Understanding spatio-temporal heterogeneity of bike-sharing and scooter-sharing mobility

doi:10.1016/j.compenvurbsys.2020.101483

Computers, Environment and Urban Systems

Volume 81, May 2020, 101483

https://doi.org/10.1016/j.compenvurbsys.2020.101483 Get rights and content

Highlights

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Compared the performance of bike sharing and scooter sharing in Singapore.
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Scooter sharing increased sharing frequency and decreased fleet size.
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Scooter sharing had high maintenance cost for rebalancing and charging.
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Scooter sharing has potential to improve the shareability further.
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Rainfall and high temperatures at noon suppress the usages but not conclusively.

Abstract

The revolution in mobility-sharing services brings disruptive changes to the transportation landscape around the globe. The authorities often rush to regulate the services without a good knowledge of these new options. In Singapore and some other cities, dockless bike-sharing systems rose and fell in just one year and were followed by the booming of docking scooter-sharing systems. This study conducts a comparative analysis of bike-sharing and scooter-sharing activities in Singapore to help understand the phenomenon and inform policy-making. Based on the collected data (i.e., origin-destination pairs enriched with the departure and arrival time and the GPS locations) for one month, this study proposed methods to construct the paths and estimated repositioning trips and the fleet sizes. Hence, the spatio-temporal heterogeneity of the two systems in two discrete urban areas was investigated. It explored the impact of the fleet size, operational regulations (dockless versus docking), and weather conditions on the usages. We found that shared scooters have spatially compact and quantitatively denser distribution compared with shared bikes, and their high demands associate with places such as attractions, metros, and the dormitory. Results suggest that scooter sharing has a better performance than bike sharing in terms of the increased sharing frequency and decreased fleet size; however, the shareability still has potential to be improved. High repositioning rates of shared-scooters indicates high maintenance cost for rebalancing and charging. Rainfall and high temperatures at noon suppress the usages but not conclusively. The study also proposes several initiatives to promote the sustainable development of scooter-sharing services.

Introduction

Transportation has been undergoing a remarkable transformation in the past few years from planned public transit to customized individual mobility, such as ride-hailing (Vazifeh, Santi, Resta, Strogatz, & Ratti, 2018), car-sharing (Jorge & Correia, 2013; Martin, Shaheen, & Lidicker, 2010), ride-sharing (Alonso-Mora, Samaranayake, Wallar, Frazzoli, & Rus, 2017; Santi et al., 2014), and even the upcoming aircraft-sharing (Teo, 2019). However, the car-centric mindset has imprisoned us into unpleasant situations from congestion to parking shortages and pollution (Kan, Wong, & Zhu, 2020; Zhu, Wong, Guilbert, & Chan, 2017). As an alternative and refreshing approach, the first- or last-mile riding on shared-bikes (SBs) or electric shared-scooters (SSs) is increasingly becoming popular for citizens, since they allow fast and cheap short-trip in street blocks without any waiting-or-congestion caused delay (McKenzie, 2019; Shen, Zhang, & Zhao, 2018a; Wen, Chen, Nassir, & Zhao, 2018).

With the development of new techniques such as mobile payment and big-data computing, the sharing economy has penetrated the bike-sharing market with new dockless bike-rental services (Shen et al., 2018a; Shen, Zhang, & Zhao, 2018b). This service allows users to locate and unlock a bike through smartphones and return it anywhere (allowed for parking) when a trip is completed. However, like a short flash of fireworks, the dockless sharing systems have confronted many challenges and some of them have already failed due to the reasons such as unsustainable business model, over-sized fleets, and vandalism (Ma, Lan, Thornton, Mangalagiu, & Zhu, 2018;Shen et al., 2018b; Xu et al., 2019).

In the last two-years, scooter sharing has bloomed and shown its competitiveness in labor-saving and faster travelling compared with bike sharing (Hardt & Bogenberger, 2019; McKenzie, 2019), which becomes prominently superior in tropical cities as high temperatures have negative impacts on bike utilization (Shen et al., 2018b). Having learned a lesson from the challenges confronted by bike-sharing services that severe obstacle of public space may occur in a dockless system, the latest operations on scooter sharing have transformed from dockless systems to the adoption of the deterministic docking stations (Today, 2018). As such, a foreseeable problem may also be tackled. In a dockless system, the battery distance of scooters is limited to a few kilometers and SSs would be abandoned if the electricity is used up on halfway so that extra manpower is needed to recycle these scooters at a considerably high cost. Nevertheless, a vague image about the performance of a scooter-sharing system is still unclear comparing with a bike-sharing system.

Thus, this study aims to investigate the performance of the widely established dockless bike-sharing service versus recently operated scooter-sharing service with docking stations and hence reveal the pros and cons of the two services. To discover the similarities and differences between the two systems operated in the same urban areas and weather factors, our focus is to investigate the performance in terms of usage rates and fleet size management when transforming from SBs to SSs. As Singapore is one of the earliest adventurers on operating micro-mobility (Shen et al., 2018b; Xu et al., 2019), we collected usage information of SBs and SSs and constructed their trips using GPS locations at origins and destinations for one month in two study areas in Singapore. Then, we make a comparative analysis of the two services by focusing on the spatial-temporal distribution represented by seven proposed indices, quantitative changes about trips over weekdays and weekends, and weather influence on demand. Lastly, we summarize the findings and propose four initiatives for the sustainable development of micro-mobility.

The paper is organized as follows. Section 2 presents a review of the revolution of bike-sharing and scooter-sharing systems. Section 3 introduces the pre-processing of the data collected in Singapore and Section 4 introduces the estimation methods. Section 5 conducts a comparative analysis between the two systems in three different aspects. Then, we propose several initiatives to tackle the revealed problems in Section 6, followed by a conclusion in Section 7.

Section snippets

Spatio-temporal analysis

Many studies have been conducted to reveal spatio-temporal patterns of micro-mobility. In New York City, the arrival and departure rates of SBs at one station were associated with bicycle flow rates between the nearby stations (Faghih-Imani & Eluru, 2016). One of the most recent studies compared SBs and SSs operated in Washington, D.C. and found that SBs were primarily used for commuting between homes and offices while SSs were for recreation (McKenzie, 2019). We will also make similar

Study area

In the past few years, Singapore has experienced the waves of rapid expansion and decline of dockless bike-sharing followed by the blooming of dock-based scooter-sharing services. SBs reached the peak of their popularity in 2017–2018 with multiple operators (e.g., Mobike, oBike, ofo, SG Bike, GBikes, and ShareBikeSG) flooding the market with bicycles, but faded quickly as they faced problems mainly with low utilization and frequent complaints about bikes parked in wrong locations. More

Construction of the paths

A probable path from o to d needs to be assigned for each r since continuous locations during each trip are not available. To achieve this, a weighted and undirected graph is refined from OpenStreetMap. The edges of the graph contain all the possible sidewalks and pedestrian paths excluding steps, bike paths, and roads except highways (as almost all roads in Singapore are associated with sidewalks). In addition, we have noticed that users carried bikes or scooters and continued riding when

Spatio-temporal distribution

Seven indices are proposed to describe the performance of the two sharing services: fs is the fleet size of bikes/scooters, d(fs) is the density of the fleet size, n(r) is the number of the real trips over 28 days, f(r) is the sharing frequency per bike/scooter per day, r(rp) is the overall the repositioning ratio, r(rb) is the repositioning ratio for rebalancing, and r(c) is the repositioning ratio for charging. Table 1 presents seven statistics to describe the performance of bike-sharing and

Discussion

Scooter sharing has a better performance than bike sharing in terms of the increased utilization and decreased fleet size. However, a scooter is still only used for 3.15 times per day on average and mostly used for less than 20 min, which suggests that scooters are not used most of the time every day. Also, SSs had high repositioning ratios at 15% in South West and 58% in Marina Bay in Singapore. The repositioning is mainly for two reasons: (i) scooters parking out of the stations are with low

Conclusion

This study conducts a comparative analysis to understand spatio-temporal heterogeneity of bike-sharing and scooter-sharing mobility in two discrete areas in Singapore. SSs have spatially compact and quantitatively denser distribution compared with SBs, and their high demand is associated with places such as attractions, metros, and dormitories. Weather in terms of rainfall and high temperatures at noon could suppress the usage of SBs and SSs, but not dominantly. On the contrary, higher

Acknowledgements

None.

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Understanding spatio-temporal heterogeneity of bike-sharing and scooter-sharing mobility

Highlights

Abstract

Introduction

Section snippets

Spatio-temporal analysis

Study area

Construction of the paths

Spatio-temporal distribution

Discussion

Conclusion

Acknowledgements

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