Optimizing bike sharing systems from the life cycle greenhouse gas emissions perspective

Highlights

• Proposed a modeling framework to optimize bike sharing system’s fleet size and rebalancing.

• Less frequent rebalancing can reduce dock-less BSS life cycle GHG emission rate.

• The current dock-less BSS in Xiamen, China is significantly oversupplied with bikes.

• Rebalancing vehicle fleet can be optimized to reduce rebalancing GHG emissions.

• Having multiple rebalancing depots can help reduce system’s GHG emissions.

Abstract

Bike sharing systems (BSSs), being viewed as providing green transportation modes, are growing rapidly in recent years. While BSS operators try to improve the system performance through bike rebalancing and launching more bikes, the current BSSs are facing several sustainability challenges. Bike oversupply could bring intensive greenhouse gas (GHG) emissions due to manufacturing excessive bikes, while frequent bike rebalancing could significantly increase fuel consumption of rebalancing vehicles. Existing studies only optimized given BSS from the system operational perspective (i.e. rebalancing), with predetermined bike fleet size and rebalancing frequencies. However, the bike fleet size and rebalancing should also be optimized from the life cycle’s perspective. This study proposes a framework to obtain the optimal bike fleet size and rebalancing strategy to minimize the system’s life cycle GHG emissions, integrating a simulation model for fleet size estimation, an optimization model for bike rebalancing, and a life cycle assessment (LCA) model to quantify the system’s GHG emission rate. The framework is applied to a dock-less BSS in Xiamen, China as a case study to evaluate the tradeoff between having more bikes (requiring less rebalancing) and more frequent rebalancing (requiring fewer bikes). Our results show that (1) the current BSS in Xiamen is significantly oversupplied, with only 15% of current bikes needed to serve the same demands; (2) decreasing bike fleet size through more frequent rebalancing will increase the system’s life cycle GHG emissions; and (3) choosing appropriate rebalancing fleet size, loading capacity, and setting multiple depots can reduce a BSS’ rebalancing GHG emissions.

Introduction

With the rapid growth of urban population, cities around the world are facing severe mobility problems (Banister, 2005). The fast-increasing vehicle use generated serious urban transportation issues, such as traffic congestion, air pollution, and greenhouse gas (GHG) emissions (Dell et al., 2014). In 2017, the transportation sector has become the largest GHG contributor in the United States, accounting for 29% of the total GHG emissions annually (U.S. EPA, 2019).

Bike sharing system (BSS), which allows customers to share the use of bikes for a short period of time (normally less than a day), is considered as one type of promising shared economies to reduce GHG emissions, mitigate congestion, and improve urban mobility. In recent years, BSS has experienced a rapid growth. As of 2019, over 2000 bike sharing systems have been in operation around the world (Meddin and DeMaio, 2018). The classical BSS is station-based, where users can pick-up or drop-off bikes at pre-set stations/docks (Kou and Cai, 2018). In recent years, the dock-less system is becoming more and more popular in many cities, eliminating the bike parking constrains from the stations (Caggiani et al., 2018, Chen et al., 2016, Ren et al., 2019, Xu et al., 2018). Convenience is the major motivation for people to use BSSs and system efficiency is critical to the BSS operation (Fishman, 2016, Shaheen et al., 2013).

The bike sharing operators have put in a lot of efforts to improve the system’s convenience. Low reliability (having no available bikes or docks) and low accessibility (the available bikes being too far away from the costumers) could impede bike share use and development. BSS operators use strategies such as bike rebalancing and launching a large quantity of bikes to improve the system’s reliability and accessibility. The imbalanced temporal and spatial distribution of customer demands could leave certain stations/regions with no bike available for pick-up, especially during the peak hours (Fishman, 2016). To better meet customer demands in different areas, regular bike rebalancing is required to redistribute the bikes using automobiles from stations/regions with surplus bikes (Cruz et al., 2017). Additionally, many BSSs also tend to supply a large number of bikes, especially in high demand regions, aiming to ensure the bike availability and attract more users.

Although seen as a sustainable transportation mode, there are indications that the current BSSs could generate negative externalities. First, the bike rebalancing is often done by trucks/vans, which consumes fossil fuels and generates emissions (Pal and Zhang, 2017). For example, due to the intensive rebalancing work, the BSS in London caused additional 766,341 km of vehicle usage every year, outweighing the car trip replacement benefit from launching the BSS (Fishman et al., 2014). Second, due to the lack of guidance and regulations, BSS operators, especially the dock-less ones, often launch excessive bikes to attract users. For example, in Shanghai, more than 1.5 million dock-less shared bikes were in the street and the retired or abandoned bikes clogged the sidewalks and created huge piles of bike wastes (Benjamin, 2017). The embodied energy and emissions in the bike manufacturing phase cannot be neglected, from the life cycle perspective (Luo et al., 2019). Manufacturing the excessive bikes wasted a large amount of energy and resources, and generated enormous GHG emissions (Berkhout and Hertin, 2001, Chester and Horvath, 2009, Coelho and Almeida, 2015). The increased bike consumption also goes against the original concept and the intended benefit of sharing economy, which aims to increase the utilization of existing products.

Current research of bike sharing has mostly focused on improving existing BSSs from the operational perspective, predicting the demands and optimizing the bike rebalancing for a given system (with predetermined bike fleet size and rebalancing frequency). The bike-sharing rebalancing problem (BRP) is well-known as solving the optimal rebalancing routes and loading/unloading quantities to improve the system performance and customer satisfaction (Liu et al., 2018). Szeto & Shui (2018) investigated the solution of the static bike rebalancing problem with multiple rebalancing vehicles, aiming to minimize the demand dissatisfaction and total service time. Although this study reduced the demand dissatisfaction through changing the target inventory level of each station, the total number of bikes was fixed as a priori. Caggiani et al. (2018) proposed a modeling framework of bike rebalancing for a free-floating (dock-less) BSS. The dynamic rebalancing strategy they proposed, which activates the rebalancing work every four hours, shows great potentials to both improve user satisfaction and reduce rebalancing cost. In their numerical study, the bike fleet size and the rebalancing frequency are also pre-determined.

The existing BSS may not have the optimal system design, such as having oversupplied bikes and/or too frequent rebalancing, but few studies evaluate the setup of existing BSSs from the system design’s perspective. Additionally, potential tradeoff exists between the number of bikes required in a system and the rebalancing frequency. Studies on the car sharing system found that having more cars can reduce the empty vehicle miles due to car rebalancing, and understanding this tradeoff could help operators make better decisions on the required fleet size and rebalancing strategy for a specific city (Spieser et al., 2016). Similar to the car sharing system, lower rebalancing frequency requires launching more bikes to meet the same demands (explained with more details in Supplementary Information Section S1), reducing the GHG emissions of rebalancing but increasing the manufacturing emissions. This tradeoff has not been examined by the existing bike share research. While more and more cities are launching BSSs, an optimization study which considers both the bike fleet size and the rebalancing strategy is critically needed to improve the system’s environmental performance.

To address the above mentioned research gap, this study proposed a modeling framework to obtain the minimum bike fleet size and optimal rebalancing routes. The framework integrates a simulation model for fleet size estimation, an optimization model for bike rebalancing, and a life cycle assessment (LCA) model to quantify the system’s GHG emission rate. Specifically, we aim to optimize the dock-less system in terms of the system’s life cycle GHG emissions, considering emissions from upstream (e.g., bike and vehicle) manufacturing, use phase (e.g., rebalancing), and downstream (e.g., end-of-life material processing). We focus on minimizing GHG emissions because BSSs are usually launched as a transportation mode to help reduce GHG emissions. But the framework can be applied to other environmental impact factors. We focus on the dock-less system because it could have higher GHG emission rate than the station-based one, due to its larger bike fleet size and heavier rebalancing burden (Luo et al., 2019). Applying the framework to a dock-less BSS in Xiamen, China as a case study, we used real-world trip data as inputs to optimize the system and minimize the system’s carbon footprint. In the context of the existing literature, the unique contributions of this study are that: 1) we proposed a modeling framework to optimize the bike fleet size and rebalancing to minimize its GHG emissions, considering all life cycle stages including bike manufacturing and rebalancing using automobiles; 2) we evaluated the tradeoff between bike fleet size and rebalancing frequency in terms of the system’s life cycle GHG emissions; and 3) we investigated the impacts of different rebalancing fleet setups (vehicle amount and capacity) and having multiple depots on the system’s environmental performance. This study can provide decision makers tools and guidelines to improve BSS design and operation strategy to maximize the system’s environmental benefits and improve transportation sustainability.