Electric Revolution and Free Floating Car Sharing: A Data Driven Methodology for System Design

Nowadays, the increase in traffic congestions, land consumption, and pollution emission due to private car ownership makes the rise of shared mobility possible. One of the most spread implementations of shared mobility is Free Floating Car Sharing (FFCS). It is a car rental model where the users can pick and release the car everywhere within an operative area. The customers can reserve (and return) the vehicle using a web-based application. With just a simple tap, the users can unlock and lock the smart vehicle. Usually, the provider bills the users only for the time spend driving, with time-minute based fares. All the other costs, like petrol, insurance, and maintenance, are in charge of the provider. This service’s flexibility fills the urban mobility gap between public transport’s relative cheapness and the comfort and capillarity of private car ownership. Indeed, FFCS allows people to travel and commute faster than the standard public bus but avoiding all the fixed and variable costs related to private car ownership. Given the recent electric cars market increase and all the benefits those vehicles carry, replacing FFCS fleet with electric-powered cars may still improve urban centers’ quality of life. The setup and management of an electric FFCS require ingenuity to minimize the users’ discomfort due to car plugging procedures. In my thesis, I present a methodology to address, in different cases of studies, all the challenges related to the conversion of combustion engine cars to electric vehicles in FFCS. In particular, my research’s main driver is to propose a methodology to build a profitable and technically sustainable system setup, able to guarantee a flexible and appealing mobility service to an increasing customer audience. In the first part of my thesis, I describe the software I developed to scrape from the web real combustion engine FFCS, from two providers: car2go and Enjoy. The car2go data collection lasted from December 2016 to January 2018, collecting more than 27 million users’ bookings spread in 23 cities. The Enjoy data collection phase started in May 2017 and lasted until June 2019, recording about 6 million bookings in 6 cities. Then, I characterize both datasets in Turin, one of the cities in which both FFCS providers work. I detect the outliers, filter them out from the dataset, and extract geotemporal users’ travel patterns. After that, I compare the car2go customer’s pattern with the one-way and two-way car-sharing system. The results show how users prefer more flexible services like FFCS or one-way car sharing. Once the data are consolidated, I develop: A methodology to place a charging station in a city by looking at users’ patterns. System policies to manage the fleet when the vehicle state of charge may not guarantee a trip. Via an event-based trace-driven simulator able to replicate the recorded trips in an electrified scenario evaluating each configuration’s feasibility. Via accurate simulation in Berlin, Milan, Turin, and Vancouver, I study different electric FFCS setup. By placing the charging station in the most frequented areas, by offering an incentive to the users to plug the car when the battery state of charge is below a safety threshold, and balancing the spread of poles, it is possible to obtain a sustainable system covering with charging station only the 8-10 % of zones. To reduce the number of charging stations to have a sustainable electric FFCS, I compare several optimization algorithms. The results show how a Genetic Algorithm can find a better solution to shrink the minimum amount of resources to sustain the same mobility demand. After that, I move my attention to the users’ rentals’ demand predictability. The main goal is to understand how different open-data sources could impact the recorded FFCS users’ rental. Initially, I compare several time-series forecasts to predict the users’ demand in the short and medium-term. Random Forest regression produces better accuracy and results in terms of interpretability. Then I correlate the socio-economics features characterizing each city neighborhood to FFCS demand, and again, the Random Forest regression outperforms other algorithms. Finally, I question the system scalability figuring out several scenarios having increasing demand. I use a model to synthesize users’ demand by looking only at the geospatial users’ rentals. By varying the electric FFCS setup and simulating the new scenario, I point out how a linear increase in the demand intensity requires a fleet sublinear increase. Finally, I project those considerations in euros, proofing how electric FFCS has room for economic growth

Free Floating Electric Car Sharing: A Data Driven Approach for System Design

In this paper, we study the design of a free floating car sharing system based on electric vehicles. We rely on data about millions of rentals of a free floating car sharing operator based on internal combustion engine cars that we recorded in four cities. We characterize the nature of rentals, highlighting the non-stationary, and highly dynamic nature of usage patterns. Building on this data, we develop a discrete-event trace-driven simulator to study the usage of a hypothetical electric car sharing system. We use it to study the charging station placement problem, modeling different return policies, car battery charge and discharge due to trips, and the stochastic behavior of customers for plugging a car to a pole. Our data-driven approach helps car sharing providers to gauge the impact of different design solutions. Our simulations show that it is preferred to place charging stations within popular parking areas where cars are parked for short time (e.g., downtown). By smartly placing charging stations in just 8% of city zones, no trip ends with a discharged battery, i.e., all trips are feasible. Customers shall collaborate by bringing the car to a charging station when the battery level goes below a minimum threshold. This may reroute the customer to a different destination zone than the desired one; however, this happens in less than 10% of all trips

Combining Analytics and Simulation Methods to Assess the Impact of Shared, Autonomous Electric Vehicles on Sustainable Urban Mobility

Urban mobility is currently undergoing three fundamental transformations with the sharing economy, electrification, and autonomous vehicles changing how people and goods move across cities. In this paper, we demonstrate the valuable contribution of decision support systems that combine data-driven analytics and simulation techniques in understanding complex systems such as urban transportation. Using the city of Berlin as a case study, we show that shared, autonomous electric vehicles can substantially reduce resource investments while keeping service levels stable. Our findings inform stakeholders on the trade-off between economic and sustainability-related considerations when fostering the transition to sustainable urban mobilit

Electric Revolution in Free Floating Car Sharing: a data driven methodology for system design

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D3.3 Business models report

RECIPROCITY aims to transform European cities into climate-resilient and connected, multimodal nodes for smart and clean mobility. The project's innovative four-stage replication approach is designed to showcase and disseminate best practices for sustainable urban development and mobility. As part of this project, the present business model report (D3.3) provides an overview of innovative urban mobility business models that could be tailored to cities in the RECIPROCITY replication ecosystem. The work developed was based upon the work carried-out in WP1-2-4, and aimed to collect and derive the business model patterns for urban mobility and propose a business model portfolio that encourage cross-sector collaboration and create an integrated mobility system. This report is therefore addressed to cities and local authorities that have to meet mobility challenges (i.e. high costs and low margin, broad set of partners, competing with private car) by providing new services to activate and accelerate a sustainable modal shift. It also targets other stakeholders interested in business model concepts applied to cities

On Scalability of Electric Car Sharing in Smart Cities

6In this paper we analyze which are the design options that would impact a free floating electric car sharing system performance and costs, studying how the system would scale with an increase in the intensity of the demand. We consider the case study of the city of Turin, for which we leverage hundred of thousands of actual rentals from a (combustion-based) car sharing system to derive an accurate demand model. Armed with this, we consider the transition to electric cars and the need to deploy a charging station infrastructure.Using a realistic simulator, we present the impact of system design options, like the number of charging poles, their allotment, and the number of cars. We first consider performance indicators, like fraction of satisfied demand and working hours system has to spend to bring to charge vehicles. Then we map these figures into revenues and costs, projecting economical indicators. At last, we investigate the scalability of the whole system, i.e., how performance and costs scale when the demand increases. Our results show that concentrating the charging stations in key places is instrumental to optimize car distribution in the city to better intercept the demand. Considering system scalability, the charging infrastructure must intuitively grow proportionally with the mobility demand. Interestingly instead, the fleet size can grow much slower, showing some nice economy of scale gains.partially_openopenBarulli, Michelangelo; Ciociola, Alessandro; Cocca, Michele; Vassio, Luca; Giordano, Danilo; Mellia, MarcoBarulli, Michelangelo; Ciociola, Alessandro; Cocca, Michele; Vassio, Luca; Giordano, Danilo; Mellia, Marc

Siting and sizing of charging infrastructure for shared autonomous electric fleets

Business models rooted in shared economy, electrification, and automation are transforming urban mobility. Accounting for how these transformations interact is crucial if synergies are to be exploited. In this paper, we focus on how a cost-effective charging infrastructure for e-mobility can support the emergence of shared, autonomous mobility. This study addresses the problem of siting and sizing of charging stations for a fleet of shared autonomous electric vehicles (SAEVs). We develop a hybrid simulation-optimization model to find locations and numbers of chargers needed to serve charging demands. Our agent-based model provides an enhanced representation of SAEV operations allowing for smart charging and vehicle cruising when parking/charging is not available. Also, we model charging station placement as full covering optimization and solve the location-allocation problem simultaneously. Finally, we employ real-world trip data from ShareNow in Berlin to evaluate our approach for realistic demand patterns under different charging strategies and fleet sizes. The results show that charging station locations depend mostly on the spatial distribution of installation costs and charging demands. Moreover, charging strategies and fleet size affect the charging patterns and the required number of chargers as well as fleet performance