Start Your Engine: Data-driven Planning for Car Sharing Systems for Smart Cities

Emerging (Electric) Vehicle Sharing

Electric vehicles (EVs) have been considered an integral part of smart city development and offer a promising solution to environmental issues in transportation. EVs produce no tailpipe emissions and offer significant improvements on well-to-wheel energy efficiency and emissions levels over their gasoline counterparts, especially when powered by clean sources of electricity (e.g., solar and wind power). The diversity of power sources also make EVs less sensitive to fossil fuel depletion and to supply uncertainty of crude oil. Despite their potential, however, mass consumer adoption of EVs have been hampered by several major hurdles, including their short driving ranges (coupled with inadequate public charging facilities), their high upfront purchase costs, and their potentially high depreciation rates due to rapid technology development.

Meanwhile, another trend in smart city development is the rapid rise of vehicle sharing (including both bikes and cars) businesses, along with the emerging sharing economy in recent years. It is estimated that the sharing economy market is currently worth $26 billion. Passenger cars make a prime candidate for sharing business models because of their low utilization rates (idle 92% of the time, on average), high fixed costs to own ($6,500 per year, on average), and relatively low variable costs to operate. Worldwide, the number of car sharing users is forecast to grow from 2.3 million in 2013 to 12 million in 2020. The largest car sharing firms, Car2go and ZipCar, operate in multiple cities with total fleet sizes of over 10,000 each. The potential for improving efficiency through reduced car ownership is clear—households are found to own 0.23 fewer cars, on average, after adopting car sharing.

Notably, the combination of EVs with car sharing operations is emerging globally as a viable alternative to EV ownership for urban dwellers. Car2go, a subsidiary of Daimler AG, currently operates a car sharing system with full EV fleets in Amsterdam (Netherlands) and Stuttgart (Germany). This innovative operations model can potentially help overcome the major barriers to EV adoption for a number of reasons. First, the range anxiety is alleviated in sharing systems because the EVs operate almost exclusively in well-defined urban service areas. Second, car sharing allows a pool of users to amortize the high fixed costs of purchasing (and maintaining) EVs into usage-based variable costs through collaborative consumption. Third, by retaining ownership, the firm effectively eases consumers’ concerns about technological risks, future resale values, and maintenance. Furthermore, from the EV manufacturer’s perspective, car sharing is an ideal show room for customers to experience the EV driving.

Car2go’s operating model differs from the conventional station-based model (e.g., Zipcar’s), by allowing both round trips and one-way trips in their free-float car sharing systems. Specifically, Car2go allows customers to check out and return cars anywhere within the service region at any on-street parking space. In free-float systems, customers can use smartphone apps to rent, reserve, and drop off vehicles on demand, wherever they choose. This flexible sharing model has been adopted by other car and bike sharing systems, e.g, DriveNow (Europe), Mobike (China), and ofo (China).

Electric Car

Source. Telematic News

Service Region Design with Operational Data

While free-float systems are attractive to customers, their operations can be very challenging to manage, as evidenced by Car2go’s discontinued operations in San Diego, California. A key strategic planning task in free-float vehicle sharing systems is to determine the service region. On the one hand, expanding geographical coverage makes the service more attractive and encourages adoption. On the other hand, doing so entails significant operational challenges, such as the repositioning of cars to ensure availability under imbalanced demand and scheduling for recharging in the case of EVs. All of these factors must be carefully accounted for in determining an effective service region design.

In our paper, we address this planning problem of service region design, which entails several challenges. First, the travel pattern and adoption behavior of potential customers are highly uncertain at the planning stage. Moreover, before entering a new city or considering a major service expansion, the firm may not possess accurate data to describe the demand uncertainty, which exacerbates the planning challenge. Second, the operational details of EV sharing, such as repositioning and recharging of EVs, depend on both the size and shape of the service region. Hence, the firm must also conscientiously account for operational cost drivers when determining the service region when only limited data are available.

Research Observations and Implications

Using a dataset on Car2go’s operations obtained through web crawling, along with data regarding travel characteristics from the California Household Travel Survey and EV charging station deployment data from the U.S. Department of Energy, we perform a case study of Car2go's service region design in San Diego using the optimization framework developed in our paper. Besides identifying the optimal service region design that could potentially improve over Car2go’s actual service area, we have also found several interesting observations.

First, EV sharing systems deliver more environmental benefits, such as savings in CO2 emissions, than replacing individually owned gasoline cars with EVs. Such savings in emissions are mainly due to the higher vehicle utilization in the sharing fleet. Thus, EV sharing systems show the potential to deliver greenhouse gas emissions savings earlier and to deliver greater cumulative environmental benefits through early adoption.

Second, while faster charging technologies help enhance profit and service coverage by improving fleet utilization, the benefits diminish as charging speed improves. Thus, it is sufficient for the car sharing system to deploy moderately fast, but not necessarily the fastest, charging equipment. In practice, high-powered quick charging (e.g., Level 3 quick charging) is expensive to deploy and may shorten battery life if used regularly. To balance this trade-off, our observation suggests that it is sufficient to deploy medium-speed chargers, i.e., Level 2 chargers, as the potential gains from further improving charging speeds are limited.

Finally, we investigate the effects of the service level requirement. Because one primary aim of car sharing services is to replace car ownership, it is of primary concern to ensure high availability of cars such that customers do not encounter significant inconveniences in forgoing (the purchase of) their own cars. On the one hand, a higher service level requirement obviously leads to a larger fleet size and higher costs. On the other hand, it ensures that a higher percentage of demand is satisfied and generates more revenue. Our finding suggests that the optimal service region tends to be larger when customers’ valuation of the availability of cars is lower and when customers’ valuation of service coverage is higher.

 

Reference

He L, Mak HY, Rong Y, Shen ZJM (2017) Service region design for urban electric vehicle sharing systems. Manufacturing Service Oper. Management 19(2):309–327.

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