Effects of Dockless Bike-Sharing Systems on The

Transportation Research Part A 130 (2019) 398–411

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Transportation Research Part A

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Eﬀects of dockless bike-sharing systems on the usage of the London Cycle Hire T ⁎ Haojie Li , Yingheng Zhang, Hongliang Ding, Gang Ren

School of Transportation, Southeast University, China Jiangsu Key Laboratory of Urban ITS, China Jiangsu Province Collaborative Innovation Center of Modern Urban Traﬃc Technologies, China

ARTICLE INFO ABSTRACT

Keywords: This paper evaluates the effects of dockless bike-sharing systems on the usage of the London Dockless bike sharing Cycle Hire (LCH). A total number of 707 LCH docking stations are included for a period of London Cycle Hire 36 weeks. Covariates such as socio-demographic characteristics, traffic conditions, and the built Causal analysis environment are included in the data set. A difference in difference (DID) based propensity score matching (PSM) method is applied to select control docking stations and estimate the effects of the dockless bike-sharing systems on the LCH usage. The results suggest that a significant reduction of 22.42 (5.93%) in the average weekly usage of each LCH docking station is caused by the dockless bike-sharing system after its initial launch. We also investigate the effects of dockless bike-sharing systems on the characteristics of the LCH trips, including travel duration, travel distance, and departure time. The majority of trips replaced by the dockless bikes are short duration (0–15 min) and middle distance (1–3 km) trips. The reduction in the average weekly short duration trips of each station is 16.91 (7.16%), while the figure is 15.11 (6.74%) for middle distance trips. The average travel duration is increased by 0.25 min, but the travel distance shows no significant change due to data restriction. Furthermore, the LCH usage is significantly reduced by 6.85% during the weekday commuting peak, and 10.47% during the weekend leisure peak. As to weekday off-peak and other times, reductions are not statistically significant.

1. Introduction

Cycling has been a popular travel mode in many cities around the world, which can help to relieve traﬃc congestion and reduce emissions. There has been a rapid growth in the number of cyclists in the past few decades around the world, including London. Numbers of policies and investments in new facilities have inspired the ridership of bicycles in London, including the London Cycle Hire (LCH) and the Cycle Superhighway (CS). By the end of 2017, the dockless bike-sharing system had become a new option for London cyclists. The LCH was launched in July 2010. By the end of 2018, more than 11,500 bikes at over 750 docking stations had been put into use across London (TfL, 2018a). In 2017, the dockless bike-sharing systems were launched in several London boroughs (London is a city that has 33 districts – the 32 London Boroughs and the City of London. For detailed information of London Borough, please refer to LondonCouncils, 2019). Multiple systems, including Mobike, Ofo, Urbo, and LCH were competing in London. For example, in 2017, Santander bikes (LCH’s bikes) had their best November with 791,961 hires (Guardian, 2017a), while Mobike also had high

⁎ Corresponding author. E-mail address: [email protected] (H. Li). https://doi.org/10.1016/j.tra.2019.09.050 Received 13 December 2018; Received in revised form 30 May 2019; Accepted 25 September 2019 0965-8564/ © 2019 Elsevier Ltd. All rights reserved. H. Li, et al. Transportation Research Part A 130 (2019) 398–411

Fig. 1. London Cycle Hire docking stations. usage rates since launching in Ealing (Bikebiz, 2018). Although the previous studies suggest that the dockless bike-sharing system is different from the dockbased one in several aspects, and it does have impacts on other travel modes (Albiński et al., 2018; McKenzie, 2018; Yang et al., 2018), it remains unclear whether the dockless system influences the dockbased one, and how these two systems interact on each other. This paper contributes to the literature by evaluating the effects of the dockless bike-sharing systems on the usage of the LCH. A difference in difference (DID) based propensity score matching (PSM) method is applied to make causal inferences on the effects. This paper is organized as follows. The introduction of the LCH and the dockless bike-sharing system is presented in the next section. The method and data used in this analysis are separately illustrated in Section 3 and 4. The results are given in Section 5, followed by the discussions and conclusions in the final section.

2. Background

In this section, we ﬁrst introduce the LCH and the dockless bike-sharing systems in London. Then we review the literature on both bike-sharing systems.

2.1. London Cycle Hire

The LCH commenced the operation in July 2010 with 5000 bicycles and 315 docking stations. There are two access fee options for hiring a Santander bike, 24-h access for 2 pounds or annual access for 90 pounds. The first 30 min of each journey is free. And for longer hire durations, the price increases by 2 pounds every extra 30 min. Fig. 1 shows the LCH docking stations available in De- cember 2018. The LCH stations are located across eleven London boroughs and in several Royal Parks in the central London. The LCH had achieved the 50 million hires milestone by 2016. In the past few years, the researches on bikeshare have focused on several popular topics, such as the impacts of bikeshare on other travel modes, health and environment benefits of bikeshare, and the factors affecting the usage of bike-sharing systems. The impacts of bikeshare on other travel modes are similar across many cities around the world. Numerous studies suggest that the majority of the bike-sharing systems’ users come from other public travel modes (Fishman et al., 2013; Midgley, 2011). As to the LCH, Fishman et al. (2014a,b) established the degree to which car trips were replaced by bikeshare through an examination of survey and trip data. Their results show that due to a low car mode substitution rate and the substantial truck use for rebalancing of the bikes, London’s bike-sharing program recorded an additional 766,341 km in the motor vehicle use. In contrast, Transport for London (TfL) found that the bike-sharing program in London encourages the private bike use (TfL, 2011). Reducing the car usage is not the

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Fig. 2. Bike-sharing systems in London. (Source: https://en.wikipedia.org; https://mobike.com/global; https://www.londoncyclist.co.uk.) only expected benefit of the bikeshare schemes. Many researchers believe that bikeshare also has health and environment benefits (Woodcock et al., 2014; Zhang and Mi, 2018). A number of studies have been conducted regarding the factors influencing the usage of bike-sharing systems. For instance, socio- demographic characteristics, such as population density, incomes, employment and deprivation can influence the bikeshare usage and membership (Fishman et al., 2014a,b; Jain et al., 2018; Ogilvie and Goodman, 2012; Zhang et al., 2017; Zhao et al., 2014). Another set of factors affecting the bikeshare ridership are surrounding built environment characteristics, including land use types, road density, proximity to the public transport (e.g. railway and bus stations) and cycling infrastructure (e.g. cycle lanes). The findings are not consistent and vary across different studies depending on the cities, bike-sharing programs and methods used (Bachand-Marleau et al., 2012; Cervero et al., 2009; Faghih-Imani et al., 2017, 2014; Osama et al., 2017; Zhao et al., 2015). A recent study by Li et al. (2018) suggested that the London Cycle Superhighways have increased the LCH usage of docking stations within 300 m from either side of the CS routes. There are some other factors closely related to the usage of bike-sharing systems. Tang et al. (2017) found that alternatives to public bikes and the main purpose of non-commuting trips by public bikes exert the greatest impact on the ride frequency in Minhang, China. Weather conditions also have impacts on the usage of bike-sharing systems (Campbell et al., 2016; Gebhart and Noland, 2014). In the next section, we introduce the dockless bike-sharing system.

2.2. Dockless bike-sharing system

The dockless bike-sharing systems were first put into use in London in 2017, led by oBike, Mobike, Ofo, and Urbo. The dockless bikeshare is a new mode of short-term bikeshare scheme, similar to the dockbased system, but with no docking stations. The dockless systems offer the users more flexibility and help to avoid the risk of not being able to end/start a ride due to a docking station being full/empty. Having started booming in China, the APP-based dockless bikeshare is spreading around the world. Billions of dollars are being pumped into the dockless bike market because of the data-mining potential (Bikebiz, 2017). Fig. 2 shows London’s three main bike-sharing systems in 2018. The first figure is Santander bikes (dockbased), which are popular known as Boris Bikes. And the other two are Mobike and Ofo (dockless). At the end of February 2019, London had at least five dockless bikeshare schemes. Mobike, the first compliant (working with local authorities) dockless bike-sharing system in London, announced the launch of its service in London on July 31, 2017, starting with the London Borough of Ealing. Cyclists had to pay 29 pounds deposit to join the scheme and would be charged 50 pence for 30 min. Ofo released 200 dockless bikes in Hackney on September 7, 2017. The Irish firm, Urbo, launched its first scheme in London in October 2017, dropping 250 of its bikes in Waltham Forest. Urbo cooperated with the borough council as well. As to Ofo, cyclists would also be charged 50 pence for 30 min, and Urbo cost 1 pounds to become a member and 50 pence for each half hour (fees vary from different areas and periods). However, Urbo pulled out of London on July 4, 2018. Mobike pulled out of Newham and Southwark in June and September 2018 separately. Singapore’s oBike, another dockless bike- sharing system in London, was launched in July 2017 without coordinating with local boroughs. We did not take oBike into con- sideration because the system had around 400 bikes in all London, which made it unlikely to affect the usage of the LCH in specific boroughs/areas. And oBike did not operate well. Since August 2017, many oBikes had been impounded by the local authorities

Table 1 London’s dockless bikeshares.

Borough Launching Firm Number of launched dockless bikes

Hackney September 2017 Ofo 200 Islington & City of London November 2017 Ofo 100 (Islington) & 100 (City) Islington November 2017 Mobike 200 Southwark March 2018 Ofo & Mobike 200 (Mobike) & 200 (Ofo) Newham March 2018 Mobike 300 Camden June 2018 Ofo 200 Wandsworth June 2018 Ofo No detailed information Camden July 2018 Mobike No detailed information

Notes: Only the boroughs considered in this study are listed.

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Fig. 3. Operation areas of the dockless bikes (September 2018).

(Guardian, 2017a) and there were no oBikes available after October 2017. Table 1 shows the dockless bike-sharing systems launch dates and the number of dockless bikes launched in different boroughs. Only the boroughs studied in this paper are listed in the table. Detailed operation areas of the dockless bike-sharing systems (till September 2018) are shown in Fig. 3. It is also worth noting that the dockless bikes are decided based on borough permissions rather than a particular desire by the operator, which has led to the fragmented nature in the operation areas as shown in Fig. 3. Latest updates of the dockless bike-sharing systems in London can be found in O’Brien’s Bike Share Map. After the launch of the dockless bikes, TfL released a code of practice for the dockless bike-sharing companies. TfL believes that the dockless bikeshare has the potential to make cycling more accessible and attractive for Londoners (TfL, 2018b). Local authorities also greeted the arrival of the dockless bike-sharing schemes. Julian Bell, the leader of Ealing Council, expressed his hope for seeing more residents leave their cars at home and switch to a Mobike instead. Walking and Cycling Commissioner Will Norman said, “Dockless bikes have real potential to make cycling easier and more accessible, and it is also important that the new operators work closely with local boroughs and TfL during and after their launch.” (Timeout, 2017). Because the dockless bikes are cheaper, fun, and lighter, some cyclists prefer to ride dockless bikes rather than Santander bikes (Guardian, 2017b). However, Patrick Collinson found that Mobike has low usability, and the Santander bike is the only option for riders 1.83 m (6 ft) or above in height (Guardian, 2018). In addition, the dockless bikes took away the space on pavements, and many of them were vandalized or broken, which caused many complaints. It remains unclear whether the dockless system has impacts on the LCH after its initial launch. Several studies have been conducted on the differences between the dockbased and dockless systems. For instance, Mckenzie (2018) investigated the spatial and temporal dimensions of the dockbased and dockless bikeshare services in Washington, D.C. The findings show that the Capital Bikeshare (dockbased) tends to be more commuter focused whereas LimeBike (dockless) reflects more leisure or non-commute related activities. Albiński et al. (2018) investigated a hybrid bike-sharing system in Munich. They analyzed the number of trips taken and reservations from the dockless and dockbased bikes, and found that there were significant differences between the dockless and dockbased bikes, and 90% of the trips were made from the dockless bikes, indicating that the dockless bikes can make the system more attractive. Regarding the relationship between the dockless bike-sharing system and other travel modes. Yang et al. (2018) developed a spatial analysis that sought to quantify the relationship between the dockless bike-sharing and metro systems. Their results show that new metro service has significantly positive impacts on the dockless bike-sharing mobility patterns. Sun (2018) found that the dockless shared bike is not an effective alternative for the frequent car-users, just like the dockbased one. Jia and Fu (2019) applied a cross-sectional study to investigate the changes in the travel modes before and after the launch of dockless bikes. The effectiveness of the dockless bikeshare on promoting the bike usage in both commuting and non-commuting trips was confirmed.

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Some other studies have focused on the factors influencing the usage of the dockless bikes. For example, Zhou et al. (2018) suggested that solving problems of unsafe cycling environment and bike unavailability will remarkably increase the usage of the dockless bikes. In addition, land use, weather conditions and access to public transportation can also affect the usage of the dockless bikes (Shen et al., 2018). Although dockless bike-sharing systems have great advantages of flexibility, affordability, and efficiency, there are some problems in London and many other cities. Gu et al. (2019b) concluded that financial sustainability, vandalism and threat to bike industry by dockless bikeshare are the three main challenges that require investigation. And rebalancing dockless bikes has become a popular research topic. Different methods have been proposed to solve this problem.

2.3. Objective

Cycling has already become a popular travel mode for commuting and recreational activities in London. London published Cycling and walking investment strategy in 2017, and Cycle city ambition baseline and interim evaluation in 2018 (Gov. UK, 2018). In addition, London has implemented two outstanding programs for improving the quality and safety of cycling, the London Cycle Hire and the Cycle Superhighway. In 2017, the dockless bike-sharing systems started to appear in London and attracted the public attention. Although a few studies have been conducted regarding the dockless bike-sharing systems, to the best of the authors’ knowledge, the impacts of the dockless shared bikes on the dockbased system have rarely been studied. This study aims to conduct a quantitative analysis on the eﬀects of the dockless bike-sharing systems on the usage of the LCH.

3. Method

3.1. Propensity score matching

For causal analysis, a critical issue is that it is impossible to observe the outcomes of the same unit with and without the treatment.

In the case of a randomized experiment, the treatment status Ti is unconditionally independent of the potential outcomes Yi. Under the particular circumstances of randomized experiments, we can employ a control group of untreated units and simply estimate the difference in mean outcomes as the treatment effect. However, randomized experiments are not always feasible due to high costs and ethical problems. PSM is an approach based on observational data. The idea behind the PSM method is to construct a control group that is similar to the treatment group in all relevant pre-treatment covariates X. The PSM method has the advantage of reducing the multiple dimensions of matching to a single dimension, the propensity score, which is the conditional probability of assignment to a particular treatment given a vector of observed covariates. Observed differences in the outcomes between the treatment and control groups can be solely attributed to the treatment effects. In other words, adjusting for the propensity score is enough to eliminate the bias due to all confounding factors.

3.1.1. Notations

Let Ti denotes the treatment indicator, where Ti = 1 if unit i receives the treatment and otherwise Ti = 0. The potential outcome for unit i is deﬁned as Yi(T), where i =1,… , N and N denotes the total population. The treatment eﬀect for unit i can be calculated as:

δYii=−(1) Y i (0) In practice, the parameter of interest is usually the average treatment eﬀect on the treated (ATT), which is deﬁned as:

δEδTEyTEYTATT ====−=( | 1) ( (1)| 1) ( (0)| 1).

3.1.2. PSM assumptions There are three crucial assumptions underlying the PSM method, which were introduced by Rosenbaum and Rubin (1983). Assumption 1 Stable Unit Treatment Value Assumption (SUTVA): This assumption requires that the treatment does not have impacts on any other unit other than the treated ones. Assumption 2 Conditional Independence Assumption (CIA): (YY(1), (0))⊥ TX | . This assumption is also known as the unconfoundedness condition and states that the potential outcomes are independent of the treatment status after controlling for covariates X. Assumption 3 Common Support Condition (CSC): 0 <=PT(1|) X <1 (Overlap). This assumption is also known as the overlap condition. The CSC ensures that units with the same X values have a positive probability of being both treated and untreated. In other words, the overlap assumption ensures that there is suﬃcient overlap in the characteristics of the treated and untreated units to ﬁnd adequate matches.

3.1.3. Implementing PSM The procedure for estimation of treatment eﬀects using the PSM method can be illustrated in three steps:

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(1) The propensity score needs to be estimated ﬁrst. The Logit and Probit models are usually applied to estimate the propensity scores. Previous study by Smith (1997) suggests that the results from both models are similar. In this study, we choose the Logit model: EXP() α+ β′ X P (1|)TX== 1()++EXP α β′ X where α is the intercept and β′ is the vector of logit regression coeﬃcients. Variables that have impacts on both the selection of treatment groups and potential outcomes should be added into the model to satisfy the unconfoundedness condition. In section 4, the covariates X included in the PSM model will be discussed.

(2) Choosing matching algorithm. An algorithm is to find appropriate control units from untreated pool. There are four frequently used algorithms: K-nearest neighbors matching, caliper and radius matching, kernel and local linear matching, stratification and interval matching. Generally, multiple matching algorithms should be tried and the results can be compared to reinforce the validity. (3) Estimating the treatment effects. The treatment effects can be evaluated by taking differences in outcomes between treated units and their matched units. Many programs are available in different statistical software. The program used in this study is psmatch2 in STATA developed by Leuven and Sianesi (2003).

It is worth noting that the DID matching approach is applied in this study. In some cases, the CIA is too strong, and may not hold when unobserved factors that may inﬂuence the outcomes are not included in the model. The DID matching estimator can relax the strong CIA, given that pre-treatment data are available and unobserved variables are time-invariant (Heckman et al., 1997). For treated units, the dependent variable is the outcome diﬀerences over pre-treatment and post-treatment periods. The outcome difference is calculated over the same periods for untreated units:

ΔYYiitit=−′ Y where t and t′ denote the pre-treatment and post-treatment periods respectively. The DID matching estimator can reduce the bias due to differences between treatment and control groups, given that differences in their effects on outcomes are time-invariant. The treatment effect can be calculated by the procedures discussed above. In summary, the procedure for using PSM method to construct the control group and estimate the effects of the dockless bike- sharing systems on the usage of the LCH can be illustrated as following steps:

(1) The data for all the treated and untreated stations, including usage, built environment, traffic condition and socio-demographic characteristics are aggregated in a single data set. (2) Covariates are selected and put into the Logit model. (3) The propensity scores are estimated for all the docking stations. (4) The distributions of the propensity scores are compared between the treated and untreated docking stations to check the overlap condition. If the condition is not satisfied, the covariates need to be re-selected. (5) Multiple matching algorithms are applied to increase the credibility of the PSM model. (6) A balancing test is conducted. If significant differences exist, Logit model is re-specified and the process is repeated. (7) The effects of dockless bikes can be evaluated by taking differences in the LCH usage between the treated and their matched control docking stations.

4. Data

4.1. Covariates

The validity of the PSM method heavily relies on the unconfoundedness assumption, which is unfortunately untestable. However, its influence can be mitigated by capturing as much information about the potential confounders as possible. Theoretically, covariates that affect the launch of the dockless bike-sharing systems and the ridership of the bikeshare service should be included in the models. A previous study by Brookhart et al. (2006) illustrated how the choice of covariates added into the propensity score model can affect the variance, bias, and mean squared error of the estimated treatment effects. They suggested that the optimal practice, in terms of bias and precision, is to include all the covariates that have impacts on the outcome regardless of whether they influence the treatment assignment. In contrast, adding a covariate related to the treatment assignment but unrelated to the outcome will increase the variance without decreasing the bias. Currently, there is no clear criterion of selecting the dockless bike launch areas in London. So covariates are chosen empirically, based on previous studies, e.g. factors affecting the usage of the LCH are considered in the model. The literature suggests that socio-demographic characteristics, such as population density, incomes, employment and deprivation can influence the bikeshare usage (Ogilvie and Goodman, 2012; Zhang et al., 2017; Zhao et al., 2014; Jain et al., 2018; Fishman et al., 2014a,b). A Previous study by Ogilvie and Goodman (2012) suggested that the registered LCH users are more likely to live in the areas of low deprivation and high cycling prevalence. To control for this condition, the data for population and employment were also obtained from the Office for National Statistics (ONS). The Index of Multiple Deprivation (IMD) was obtained from the Deputy Prime

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Minister’sOffice. IMD is a measure of relative deprivation for small areas. Land use characteristics can also affect users’ decision to use the bikeshare service (Osama et al., 2017; Bachand-Marleau et al., 2012; Faghih-Imani et al., 2014). The key features included in this study are the percentages of domestic buildings, non-domestic buildings, and greenspace. In addition, nearby road characteristics also have impacts on the usage of the bike-sharing systems. We further included the information of the density of different types of road. It is worth noting that the above information was obtained based on the lower-super-output-area (LSOA), which is the primary unit of British administrative and electoral geography with an average population of 1500. Another built environment factor influencing the bikeshare ridership is cycling infrastructures (Cervero et al., 2009; Faghih-Imani et al., 2014; Zhang et al., 2017; Li et al., 2018). In London, twelve CS routes were initially planned to radiate from the central London based on the clock face layout, and eight of them had been completed by 2018 (TfL, 2018c). As discussed earlier, the effect area of the CS is 300 m from either side of the CS routes (Li et al., 2018). We added an indicator variable – whether the docking station is within the CS routes’ 300 m buffer area to control for such effects. Neighboring docking stations can also influence the bikeshare usage (Zhang et al., 2017). So the number of neighboring docking stations within 400 m of each docking station is included in the model. The reasons for choosing 400 m are as follows. First, the observations for a smaller radius such as 100 and 200 m contain excess zero counts. Second, Daniels and Mulley (2013) suggested that 400 m is often used in guidelines as the key walking distance in public transport network and service planning. Regarding the influence of other public transport modes, Jain et al. (2018) suggested that long-term subscribers’ usage is positively related to the proximity to the major transport hubs. Similar results are shown in many other studies (Weng et al., 2018; Zhao et al., 2015; Jäppinen et al., 2013; Gu et al., 2019a). In this study, we obtained the average daily passenger volumes data for railway stations within 400 m of each docking stations, including London Underground, Overground, Dockland Light Railway and part of National Rail services. The number of neighboring bus stations is also considered. It is also important to control for the nearby traffic conditions when analyzing the usage of the bikeshare services. The traffic data were collected from the Department for Transport (DfT). Data are available for each link on the major road network and for the sample of points on the minor road network. In this study, annul average daily traffic (AADT) within 400 m of each docking station was obtained. In addition, we added the bicycle volumes into the model to control for the nearby cycling environment condition. In summary, the covariates included in the PSM model are shown in Table 2.

4.2. Sample size

A total number of 707 docking stations are included in this study. The transaction records of the LCH were obtained from TfL. The records of each station are aggregated at the week level, which covers a period from July 2017 to March 2018. Each transaction record includes rental ID, bike ID, hire duration, start station and end time, start station and end station, and station name as shown in Table 3. The PSM method is known as a “data-hungry” method in terms of the number of treated and untreated units. Matching can be only implemented when there is sufficient overlap between the treated and untreated groups for every propensity score block. Therefore, a large untreated pool is required. In this study, Hackney, Islington and the City of London are chosen as the treated areas. There are two reasons. First, the ratio is around 6:1 (607 untreated stations and 100 treated stations), which is assumed sufficient to ensure the matching quality. Second, the dockless bikes’ launch dates of these three boroughs are close to each other, which makes it possible to determine the pre-treatment and post-treatment periods. In addition, in the second half of 2018, the dockless bike-sharing schemes reduced their operation areas in some boroughs due to problems such as theft and vandalism. Thus, the period from July 2017 to March 2018 is chosen as our study period. It is worth noting that the DID matching approach is applied in this study. The outcome variable is the difference in the usages

Table 2 Descriptive statistics of the covariates.

Variables Description Mean S.D. Max Min

Cycle hires Usage of docking station (July 2017 to March 2018) per week 236.88 189.58 2361 2 Population density Population per km2 10056.1 6766.98 44589.5 320.95 Employment density Number of employees per km2 3714.9 2550.57 17252.3 135.50 Affected by CS or not Whether in Cycle Superhighways 300 m buffer 0.215 0.411 1 0 Number of railway passengers The number of railway passengers within 400 m of the docking stations 17,741 33,636 161,241 0 Number of bus stations The number of bus stations within 400 m of the docking stations 17.90 9.76 69 0 Number of neighboring docking stations The number of neighboring docking stations within 400 m of the docking 4.72 2.04 11 1 stations A road density The density of A road per km2 2.90 2.36 13.07 0 B road density The density of B road per km2 0.75 1.32 9.03 0 Minor road density The density of minor road per km2 10.90 5.75 32.19 0.19 Domestic Percentage of domestic buildings, e.g. residential area 0.1294 0.0751 0.4543 0.004 Greenspace Percentage of green area, e.g. parks 0.1471 0.1354 0.7018 0 AADT Average annual daily traffic on neighboring road of the docking stations 46,475 46,758 246,228 0 Bicycles Bicycle volumes on neighboring road of the docking stations 5895 7557 43,130 0 IMD The index of multiple deprivation 4.29 2.09 9 1

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Table 3 Sample of transaction records.

Rental Id Duration Bike Id End Date EndStation Id EndStation Name Start Date StartStation Id StartStation Name

67,512,538 120 7182 19/07/2017 00:02 744 Ingrave Street, 19/07/2017 00:00 735 Grant Road East, Clapham Junction Clapham Junction 67,512,539 1380 4324 19/07/2017 00:23 642 Fawcett Close, 19/07/2017 00:00 762 Storey's Gate, Battersea Westminster 67,512,540 840 2588 19/07/2017 00:14 30 Windsor Terrace, 19/07/2017 00:00 34 Pancras Road, Hoxton King's Cross 67,512,541 1440 10,884 19/07/2017 00:24 332 Nevern Place, 19/07/2017 00:00 20 Drummond Street, Earl's Court Euston between the pre- and post-treatment periods. To ensure that the eﬀect estimates are temporally consistent for all the three treated areas, the data from November 2017 to March 2018 (18 weeks) are used for the post-treatment (after the launch of the dockless bike- sharing systems) period. The pre-treatment period also contains data for 18 weeks. For the untreated stations, the pre- and post- treatment periods are deﬁned the same as the treated stations.

5. Results

5.1. Preliminary analysis

In this section, we first conduct a preliminary analysis on the usage of the LCH. Fig. 4 shows the time trend of the average weekly usage of each LCH station in the treated and untreated areas. Fig. 5 also presents the LCH usage before and after the launch of the dockless bikes. It can be seen that both the usages of the treated and untreated groups show a declining trend, which could be due to seasonality. And during the Christmas Holiday, the usage came to a low peak period. However, there is no obvious decrease in the difference (grey line in Fig. 4). In addition, the average usage in the pre-treatment period is higher for the treatment group. And it is possible that the treatment group has a higher number of reduction because its usage level is high in the first place. So it cannot be concluded that the dockless bikes have effects on the usage of the LCH from these figures. However, this issue can be partly tackled by the PSM method.

5.2. Validity tests of PSM

Before estimating the eﬀects of the dockless bike-sharing systems on the usage of the LCH, it is important to construct a control group which has similar characteristics to those of the treatment group, i.e. the stations in the control group must be representative of

Fig. 4. Time trend of the average weekly usage of the LCH.

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Fig. 5. Average weekly usage in the pre- and post-treatment periods. the treated ones. The LCH stations in Hackney, Islington and the City of London are defined as the treated stations, while the others are the untreated. Then, the PSM method is used to construct the control group from the untreated LCH docking stations. The validity of the PSM method can be checked via a visual inspection of the propensity score distribution for both treated and untreated groups. From the histograms of the propensity scores for both groups, we can investigate the extent to which there is an overlap in the propensity scores between the treated and untreated groups. Units that fall outside the common support region (off- support) should be discarded. Fig. 6 shows the distribution of the propensity scores for both groups. It can be seen from the histograms in Fig. 6 that the propensity scores have similar ranges across the two groups and overlap well, indicating that the overlap condition is plausible. The balancing test is performed subsequently, which can verify that the treatment is independent of the covariates after matching. The matching method aims to balance the covariates affecting the potential outcome between the treatment and control groups, i.e. there should be no significant difference in the variables means between the treatment and control groups after matching. Table 4 shows the t-test on the differences in the covariates means before and after matching. It shows that there are significant differences (P < 0.05) in most of the covariates before matching. The PSM method is used to refine the control group. It can be seen that the bias due to the differences in the covariates is reduced (as shown in Table 4). All the covariates are balanced between the treatment and control groups after matching. In addition, it is worth noting that the difference in the average weekly usage between the treated and untreated groups during the pre-treatment period is also reduced by 55.8% after matching (as shown in Fig. 7).

Fig. 6. Propensity score distribution by treatment status.

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Table 4 Balancing test between the treated and control groups.

Variables Unmatched Mean Reduce(%) t-test Matched Treated Controls %bias |bias| t P > |t|

Population density U 8000.5 10,395 −36.8 −3.30 0.001 M 8392.7 7995.9 6.1 83.4 0.44 0.660 Employment density U 2823.1 3861.8 −44.3 −3.81 0.000 M 2955.5 2842.8 4.8 89.1 0.37 0.711 Aﬀected by CS or not U 0.220 0.214 1.4 0.13 0.896 M 0.211 0.201 2.2 −59.4 0.16 0.875 Number of railway passengers U 17,414 17,795 −1.2 −0.10 0.917 M 18,331 17,265 3.3 −180.0 0.22 0.823 Number of neighboring bus stations U 22.13 17.20 54.5 4.76 0.000 M 21.52 22.17 −7.2 86.8 −0.47 0.636 Number of neighboring docking stations U 5.720 4.555 61.1 5.39 0.000 M 5.653 5.585 3.5 94.2 0.23 0.821 A road density U 2.56 2.96 −16.8 −1.55 0.122 M 2.62 2.81 −8.2 51.2 −0.58 0.561 B road density U 0.99 0.71 20.1 1.97 0.049 M 1.03 1.00 2.2 89.3 0.14 0.892 Minor road density U 9.46 11.13 −32.9 −2.71 0.007 M 9.69 9.86 −3.3 90.0 −0.23 0.817 Domestic U 0.096 0.135 −63.3 −4.93 0.000 M 0.097 0.095 2.0 96.8 0.18 0.857 Greenspace U 0.122 0.151 −24.0 −2.02 0.043 M 0.123 0.136 −8.2 65.8 −0.57 0.567 AADT U 73,728 66,509 11.1 1.00 0.316 M 71,153 70,542 0.9 91.5 0.06 0.949 Bicycles U 9190.4 5352.0 48.6 4.78 0.000 M 8651.9 8873.6 −2.8 94.2 −0.17 0.865 IMD U 4.26 4.29 −1.5 −0.14 0.889 M 4.15 3.93 10.2 −589.6 0.75 0.455

Fig. 7. Density plots of the usage before and after matching.

5.3. Causal eﬀects of dockless bikes on LCH

In this section, the effects of the dockless bikes on the usage of the LCH are evaluated. Since multiple matching algorithms can be used when employing the PSM method, the robustness of the results should be checked to ensure that the estimation does not depend on the chosen algorithm. In this study, results from five matching algorithms, three of which are of one type (K-nearest neighbors matching), are compared to reinforce the validity of the PSM model. The five algorithms applied are K-nearest neighbors matching (K = 1), K-nearest neighbors matching (K = 3), K-nearest neighbors matching (K = 5), kernel matching (bandwidth = 0.05) and radius matching (caliper = 0.05). Table 5 shows that the observed reduction in the average weekly usage is 14.39 in absolute numbers before matching. When applying the PSM method, the results are similar for all the five algorithms except K-nearest neighbors matching (K = 1), which indicates that K-nearest neighbors matching (K = 1) may be inappropriate. The average reduction in the weekly usage of each LCH station is around 22.42 (5.93%). Such similar results indicate that the estimations are independent of the algorithms applied and increase our confidence in the PSM method. Thus, in the following processes, K-nearest neighboring matching (K = 5) is used to estimate the treatment effects. Considered that the dockless bikes may also have impacts on the usage of the LCH stations in the adjacent untreated areas. We set the docking stations within 400 m of the three treated boroughs

407 H. Li, et al. Transportation Research Part A 130 (2019) 398–411

Table 5 Eﬀects of dockless bikes on the weekly usage of the LCH.

Models Treated Controls Eﬀect S.E. T-stat

Unmatched −115.40 −101.01 −14.39 9.52 −1.51 DID propensity score matching K-nearest neighbors matching (K =1) −115.40 −98.82 −16.59 11.22 −1.48 K-nearest neighbors matching (K =3) −115.40 −93.29 −22.11 9.12 −2.42* K-nearest neighbors matching (K =5) −115.40 −93.30 −22.10 9.06 −2.44* Radius matching (caliper = 0.05) −115.40 −92.70 −22.70 9.80 −2.32* Kernel matching (bandwidth = 0.05) −115.40 −92.63 −22.77 9.88 −2.31*

Notes: * - Figures are significant at 95%. as the potential influenced group. However, the results in Table 6 suggest that the dockless bikes have no significant effects on the LCH usage in the adjacent areas. In addition, we investigated the effects of the dockless systems on the LCH trips with different hire durations. The LCH trips are divided into three groups: short duration trips (0–15 min), middle duration trips (15–30 min), and long duration trips (30–60 min). The reasons for such categorization are as follows. First, as to Santander bikes, the two access fee options were 24-h access for 2 pounds or annual access for 90 pounds. The first 30 min of each journey is free, and for longer hire durations, the price increases by 2 pounds every extra 30 min. Before June 2018, Mobike and Ofo both charged 50 pennies for every 30 min. So we suspect that 30 min is a key threshold value. Second, as a manpower travel mode, few single trips last for more than one hour. And the data show that the LCH trips with a duration over 60 min only account for 2.87% in total. Table 7 shows that the average weekly short duration trips of each station significantly reduced by 16.91 (7.16%). Middle and long duration trips show no significant changes. Table 8 shows the effects of the dockless bike-sharing systems on the average travel duration of the LCH. When applying the PSM method, the average increase in the travel duration caused by the dockless bikes is 0.25 min (14.56 s), which is also statistically significant, indicating that the dockless bikes mainly replace the LCH trips with a short duration. In terms of the travel distance, we divided the LCH trips into three groups based on lower quartile and upper quartile of the travel distance: short distance trips (0–1 km), middle distance trips (1–3 km), and long distance trips (over 3 km). The results in Table 9 suggest that the dockless bike-sharing systems significantly reduce the trips with a distance between 1 and 3 km and over 3 km. The reductions in the average weekly trips are 15.11 (6.74%) and 5.92 (7.61%) separately. The results show no statistically significant effects on the average travel distance (as shown in Table 10). A possible reason is that the data employed are transaction records, which have no information on specific cycling routes. And only the Euclidean distance between the start station and the end station is studied. We further investigated the effects of the dockless bikes on the usage of the LCH on weekdays and weekends by different periods. We assume that the “commuting peak” is from 6:30 to 9:30 and 16:00 to 19:00 on weekdays. For Weekends, a “leisure peak” from 10:00 to 16:00 is defined based on the raw data. Table 11 shows that the LCH usage is significantly reduced by 6.85% during the weekday commuting peak, and 10.47% during the weekend leisure peak. For the usage during other times, the reductions are not significant.

6. Discussions and conclusions

Bike-sharing systems have existed for decades. In recent years, the dockless bike-sharing systems started to appear in many countries. The dockless bike-sharing schemes provide a new transport alternative for short distance trips. After the initial launch in London in 2017, the dockless bikes were welcome by the local authorities and the public. Despite that a number of studies have been conducted on the dockless bike-sharing systems, their effects on the traditional dockbased system are still unclear. This study contributes to the literature by applying causal models to evaluate the effects of the dockless bike-sharing systems on the usage of the LCH. A DID based PSM method is employed to evaluate the effects. The results suggest that the average reduction in the weekly usage of each LCH station caused by the dockless bikes is 22.42 (5.93%). Furthermore, we also investigated the effects of the dockless systems on the characteristics of the LCH trips, including travel duration, travel distance, and departure time. The results suggest that the majority of the LCH trips replaced by the dockless bikes are short duration trips (0–15 min). The average weekly short duration trips of each LCH station were reduced by 16.91 (7.16%). There are two potential reasons. First, the dockless bikes are cheaper for short trips for casual users. Santander bikes cost at least 2 pounds, while Mobike and Ofo cost 50 pennies for 30 min hire. Second, the dockless bikes are not suitable for long travel. For instance, Mobikes have no gears, which makes going uphill a heavy work

Table 6 Eﬀect of dockless bikes on the weekly usage of the LCH in adjacent boroughs.

Models Treated Controls Eﬀect S.E. T-stat

Unmatched −132.52 −99.71 −32.81 18.86 −1.74 PSM model −132.52 −112.36 −20.16 26.16 −0.77

408 H. Li, et al. Transportation Research Part A 130 (2019) 398–411

Table 7 Eﬀect of dockless bikes on the weekly usage of the LCH (diﬀerent travel duration).

Travel duration Sample Treated Controls Eﬀect S.E. T-stat

0–15 min Unmatched −54.15 −37.83 −16.32 2.99 −5.46* PSM model −54.15 −37.24 −16.91 4.11 −4.11* 15–30 min Unmatched −45.85 −41.80 −4.06 3.94 −1.03 PSM model −45.85 −39.06 −6.80 3.83 −1.77 30–60 min Unmatched −11.13 −13.66 2.53 2.04 1.24 PSM model −11.13 −11.33 0.20 1.22 0.16

Notes: * - Figures are signiﬁcant at 95%.

Table 8 Eﬀect of dockless bikes on the average hire duration (seconds) of the LCH.

Models Treated Controls Eﬀect S.E. T-stat

Unmatched −69.90 −84.96 15.06 4.70 3.21* PSM model −69.90 −84.46 14.56 4.61 3.16*

Notes: * - Figures are signiﬁcant at 95%.

Table 9 Eﬀect of dockless bikes on the weekly usage of the LCH (diﬀerent travel distance).

Distance (O-D) Sample Treated Controls Eﬀect S.E. T-stat

0–1 km Unmatched −14.78 −16.08 1.30 1.87 0.69 PSM model −14.78 −15.91 1.13 1.57 0.72 1–3 km Unmatched −63.70 −50.30 −13.40 4.70 −2.85* PSM model −63.70 −48.59 −15.11 5.20 −2.90* Over 3 km Unmatched −29.19 −24.78 −4.41 2.00 −2.20* PSM model −29.19 −23.27 −5.92 2.47 −2.40*

Notes: * - Figures are signiﬁcant at 95%.

Table 10 Eﬀect of dockless bikes on the average travel distance (meters) of the LCH.

Models Treated Controls Eﬀect S.E. T-stat

Unmatched −73.78 −60.46 −13.32 14.08 −0.95 PSM model −73.78 −50.00 −23.79 12.73 −1.87

Table 11 Eﬀect of dockless bikes on the usage of the LCH during diﬀerent periods.

Period Sample Treated Controls Eﬀect S.E. T-stat

Weekday commuting peak Unmatched −43.52 −31.59 −11.93 2.91 −4.10* PSM model −43.52 −30.84 −12.68 3.33 −3.81* Weekday oﬀ-peak Unmatched −35.00 −33.41 −1.59 3.49 −0.45 PSM model −35.00 −31.40 −3.60 3.15 −1.14 Weekends leisure peak Unmatched −17.25 −16.01 −1.23 1.59 −0.78 PSM model −17.25 −13.50 −3.75 1.68 −2.23* Weekends other times Unmatched −19.56 −19.92 0.36 2.14 0.17 PSM model −19.56 −17.51 −2.05 2.10 −0.98

Notes: * - Figures are signiﬁcant at 95%.

(Guardian, 2018). As a result, the average travel duration of the LCH shows an increase of 0.25 min. Regarding the travel distance, the majority of the LCH trips replaced by the dockless bikes are middle distance trips (1–3 km), which shows a significant reduction of 15.11 (6.74%). In addition, the LCH usage is significantly reduced by 6.85% and 10.47% during the weekday commuting peak and weekend leisure peak separately. We suspect that a part of the LCH casual users switch to the dockless bikes for short duration commuting trips, which led to the reduction in the LCH usage during weekday commuting peak. And on weekends, residents and tourists would prefer to use the dockless bikes for leisure activities, because the dockless bikes have the advantage of flexibility over the Santander bikes. Although the dockless bikes has several advantages over the dockbased one, it has encountered problems. In the second half of

409 H. Li, et al. Transportation Research Part A 130 (2019) 398–411

2018, the dockless bike-sharing schemes suﬀered increased bike losses due to theft and vandalism (Mobike, 2018). And many of them were piled up and blocked the streets. Also, issues such as rebalancing and redistribution are of vital importance and require a lot of manpower and money. As a consequence, oBike, Urbo, and Ofo have pulled out, and Mobike stopped its service in Manchester and reduced its operation areas in London. In 2019, the second wave of dockless bikeshares came. Electric assist bikes (E-bikes) started to appear in London. Follow-up studies are suggested to monitor the inﬂuences of the dockless bikes over time.

Acknowledgement

This work was supported by the National Natural Science Foundation of China (Grant No. 71701042), the Key Project of National Natural Science Foundation of China (Grant No. 51638004), and the National Natural Science Foundation of China (Grant No. 51578149).

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