. Users can check the mobile app to locate nearby available shared bikes
3. Users fill in their information in the app and pay a deposit to obtain the right to use
4. Ride to the destination
5. Find the nearest parking spot through the mobile app
6. Park at the designated spot and pay the total journey fare and lock it in the mobile app.

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 them, 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 .

Bike-sharing targets frequent users like commuters and students who often use bikes to travel between home and public transportation, effectively addressing the "last mile" problem. It also serves less frequent users, such as tourists, who can ride on popular cycling paths, providing a flexible alternative to traditional modes.

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 revealed that insufficient management can result in the public assets loss, while Portsmouth Bikeabout validated the feasibility of smart card technology, positively influencing developments. With technological innovation, limitations in bike management and operating costs have been effectively addressed, setting the stage for modern bike sharing. Specifically, advances in three key areas have greatly contributed to its success.

GPS Technology

By 2016, as low-cost GPS modules became widespread, Mobike and ofo began integrating those commercial-grade 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 and high construction and maintenance costs, thereby limiting the bike sharing scalability. The advance in GPS technology expanded coverage and made dockless bike sharing possible . For example, while Amsterdam’s first-generation system served only 5 square kilometers in the city center, Hangzhou’s dockless model expanded the service area to 800 square kilometers. 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 was key to operational efficiency improvement. 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. This has greatly improved operators’ ability to manage bikes: (1) With its extensive coverage, operators can remotely monitor vehicle status and operations, such as controlling locks to prevent theft or misuse. (2) Bikes in various regions can be monitored in real time to assess their conditions or detect potential issues, enabling prompt maintenance of smart lock malfunctions. (3) Broader coverage and more stable communication allow for real-time monitoring and recording of various conditions during rides, 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 through a credit system that fairly assigns responsibility . Traditionally, users had to pay a deposit of 200 - 300 RMB; however, with the credit system, users can complete registration and start riding within 30 seconds, greatly enhancing user efficiency. On the operator side, a dynamic credit scoring model is employed to manage user violations such as illegal parking and bike damage. If misconduct occurs, the system deducts corresponding credit points, and these negative records are incorporated into the users' 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 group, leveraged existing infrastructure to launch a dock-based bike-sharing system near the West Lake scenic area, targeting the tourism market. The service aimed to address short-distance trips that traditional transit could not cover and to ease movement in congested areas surrounding the scenic spot. 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 usage, and 1 RMB for each additional hour. According to a report by the Hangzhou Transportation Bureau, 60% of users were tourists, and the system contributed to a 12% increase in local economic development, 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 dynamic dispatch management constraint its scalability.

Case 2: OFO at Peking University (2015)

Because dormitories are so distant from classrooms in Peking University was so long, OFO's founders deployed 2,000 dockless bikes to meet this travel demand. According to OFO operational report, 80% of students became users, and each bike was used around 4 times per day. 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 through three policy approaches. First, it aimed to promote public bicycles by drawing inspiration from European models. Second, it showed a degree of tolerance and implicit approval for certain practices that facilitated the introduction of bike-sharing, even when they technically violated non-motorized vehicles regulations. However, despite its eagerness to adopt bike-sharing, 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. Additionally, the government’s subsidy structure was based on the number of docking stations rather than operational efficiency, 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 tacitly allowed OFO to pilot dockless bikes on the Peking University campus, relaxing regulations despite its conflict with non-motorized vehicle management regulations. However, the high market acceptance of bike-sharing and OFO’s successful launch demonstrated the crucial role of regulatory relaxation in fostering the growth of emerging transport modes. The government’s regulatory ambiguity solidified the dockless market dominance, laying a strong foundation for its further expansion in market growth stage.

During the early stage of bike-sharing market, government policies played three key roles: (1) by tacitly permitting operators to bypass conventional approval protocols, authorities encouraged dockless system innovation and enable the viability of dockless bike sharing. (2) to address market saturation from highly competition, the 2017 deployment ban was introduced, but failed to curb expansion, companied continued competing market share, further compressing profit margins. (3) after Wuhan incident involving 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. These policies lay the groundwork for the industry’s decline after maturity.

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 deployed nearly 500000 bikes daily after raising over $2 billion in funding. Governments attempted to curb new deployment through policies, but with limited effectiveness. For example, Beijing municipal mandated in 2017 that companies allocate five staff per square kilometer to manage bikes, but failed to stop such aggressive growth. By 2018, 30% of China’s shared bikes was idle.

During the explosive growth phase, various industry irregularities began to emerge. For example, some executives illegally misappropriated user deposits. By the time OFO went bankrupt in 2018, unreturned deposits amounted to 3.2 billion RMB, but the company's assets were insufficient to cover the liabilities. According to a ruling by the Beijing First Intermediate People's Court, OFO diverted 1.8 billion RMB in deposits to pay off supplier debts instead of refunding users. The profit-driven nature of venture capital didn’t account for the sustainability of both the company and the industry. For example, GSR Ventures influenced OFO's decisions through its equity holdings, prioritizing aggressive market expansion over financial stability. They pushed OFO to capture market share in 100 cities within just 90 days, sacrificing cash flow and reinvesting raised funds into an unrestrained competition for market share, rather than sustainability.

Although the growth led to resource waste and inefficient competition, it greatly accelerated the adoption of bike-sharing for short-distance travel. Penetration rates increase rapidly as demand surged. According to data from the China Urban Rail Transit Association, shared bikes became the dominant mode of short-distance urban travel, with the proportion of trips under 3 km travel rising from 5% in 2015 to 65% in 2017. In Beijing and Shanghai, daily ridership approached 4 million trips, around15% users of the urban population.

Maturity

During the maturity phase, companies adopted different strategies to transform their business models and unlock new revenue growth. (1) HelloBike leveraged AI-driven real-time dispatching, using big data to handle deployment, improve real-time bike reallocation. As a result, the average daily turnover rate per bike increased from 1.8 to 2.3, while operational costs dropped by 37%. (2) Meituan introduced dynamic pricing, adjusting fares based on different time of the day, for example, increasing prices during peak hours. Even though order volume declined by 18%, revenue still grew by 12%, indicating the industry broke free from the previously mentioned "no price increase or decrease paradox.". (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, this expanded the user base by 120%.

Despite these developments, the market still faces growth bottlenecks. (1) Consumer price sensitivity: Users have long been costed 1 RMB per hour, making them resistant to higher price. So after HelloBike increased its pricing to 1.5 RMB in 2022, user attrition reached 18%. (2) Infrastructure compatibility and costs: Dockless bikes now account for 95% of the total bike-sharing, they lack compatibility with dock-based public transit systems. The high retrofitting cost makes it difficult to rebalance the dominance of dockless models. (3) Meituan had shifted focus from bike-sharing operations to platform integration. As a result, Meituan reduced its R&D budget for hardware from 12% in 2019 to just 3%, significantly constraint technology development.

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 users, bike-sharing competes with traditional bicycles, and among efficiency-focused users, it competes with electric-assist bikes. In Beijing, average daily bike-sharing trips have dropped to 2.5 million per city due to this shift in user preferences. Despite the decline in demand and revenue, and technology stagnation, AI-driven cost-saving measures can effectively improve operational efficiency, which can help stabilize profit.

Quantitative

The bike-sharing market size data from 2017 to 2023 is used to measure industry development.

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.

The estimated coefficients, b = 0.655 and ti = 2017.4, 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 = 2017.4, S(2017.4) = 175.6, which aligns with the expected value.

The model $S(t)={\frac {351.2}{1+e^{-0.655(t-2017.4)}}}$ 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.

https://github.com/Rzha9509/assignment/blob/main/marketsize20250309.png

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 can support this, as the slope between 2017 and 2020 is more steep.
2020- present	Mature	In this stage, the market size grew slower than Growth phase, it should be classified as Mature stage.